Dr Sophie Prache

Dominique Gruffat

Denis Durand

Dr Jean-François Hocquette
email: jean-francois.hocquette@inrae.fr

Prof Pierre Ellies-Oury

Dr Sghaier

Moise Kombolo-Ngah

Lucille Rey

Matteo Santinello

Nathalia Mendes

John Albechaalany

Pauline Denis

Valentin Menoury

Myriam Landry

Pascale Beraud

Pascal Faure

Anne Ferlay

Cyril Labonne

Didier Bany

Isabelle Constant

Hieu Luciano Barreto-Mendes

Jingjing Liu

Sghaier Chriki

Marie-Pierre Ellies-Oury

Isabelle Legrand

Grzegorz Pogorzelski

Jerzy Wierzbicki

Linda Farmer

Declan Troy

Rod Polkinghorne

Xin Luo

Alix Neveu

David Pethick

European conformation and fat scores of bovine carcasses are not good indicators of marbling

Keywords: Beef Marbling European conformation score marbling, meat color, fat color, grading site, beef carcass, Meat Standards Australia

Sepchat, who dedicated their expertise on novel farming systems and be kind enough give me the opportunity to work on the SALAMIX data. This contributes to my thesis as an important section with two interesting publications. The same appreciation would be given to the Institut de l'Elevage in Limoges and Villers, thanks again to Isabelle Legarand, Faustine Noel, Laure Thoumy

II

Acknowledgement

I would like to give a million thanks to my major supervisor, Dr. Jean-François Hocquette, for giving me the opportunity to pursue my doctoral degree and start my education path towards becoming a meat scientist. I appreciate many opportunities you have given me to grow throughout my career and the care you have given me in my daily studies and life. I cannot thank you enough for all that you have done for me and taught me; these qualities will continue to play their part in the future. When I was down, you inspired me; when I was in difficulties, you were always there to help. Your expertise, passion, patience, and responsibility to your student is admirable. I would also like to thank my co-supervisors, Prof. Marie-Pierre Ellies-Oury and Mme. Isabelle Legrand for your help and support throughout my degree. You are always committed to busy schedules, which did not diminish your attention to my work. You have always given me feedback in the shortest time, and I was impressed and grateful when I receive your emails in the early hours of the morning or late at night. Your diligent work ethic also inspires me to work harder. We have always had a good time working together and I indeed consider myself particularly lucky that my supervisors are not only dedicated to my work but also nice enough to allow me to work without stress. I love you all ;). I also would like to thank all members of my thesis committee, Prof. Andre Lebert, Dr. Sghaier Chriki, Dr. Liselotte Pannier and Prof. Zhenmin Zhou, your supervison and advice helped me to look back on each academic year and preparing for the next. I consider myself lucky to have worked closely with Dr. Rod Polkinghorne, Prof. David Pethick, Jerzy Wierzbicki, Dr. Linda Farmer, Dr. Declan Troy, Ben Robinson and Dr. Grzegorz Pogorzelski, I look forward to continuing growing our working relationship in future endeavors. I consider myself very fortunate indeed to have studied the Meat Standards Australia system, the most advanced beef grading system in the world. At one time I had only read about it in books, never imagining that one day I would become an MSA certified carcass grader. I would like also to thank Beauvallet CV. Plainemaison, who provided chiller, facilities, animals, carcasses and beef samples, which enable the Australia Beef Chiller Assessment System to be conducted and related articles to be published. The research work during my thesis was sponsored by Chinese Scholarship Council (CSC), for this I have many thanks to CSC for their financial support. I also received financial support from the INTAQT project during the final period of my PhD and I am very grateful to Dr. Bruno Martin and other colleagues for their understanding and support, which allowed me to continue finishing my thesis with peace of mind. Thanks to the financial contributions of the European research project ProSafeBeef, the Polish ProOptiBeef Farm to Fork project funded by the EU Innovative, the French 'Direction Générale de l'Alimentation' and FranceAgriMer, the Irish Department of Agriculture Food and The Marine under the FIRM program, and the Northern Ireland Department of Agriculture and Rural Development 'Vision' program, I would be able to get access to the existing datasets, which are important to form part of my thesis.

V et lumborum at 10 th -13 th thoracic vertebrae in Australia but the 5 th rib is preferred for cutting carcasses by the European meat plants, due to the consideration of the economic value of the carcass. Therefore, we assessed marbling at both the 5 th rib and the 10 th rib according to the European and Australia ways of carcass grading on carcasses from 208 French cattle mainly Limousine cows. We found that the grading of marbling can be conducted at the 5 th rib for a routine use of the MSA system in France and more generally in Europe, since there was no significant difference in marbling score, between the 5 th rib and the 10 th rib. Most importantly, as marbling score used as an important predictor of eating quality in the MSA grading scheme, the MSA prediction of beef eating quality was not affected by marbling score assessed either at the 5 th rib or at the 10 th rib. After exploring carcass traits and assessment, the application of MSA consumer testing standards with European consumers was investigated. In the MSA system, tenderness, juiciness, flavor liking and overall liking are used to determine the eating quality of beef that can be evaluated by consumers. Since it would be useful to understand consumer perception of meat eating quality, the contributions of tenderness, juiciness, flavor liking toward overall liking was studied in this thesis. Through 8,216 muscle cuts with consumer testing scores (collected based on MSA protocol) from France, Poland, Ireland and Northern Ireland collected from 743 cattle and 18 different muscles, we found that for European consumers, flavor liking was the most important contributor to beef overall liking, followed by tenderness and juiciness. The improvement in tenderness over the last decades may explain the highest contribution of flavor liking nowadays. With the increasing consumer demands regarding nutritional and sensorial quality, and agricultural sustainability, it is necessary to promote innovative farming systems based, for instance on animal crossbreeding between commercial and local breeds, natural feeding with a more flexible management and a more productive, diverse and sustainable supply chain. In this thesis, we used crossbred Angus and Salers animals grass-fed and reared in two farming systems for a better meat production with high nutritional and sensorial quality, as well as for a higher efficiency of feeding, better parasite management and an enhanced sustainability of local meat production. First of all, the carcass and meat attributes of Angus x Salers animals reared in two farming systems (a mono cattle system versus a mixed system with beef cattle and sheep) were characterized. The results indicated that the farming system had no significant impact on bovine carcass characteristics and meat quality. Based on beef quality assessment by untrained consumers and trained panels for sensorial traits, based on objective measurements of the Warner-Bratzler shear force, and of fatty acid and antioxidant contents, we found that these grass-fed Angus x Salers animals can produce lean meat rich in ω-3 PUFAs with a low ω-6/ω-3 ratio and "better-thanaverage" beef eating quality. Regarding eating quality assessment, we found that the MSA consumer testing protocol, MQ4 calculation and its association with the MSA quality grades are relevant to develop a consumer-based grading system for beef eating quality prediction in France. Furthermore, French consumers were able to discern differences in eating quality traits from different muscle cuts and ageing times VI for these crossbred animals. However, due to the fact that these animals were indeed lean, which might be considered as non-compliant in the MSA system, carcass grading is less accurate to predict eating quality scores according to the current MSA prediction model. In addition to the exploration with eating quality of conventional meat, an international survey study was widely distributed and carried out in this thesis to investigate especially Chinese and French attitudes toward cultured "meat". The results indicated that consumers from different countries had different attitudes toward this novel product and subsequently their willingness to try, to eat and to pay for cultured "meat" is different. On average, Chinese respondents had higher willingness to try, to eat and to accept cultured "meat" in the future than French respondents, although all of them had not shown a strong willingness to accept cultured "meat". Unnaturalness was the most barrier for both Chinese and French respondents to try cultured "meat". The main driver for Chinese respondents to accept cultured "meat" is the food safety issue, whereas Chinese consumers had less concerns about animal welfare and environmental sustainability, which are the main drivers for French respondents. From different perspectives, most of the Chinese respondents (85%) thought cultured "meat" as intriguing and/or promising, by contrast, 60% of French respondents found cultured "meat" as absurd and/or disgusting. Overall, the potential acceptance of cultured "meat" in different countries depends on different catering culture, perception of food and traditional philosophy. All these findings indicate the need for the European beef industry to make progress in the description of meat quality for a better determination of the commercial value of carcasses with consistent quality guaranteed by appropriate quality description, which may imply to develop a new consumer-based quality grading scheme. Given the relevance and potential applicability of the MSA grading scheme, further investigation and adjustment would be needed to include critical points in current grading systems such as carcass characteristics (i.e marbling and rib fat thickness) while respecting European carcass quartering practices. In addition, further research focused on different animal types (i.e. lean animal), either crossbred or with genetic specifies as well as with novel livestock production systems is still required for a more accurate and robust modelling system to predict beef eating quality. Furthermore, with the rise of various novel meat alternatives, the improvement of beef quality and the need to provide a consistent guarantee (i.e. quality description) to consumers is of importance for the competitiveness and success of the conventional beef market and industry.

VII

Résumé en Français

Répondre aux demandes et aux attentes des consommateurs est de la plus haute importance pour le développement et le succès du marché et de l'industrie de la viande bovine, pour la production conventionnelle comme pour celle de substituts de viande. La qualité gustative de la viande bovine, critère de choix parmi les plus importants pour les consommateurs, est très variable et doit être contrôlée et garantie par la filière. C'est pourquoi un système capable de prédire avec précision la qualité gustative de la viande bovine et pouvant être largement mis en oeuvre dans la chaîne d'approvisionnement est une option pertinente pour la filière viande bovine européenne. Le secteur australien de la viande bovine a innové en mettant en place un système de garantie de la qualité gustative. Basé sur un ensemble de points de contrôle critiques (CCP) de la production à la consommation, ce système considère les caractéristiques des animaux avant abattage, les conditions de l'abattage et du traitement postabattage (maturation, cuisson) ainsi que les aspects de valorisation qui affectent la qualité de la viande bovine. L'un des principaux objectifs de cette thèse est d'étudier la pertinence d'un système de classement de type Meat Standards Australia (MSA) pour la filière viande bovine européenne et les consommateurs européens, en particulier en France. Au-delà de l'industrie bovine conventionnelle, différentes alternatives à la viande sont développées pour répondre aux exigences supplémentaires des consommateurs telles que le bien-être animal, la protection de notre environnement ou encore la durabilité de la production. La "viande" cultivée est une technique emblématique qui promet de répondre aux besoins d'un plus grand nombre de consommateurs, en traitant de ces questions outre la satisfaction de nos besoins alimentaires. En conséquence, l'autre objectif de cette thèse est de fournir un aperçu de la perception et de l'acceptation de la " viande " de culture par les consommateurs de différents pays. Il s'agit d'étudier dans quelle mesure les différences de perception peuvent entrainer une acceptation différente de la "viande" de culture entre pays. Les données utilisées pour cette thèse ont différentes sources 1) une base de données existante, gérée sous les auspices de la Fondation internationale 3G pour la recherche sur la viande (Fondation Meat3G), et contenant des données sur le classement de bovins irlandais, polonais, britanniques et français et les résultats des tests consommateurs afférents; 2) une expérimentation conduite à INRAE basée sur un système d'élevage innovant avec des animaux croisés Angus x Salers conduits en système herbager à bas intrants; 3) une enquête internationale sur la "viande" de culture, soumise principalement à des consommateurs chinois et français (mais également à des consommateurs de différents autres régions du monde).

VIII

Afin d'améliorer le système de classement de la viande bovine en Europe, notamment en établissant un lien entre le système de classement européen et australien, nous avons étudié conjointement la grille EUROP et le système MSA. La grille EUROP est obligatoire en Europe pour le classement et le commerce des carcasses de bovins : elle est basée sur l'évaluation de la conformation (développement musculaire de la carcasse) et de l'engraissement des carcasses. S'il est bien établi que la grille EUROP a peu de rapport avec la qualité gustative de la viande ou avec la satisfaction des consommateurs, en revanche, le persillé largement utilisé en dehors de l'Europe est un indicateur important de cette qualité. Dans cette thèse, nous avons étudié la relation entre la note de persillé et les scores européens de conformation et d'engraissement des carcasses afin d'explorer la possibilité de prédire le persillé avec les scores de la grille EUROP. En classant plus de 700 carcasses ayant un large éventail d'origines, de races, d'âges et de types d'animaux, de conditions d'alimentation et d'élevage, il a été constaté que la relation entre la note de persillé et les scores européens de conformation et d'engraissement des carcasses étaient faibles et variables selon les types d'animaux. Alors que la grille de conformation EUROP est bien adaptée pour estimer le rendement des carcasses, elle ne reflète en revanche pas le persillé. Il serait donc pertinent d'introduire dans le système de classement des carcasses actuellement en vigueur en Europe des caractéristiques de la carcasse qui soient réellement liées à la qualité en bouche de la viande. Sur la base de ce constat, le classement des 208 carcasses selon les méthodes du « Australian Beef Chiller Assessment System » (ABCAS) a été réalisé dans un abattoir d'une entreprise privée à Limoges. La mise en oeuvre de cette méthode a toutefois nécessité quelques ajustements, le contexte européen ne permettant pas de suivre à 100 % la méthode australienne de classement. Par exemple, si le site de notation du persillé se trouve généralement sur les M. longissimus thoracis et lumborum au niveau des 10 ème -13 ème vertèbres thoraciques en Australie, les abattoirs européens préfèrent quant-à-eux séparer les quartiers au niveau de la 5 ème côte. Afin de s'affranchir de ce biais, nous avons évalué le persillé à la fois au niveau des 5 ème et 10 ème côtes selon les méthodes européenne et australienne sur les carcasses de 208 bovins français, principalement des vaches Limousines. A l'issue de cette comparaison, il ressort qu'il est possible de maintenir en Europe une évaluation du persillé selon la méthodologie australienne au niveau de la 5 ème côte, car il n'y a pas de différence significative de note de persillé, entre la 5 ème côte et la 10 ème côte. Plus important encore, nous avons montré que la prédiction MSA de la qualité de la viande bovine n'a pas été affectée par le changement de site d'évaluation du persillé, l'un des prédicteurs importants de la qualité de la viande dans le système de classement MSA. La qualité de la viande était estimée par la classe MSA (déclassé, 3*, 4* ou 5*) et la note MQ4 (note de qualité de 0 à 100). Le MQ4 est calculé par la combinaison pondérée de la tendresse, de la jutosité, de l'appréciation de la flaveur et de l'appréciation générale en relation avec la classe MSA de la viande déterminée par les consommateurs. Après le classement des carcasses, les études suivantes ont porté sur le déploiement potentiel du système MSA à des consommateurs européens. Le système de classement MSA présente deux spécificités : 1) la qualité gustative des différents IX morceaux est prédite de manière indépendante sur la base de critères de la production à la consommation en passant par le classement des carcasses à l'abattoir ; 2) la qualité gustative finale est confirmée par une évaluation sensorielle par des consommateurs non entrainés. Après avoir exploré les caractéristiques et l'évaluation des carcasses, nous avons étudié les préférences des consommateurs européens avec la méthodologie MSA. Dans le système MSA, la tendreté, la jutosité, l'appréciation de la flaveur et l'appréciation globale sont utilisées pour déterminer la qualité gustative de la viande bovine telle que perçue par les consommateurs. Afin de mieux comprendre cette perception, les contributions de la tendreté, de la jutosité, de la flaveur et de l'appréciation globale à la qualité sensorielle ont été étudiées. Avec des scores issus de tests consommateurs (collectés sur la base du protocole MSA) pour 8 216 morceaux provenant de 743 bovins de France, de Pologne, d'Irlande et d'Irlande du Nord et de 18 muscles différents (pour refléter, un large éventail de qualités gustatives), nous avons constaté que pour les consommateurs européens dégustant des échantillons de viande bovine européenne, l'appréciation de la flaveur était le facteur le plus important de la qualité globale de la viande bovine, suivie des autres attributs, tendreté puis jutosité. L'appréciation de la flaveur est donc le principal facteur de variabilité de la qualité globale de la viande pour les consommateurs européens étudiés. Nous avons émis l'hypothèse que l'amélioration de la tendreté au cours des dernières décennies pourrait expliquer la plus grande contribution de l'appréciation de la flaveur aujourd'hui (c'était la tendreté qui était préalablement le critère le plus important). La jutosité est la caractéristique la moins robuste. Elle pourrait être influencée par d'autres critères au cours de la dégustation par le consommateur, mais elle reste importante pour la perception globale de la qualité gustative du boeuf. Dans le système de classement de la MSA, le modèle de prédiction de la qualité gustative est un point clé qui repose sur l'évaluation des classes de qualité MSA par des consommateurs non formés. La prédiction de la qualité est déterminée par les points de contrôle critiques ou CCP. Ces facteurs sont utilisés pour prédire un score unique de qualité de la viande (MQ4). Si l'on souhaite améliorer la satisfaction des consommateurs grâce au système de classement, il serait primordial que la qualité en bouche, évaluée par les consommateurs européens, corresponde aux catégories de qualité prédites par le système MSA. Un grand nombre de données ont été collectées en utilisant les protocoles standards de la méthode MSA pour conduire des tests à grande échelle auprès des consommateurs et classer les carcasses de différentes races bovines et systèmes de production, pour différentes coupes musculaires et origines de groupes de consommateurs. La pertinence du système de classement MSA a été confirmée dans ces différents contextes. Néanmoins, il existe encore des restrictions pour certains types d'animaux dont la qualité n'est pas prédite par le système MSA. Par exemple, les carcasses très maigres (c'est-à-dire dont l'épaisseur du gras sur les côtes est inférieure à 3 mm) sont exclues du système de classement. Compte tenu des exigences croissantes des consommateurs en matière de qualité nutritionnelle et sensorielle, de production durable sur le plan environnemental et écologique, il est nécessaire de promouvoir des systèmes d'élevage innovants basés, X par exemple, sur le croisement d'animaux, les races locales et une alimentation naturelle à base d'herbe, avec une gestion plus souple et une production diversifiée, durable et suffisante pour assurer un minimum de revenu aux éleveurs, . Une production de viande plus diversifiée serait donc souhaitable, y compris par exemple en produisant des carcasses ou des viandes maigres à côté de viande plus persillée. Dans cette thèse, nous avons étudié des animaux croisés Angus x Salers en combinaison avec une stratégie d'alimentation à l'herbe mise en oeuvre en région Auvergne-Rhône-Alpes en France. Il s'agissait de produire une viande bovine de haute qualité sensorielle, bien adaptée à l'environnement local de pâturages montagneux, avec l'objectif d'une durabilité accrue de la production locale de viande. Ont été étudiés tout d'abord, les attributs de la carcasse et de la viande d'animaux Angus x Salers élevés dans deux systèmes d'élevage (un système bovin versus un système mixte avec des bovins et des ovins). Les résultats ont indiqué que le système d'élevage n'avait pas d'impact significatif sur les caractéristiques de la carcasse et la qualité de la viande. Dans cette expérimentation, l'évaluation de la qualité de la viande bovine a été réalisée 1/ via des évaluations sensorielles (par des consommateurs non entrainés ainsi que par des panels experts), 2/ via des mesures objectives de la force de cisaillement Warner-Bratzler, mais également 3/ via le dosage des teneurs en acides gras et en antioxydants pour apprécier également la qualité nutritionnelle. Cette caractérisation des viandes nous a permis de constater que ces animaux Angus x Salers nourris à l'herbe pouvaient produire une viande maigre, riche en AGPI ω-3 avec un faible rapport ω-6/ω-3 et une qualité en bouche de la viande bovine " supérieure à la moyenne ". En ce qui concerne l'évaluation de la qualité sensorielle, les méthodologies MSA ont été utilisées 1) pour déterminer la qualité des animaux croisés Angus x Salers nourris à l'herbe ; 2) pour étudier si le système de classement MSA est pertinent pour ces animaux dans le contexte français. Nous avons réalisé des tests sensoriels avec des consommateurs français non entraînés qui ont évalué la qualité en bouche de 155 coupes musculaires de M. longissimus dorsi et thoracis, M. triceps brachii caput longum et M. obliquus internus abdominis provenant de 31 animaux Angus x Salers nourris à l'herbe, en combinaison avec deux temps de maturation (5 jours, 14 jours) et deux méthodes de cuisson (grillade selon les habitudes australiennes ou françaises) pour certaines coupes. Lors du protocole de test MSA par des consommateurs, le calcul du MQ4 (score global de qualité) et son association avec les classes MSA de qualité sont apparus suffisamment pertinents pour développer un système de classement et de prédiction de la qualité en bouche de la viande bovine en France, basé sur les consommateurs. En outre, comme ceux d'autres pays, les consommateurs français ont été en mesure de discerner des différences dans les caractéristiques de qualité sensorielle de différentes coupes musculaires et durées de maturation pour ces animaux croisés. Cependant, étant donné que ces animaux étaient effectivement maigres (ce qui pourrait être considéré comme non conforme dans le système MSA), le classement des carcasses est apparu moins précis pour prédire les notes de qualité sensorielle selon le modèle de prédiction MSA dans sa version actuelle.

XI

Outre l'exploration de la qualité sensorielle de la viande conventionnelle, dans le cadre de cette thèse, une enquête internationale a été largement réalisée et diffusée dans différents pays pour étudier en particulier les attitudes des consommateurs chinois et français à l'égard de la "viande" de culture. Les réponses de 4 666 consommateurs chinois et 5 418 consommateurs français ont indiqué que les consommateurs de différents pays avaient des attitudes différentes envers ce nouveau produit et que leur volonté d'essayer, de consommer régulièrement et de payer pour de la "viande" de culture était différente. En moyenne, les consommateurs chinois étaient plus disposés à essayer, à consommer et à accepter la "viande" cultivée à l'avenir que les consommateurs français, bien que les consommateurs des deux pays n'aient pas montré de forte volonté de consommer la "viande" cultivée. Le manque de naturalité était le principal obstacle pour goûter la "viande" de culture pour les deux types de consommateurs. La principale motivation des consommateurs chinois à accepter la "viande" de culture était la question de la sécurité alimentaire, avec moins de préoccupations sur le bien-être des animaux et la durabilité environnementale. En revanche, les critiques éthiques et environnementales de l'élevage figuraient parmi les principales motivations des consommateurs français. En outre, la plupart des consommateurs Chinois interrogés (85 %) ont considéré la "viande" de culture comme « intrigante et/ou prometteuse », alors que 60 % des Français interrogés l'ont trouvé « absurde et/ou dégoûtante ». Dans l'ensemble, l'acceptation potentielle de la "viande" de culture dans les différents pays dépend de la culture gastronomique du pays, de la perception de l'alimentation et des traditions culinaires. Tous ces résultats indiquent que la filière viande bovine européenne doit progresser dans l'évaluation des caractéristiques de qualité de la viande pour mieux déterminer la valeur commerciale des carcasses, ce qui peut impliquer de développer un nouveau système de classement de la qualité basé sur l'appréciation directe par les consommateurs. Compte tenu de la pertinence et de l'applicabilité potentielle du système de classement MSA, des recherches plus approfondies et des ajustements seraient nécessaires pour inclure des points critiques dans les systèmes de classement actuels en Europe tels que les caractéristiques de la carcasse (c'est-à-dire l'épaisseur du gras au niveau des côtes comme en Australie) tout en respectant les pratiques européennes de découpe des carcasses. En outre, des recherches supplémentaires axées sur différents types d'animaux (c'est-à-dire les animaux maigres), croisés ou avec des spécificités génétiques, ainsi que sur de nouveaux systèmes de production animale, sont encore nécessaires pour mettre au point un système de modélisation plus précis et plus robuste permettant de prédire la qualité en bouche de la viande bovine en Europe. Avec l'essor de diverses nouvelles alternatives à la viande, l'amélioration de la qualité de la viande bovine et la nécessité de fournir une garantie cohérente aux consommateurs présentent une importance croissante pour la compétitivité et le succès du marché et de la filière viande bovine conventionnelle. Reasons not to try "cultured meat" also called "artificial meat" (A), expectations (B) and reasons to try "cultured meat" (C). Results are expressed as a percentage of answers (N = 5418 responses). As these are multiple choices questions, the sum of the percentages is greater than 100……………………………………………………………………………………177 color scores determined at the fifth and the tenth ribs……………………………………………75 Table 3. Correlation coefficients (r) among the assessment scores at the fifth rib and the tenth rib………………………………………………………………………………………………………75 Table 4. Pearson correlations and average differences between the MQ4 scores predicted by the marbling score assessed at the fifth rib and the tenth rib……………………………………76 Table 5. Linear regression equations for indicating the MQ4 score using the marbling score assessment at the fifth rib and the tenth rib…………………………………………………………77 Publication 3 Table 1. Multiple regression models of sensory traits to overall liking……………………………85 Table 2. Relative importance and 95% CI attributes of sensory traits for overall liking…………86 Table 3. Multicollinearity test for multiple regression model of flavor liking, tenderness and juiciness to overall liking……………………………………………………………………………87 Table 4. Multiple regression models of sensory traits to overall liking based on four levels of tenderness evaluation………………………………………………………………………………87 Table 5. Contributions of sensory traits to overall liking based on four levels of tenderness evaluation……………………………………………………………………………………………88 Table 6. Multiple regression models for contributions of sensory traits and interactions to overall liking with (R 2 = 0.94) or without (R 2 = 0.94) juiciness……………………………………………88 Table 7. Consumer palatability scores of tenderness, juiciness, flavor liking and overall liking depending on three cooking methods………………………………………………………………89 Table 8. Contributions of tenderness, juiciness and flavor liking to overall liking depending on three cooking method………………………………………………………………………………89 Table 9. Consumer palatability scores of tenderness, juiciness, flavor liking and overall liking from eighteen muscles………………………………………………………………………………89 2. Mean (clipped data), standard error (SE), minimum and maximum values for consumer scores of eating quality traits and MQ4 score for each type of beef sample……………………120 Table 3. Accuracy of MQ4-assigned quality grade in comparison with actual consumer selected grade…………………………………………………………………………………………………123 Table 4. Simple correlation coefficients and mean differences between observed and predicted MQ4 scores…………………………………………………………………………………………125 Table 5. Multiple regression models for indicating MQ4 score using main carcass grading factors………………………………………………………………………………………………126 Table 6. Variance analysis of mean values of sensory scores of BLD096 between 5 and 14 days of ageing………………………………………………………………………………………129 Table 7. Variance analysis of mean values of sensory scores of TFL052 between two muscle slicing methods………………………………………………………………………………………130

XIX

List of figures

XXI

List of tables

Publication 6

Table 1. Demographic information of the respondents of our survey (4666 responses)……142 Table 2. Responses about societal challenges related to the meat industry and the perceptions about artificial meat (4666 responses)……………………………………………………………144 Table 3. Responses about willingness to engage with artificial meat and the future development……………………………………………………………………………….………..146 Table 4. Analysis of variance of willingness to try according to demographics………………148 Table 5. Pairwise comparisons between significant demographic groups for willingness to try……………………………………………………………………………………………………149 Table 6. Analysis of variance of willingness to eat regularly/willingness to pay according to demographics………………………………………………………………………………………150 Publication 7

Table 1. Demographic information of the French survey respondents (N = 5418 responses)…………………………………………………………………………………………170 Table 2. Pairwise comparisons for willingness to try between demographic groups for occupation interacting with age or level of meat consumption (N = 5418 responses)………171 XXIII Table 3. Pairwise comparisons for willingness to regularly eat "artificial meat" between demographic groups for different interactions between selected factors (level of meat consumption, gender, age groups, occupation and familiarity with "artificial meat", N = 5418 responses)…………………………………………………………………………………………172 Table 4. Pairwise comparisons for willingness to pay between demographic groups for level of meat consumption interacting with age (N = 5418 responses)…………………………………172 Table 5. Effects of "gender × age" interaction on drivers, motives and barriers of acceptance of "artificial meat" ………………………………………………………………………………………173 Table 6. Effects of "gender × meat consumption level" interaction on drivers, motives and barriers of acceptance of "artificial meat" …………………………………………………………173 Table 7. Effects of "occupation × level of meat consumption" interaction on drivers, motives and barriers of acceptance of "artificial meat"……………………………………………………173 Table 8. Effects of "level of meat consumption × age" interaction on drivers, motives and barriers of acceptance of "artificial meat"…………………………………………………………174 Table 9. Effects of "occupation × age" interaction on drivers, motives and barriers of acceptance of "artificial meat"……………………………… ……………………………………………………174 Table 10. Differences in answers to the different questions in the survey by respondents from the two major clusters………………………………………………………………………………178

Chapter 1. Introduction

Beef quality is a complex concept based on different dimensions involving morphological quality, eating quality and credence quality (Grunert et al., 2004). Among them, intrinsic beef quality such as eating quality is of paramount importance for consumer perception of beef quality. Beef eating quality has a multifactorial determinism with various factors that have direct or indirect impacts on the ultimate eating quality of the beef product. Those determining factors include on one hand animal-related factors such as animal breed or genotype, sex, age or physiological maturity, growth potential, rearing conditions (i.e. in feedlots or on pastures), hormonal implants, beef cut in relation to muscle type; pre-slaughter management such as transport and handling. The determining factors also include postmortem factors such as electrical stimulation, carcass hang and chilling conditions with rigorous control of pH and temperature decline (Hocquette et al., 2012;Pethick et al., 2021). Due to the complexity of determinism of beef eating quality, numbers of approaches (including mechanical measurements, non-destructive instrumental methods and omics approaches) have been developed to assess and predict eating quality of beef, which actually would be ultimately scored by normal consumers. In practice, sensory evaluations by consumers and also by panelists have been widely used in beef eating quality evaluation studies. As it has long been discussed in the beef quality research field, the establishment of a robust predict model based on all above quality determining factors would be an efficient solution to guarantee beef eating quality for consumers (Hocquette et al., 2020). Unfortunately, so far no perfect system has been developed and applied throughout the world, despite the use of several grading systems, which are well-known but mainly implemented locally instead at worldwide level. Meat scientists are aware of this so that numbers of studies were conducted in the last two decades to explore the application of those grading systems in different regions of the world with different animal types, muscle cuts, cooking methods and various consumer segments. One of the most successful grading systems highly studied worldwide is the Meat Standards Australia (MSA) grading scheme now widespread with the acronym 3G (for Global Guaranteed Grading) when developed outside Australia (Hocquette et al., 2020).

In Europe, the widely applied carcass grading scheme is the EUROP grid. As being a yield description system rather than a consumer-based and/or muscle cut-based eating quality grading system, the EUROP grid has difficulty to estimate and predict eating quality for consumers at the industrial level (Bonny et al., 2016a). However, effective improvements in beef eating quality should be based on consumer evaluation of individual muscle cuts. This is the major reason why numerous consumer beef eating quality tests based on the MSA/3G protocols have been conducted in Europe, and the relevance of the MSA standards in the European consumer market has been already confirmed (Bonny et al., 2018;Hocquette et al., 2014;Legrand et al., 2013). What needs to be done further is the implementation of the MSA/3G system considering the practical conditions of the European beef industry (i.e. animal types, on-farm beef production systems, in-plant process practices, etc.). Indeed, several practical issues arise with increasing international cooperation and large-scale implementations in local beef plants, such as carcass grading practices. Ongoing validation and model development of the MSA system are indeed making progress such as the inclusion of different weightings of eating quality attributes to the overall satisfaction of beef quality depending on consumer segments or on specific crossbred animals. The conclusions derived from international data sharing, pooling and metaanalyses with European consumers and beef production systems are shedding lights on these updates.

In general, extrinsic quality cues which include not only product brand, origin, price and packaging but also some factors that adding values to the products such as animal welfare, environmental and agricultural sustainability are also of importance for consumers. This is the reason why consumers prefer to buy meat produced from grass-fed animals or from extensive livestock farming systems (Stampa et al., 2020). Therefore, this is a growing interest of scientists in animal science to promote systems based on agroecological farming and sustainable intensification, which partially relies on better natural resources usage efficiency, flexible management of animal crossbreeding and production for the increasing demand of meat production of high nutritional and eating quality. Similarly, it increases the need for conventional livestock farming and meat supply chain to be in the same time more productive, diverse and sustainable (Aydin, 2021). In addition, especially in France, there is a great interest for pure breeds. However, for improved animal production, new quality-based and consumer-accepted farming practices are necessary such as those based on specific crossbreeding, and on the use of local terroir and feed resources. Indeed, appropriate crossbreeding can be an efficient way to protect local breeds and to retain local agricultural sustainability. Consequently, more research is needed to characterize these systems and to ascertain if meat produced in this way is of acceptable quality. This can be on one hand examined by using existing beef quality grading schemes and on the other hand by expanding those grading systems to new animal genotypes and innovative on-farm production practices.

The increasing interest of consumers for sustainability also explains the success of meat alternatives (i.e. plant-and mycoprotein-based products) and the another huge heat of the novel product -cultured "meat" has been caused in the public media [START_REF] De Koning | Drivers and inhibitors in the acceptance of meat alternatives: The case of plant and insect-based proteins[END_REF][START_REF] Lee | Status of meat alternatives and their potential role in the future meat market-A review[END_REF]. Cultured "meat" is produced by animal cell culture and muscle tissue genesis rather than the conventional way of animal rearing and slaughtering. Cultured "meat" is gaining attention for its potential capacity to address concerns regarding animal welfare, environment protection, resource consumption, food safety and public health that in association with conventional meat production (Bryant & Barnett, 2020). Due to the huge enthusiasms of media, business and market on cultured "meat", advanced scientific and technological breakthroughs involving animal cell composition, culture media optimization, bioreactor designation, scaffold fabrication and cost reduction have been made for large-scale manufacturing of cultured "meat" [START_REF] Guan | Trends and ideas in technology, regulation and public acceptance of cultured meat[END_REF]. In particular with the achievement of animal serum free medium, cultured "meat" would be accepted by more consumer segments. Nevertheless, consumer acceptability is still the most critical factor determining the survivability of the cultured "meat" industry (Jairath et al., 2021). Hence, there is always a need to better collect opinions of consumers on this subject and to further capture consumers' perception, attitude, willingness to engage (try, eat and pay) with this novel food product. This issue is indeed closely associated with the development of conventional meat industry and meat science related research. This thesis is mainly composed of three thematic chapters based on the following hypotheses. The first part of this thesis related to the MSA/3G grading system aims to answer the following questions: with the use of the MSA standards in the European context in terms of carcass grading and consumer sensory perception on beef eating quality, is it possible to estimate MSA predictive inputs such as marbling score from conformation and fat scores of the EUROP grid? In practical, is it viable to grade cattle carcasses in European plants following guidelines of the Australian Beef Chiller Assessment System? How European consumers weight tenderness, juiciness and flavor liking to assess overall liking? The first part is thus focused on the development of carcass grading and consumer sensory perception on beef eating quality by following the MSA protocols. The outputs of these studies may be helpful for the further application of the MSA standards in Europe. The second part of this thesis explores the application of the MSA standards for crossbred animals that were reared in local mountainous pasture environment in the Auvergne-Rhône-Alpes region of France and for French consumers who evaluated beef eating quality of those animals. Two livestock farming systems (a mono cattle system vs. a mixed system with beef cattle and sheep) both based on grass feeding and a specific crossbreeding (Angus x Salers), beef quality especially eating quality are investigated according to the MSA standards with untrained consumers assessing and other measurements including panel assessing and mechanical measurement. The hypotheses we proposed are 1) can French consumers distinguish quality differences in different sensorial attributes with these young, lean and grass-fed crossbred animals? 2) is the MSA prediction of eating quality scores applicable for these specific crossbred animals and French untrained consumers? The findings obtained from the second part of the thesis may expand the potential use of the MSA/3G grading scheme with specific and local on-farm production systems, ultimately for a more accurate and robust modelling system to predict beef eating quality. In parallel of the development and application of new eating quality-based beef grading schemes, it is also important to improve meat quantity/yield for the increasing human population while considering the environmental, ethical and economic sustainability. Meat substitutes have been claimed as a relevant solution to address the issues of resource shortage, animal suffering and environmental pollution. One of the most popular meat alternatives, so-called cultured "meat", aims to be commercially available in the market. Especially with the stealth advocacy of the proponents of cultured "meat", consumers around the world seem to be supportive toward this novel technology. While it will be surely subject to regional legislation and market regulation, consumer acceptance is still the most important issue. Would consumers be willing or eager to try, eat and pay for cultured "meat"? Thus, the third part of this thesis studies the perception, attitudes, acceptance of cultured "meat" with consumers originated from China and France. These results would add new knowledge to the rapidly expanding field of cultured "meat" regarding willingness and engagement of different consumer segments.

Chapter 2. Literature review

Consumer perception of beef quality

The importance of beef quality to consumers

Beef quality is of paramount importance to consumers since consumer perception of quality determines the decision to purchase and repeat purchase, which is of utmost importance for the development and success of the beef market and industry. Beef quality is multifactorial, and consumer perception of beef quality mainly depends on four dimensions: 1) search quality (visual appeal): the morphological property of beef such as the appearance (e.g. color, visible fat), technological and convenience quality attributes (e.g. cooking yield, shelf life); 2) experience quality (sensory appeal): the eating experience of beef such as beef tenderness, juiciness and flavor liking; 3) credence quality: the safety, nutritional and health value of a product and other additional values related to certain attributes such as animal welfare and environmental sustainability; 4) quality of value (cost effectiveness): the cost/price of the product is perceived to correspond as much as possible to the value and image of the product (Grunert et al., 2004;Pethick et al., 2021). Prior to purchase, only search quality can be reached by consumers and based on the appearance of the product, consumers might develop expectations according to the available information conveyed by the extrinsic cues of a product at the time of purchase. Extrinsic quality cues primarily have their influence on the search and credence quality of the product from the outside, such as brand, origin, price and image (Grunert et al., 2004). The image value is more related to the livestock and industrial production system (Grunert et al., 2004), which would create an overall image of the product presented to consumers. On the other hand, the experienced quality is the key criteria that is most responsible for the actual demand and satisfaction of consumers and their repeat purchase intention [START_REF] Banović | Beef quality perception at the point of purchase: A study from Portugal[END_REF]. However, experienced quality can only be determined after purchase and is mainly related to the intrinsic properties of the product. These intrinsic factors cover the physical characteristics of the beef product itself, such as meat color, muscle cut, fat content and marbling distribution, (Hocquette et al., 2011), etc. Quality is sought because it contributes to the fulfillment of purchase motives (Grunert et al., 2004). Consumers expect good quality when they eat beef, but the presumed and/or experienced eating quality may not always match their expectations and the price they paid is usually higher than what the product actually deserved [START_REF] Henchion | Meat consumption: Trends and quality matters[END_REF]. This is, at least in part, the major reason why beef consumption has declined, especially in Europe. Maintaining consistency between expectations and actual experience is beneficial to a long-term consistent consumption level and to the success of the beef industry. The study of consumer decision-making on intrinsic and extrinsic cues is essential in order to understand how consumer quality perception for beef products is formed (Figure 1). More precisely, consumers' inability to predict their own quality experience after purchase is sometimes due to the scarcity of extrinsic cues and misinterpretation of intrinsic cues. For instance, a higher marbling level may not be good for the credence quality for normal consumers but it is actually good for the eating quality of the product (Grunert et al., 2004). In this situation, an appropriate extrinsic cue is needed to inform consumers of the relevant eating quality through the visible marbling. This indicates that, on one hand, the perception of beef quality is affected by both intrinsic and extrinsic quality cues and, on the other hand, the perceived intrinsic cues are affected by the perceived extrinsic cues and vice versa. This is useful for the industry to align the value more accurately with the quality of the product for marketing purposes. Much scientific and industrial effort has been devoted into closing the gap between expectation and experience through the consistency of extrinsic (e.g. brand, grade, price, etc.) and intrinsic (e.g. marbling level, eating quality, etc.) cues of the product and aligning value as closely as possible with the quality of the product. The MSA system is a good example. In this system, beef eating quality is consistently guaranteed by a specified quality grade and a money-back guarantee. Indeed, the quality of beef that consumers receive is that for which they are prepared to pay (Pethick et al., 2021) that ensures consumer satisfaction to large extent. In addition, the price premiums generated predominantly from the MSA-guaranteed quality are being noticed by the Australian beef industry [START_REF] Farmer | Review: Beef-eating quality: A European journey[END_REF]. Overall, understanding quality factors and ultimately improving beef quality is imperative to both consumers and the industry.

Extrinsic quality traits

When consumers select products in shops and markets, the extrinsic cues conveyed by the products play a dominant role in the formation of quality perception and expectation and in the subsequent purchase decision-making. Within the meat sector, the extrinsic quality traits of beef are predominantly related to production, processing and marketing, including the commercial quality of beef carcasses, brand, origin, image, quality grade, price and other product information that is of value to consumers, such as animal breed, feeding resources, breeding environment and ethical, cultural and environmental dimensions (Clinquart et al., 2022).

Commercial quality, quality grade and price

The variability in bovine carcasses and, consequently, in meat quality is high, inconsistent and multifactorial in origin (Clinquart et al., 2022). As the unit of trade and grading is based on carcasses, the commercial quality of bovine carcasses is of paramount importance not only for farmers, but also for the intermediate actors of the supply chain and retailers to ensure an optimized meat quality for consumers (Ellies- Oury et al., 2020). In many countries such as the United States, Japan and South Africa, the commercial quality of a carcass is mainly evaluated based on animal type (sex and age) and carcass weight. Additionally, conformation and fat scores are important commercial quality traits of carcasses in European countries. Other attributes can indicate carcass quality, especially with the incorporation of meat quality predictors, but these attributes are also carcass-based, which means they can be considered as commercial quality attributes of the beef carcass. With the exception of sex and carcass weight, other traits such as rib fat depth, marbling score, ultimate pH, ossification, hump height (estimate of Bos indicus content) are used by the MSA system to predict beef eating quality. All of these attributes related to carcass quality can be good references for producers to target the value proposition between commercial carcass quality and eating quality (Pethick et al., 2021). For instance, as the marbling score is a key component of beef quality, there are several mechanisms available to breeders to improve this attribute through genetics, animal type, carcass weight and fat level, high energy diets and maturity patterns, to ultimately improve the quality of beef products for consumers [START_REF] Pethick | Growth, development and nutritional manipulation of marbling in cattle: a review[END_REF]. There are several beef grading schemes (i.e. MSA, USDA, JMGA) around the world to score beef eating quality. It has been shown that quality grade is a reliable predictor of expected quality in studies on consumer willingness to purchase. Indeed, [START_REF] Lyford | Is willingness to pay (WTP) for beef quality grades affected by consumer demographics and meat consumption preferences[END_REF] found that consumers from different countries (Japan, Australia, the United States and Ireland) would be willing to pay more for beef with a higher quality grade [START_REF] Lyford | Is willingness to pay (WTP) for beef quality grades affected by consumer demographics and meat consumption preferences[END_REF]. This implies that a higher grade is linked to a better quality perception and that consumers would indeed be willing to pay a premium for the product that is perceived as valuable to them. On the other hand, price is also used as an indicator of quality since a higher price should logically correspond to a higher quality. Unlike the predominant extrinsic cues such as brand and quality grade, which are associated to a large extent with experience and credence quality (e.g. palatability, safety and health), price only influences credence quality expectations of consumers [START_REF] Banović | Beef quality perception at the point of purchase: A study from Portugal[END_REF]. In fact, price may be more related to the packaging characteristics of the product in certain circumstances, and is also a strong driver of perceived quality [START_REF] Loose | Market price differentials for food packaging characteristics[END_REF]. It has also been observed that in France, there is no apparent link between the market price of beef and the perceived tenderness of the meat by consumers. This implies that consumers can either obtain good beef at a low price or be disappointed by expensive beef by pure chance (Hocquette et al., 2012). This emphasizes that, as mentioned above, an accurate grading scheme for at least beef eating quality is paramount in order to enable valuebased purchasing for consumers.

Brand

Specifically when consumers have prior knowledge of the brand, it is regarded as the most dominant factor in forming expected quality as consumers rely on brand as a trustworthy quality indicator to help them reduce purchase uncertainty due to the high biological variability in beef quality [START_REF] Banović | Beef quality perception at the point of purchase: A study from Portugal[END_REF]. For example, Certified Angus Beef brand is reputed to be tender, juicy and flavorful, and indeed receives a higher palatability perception by consumers [START_REF] Wilfong | The effect of branding on consumer palatability ratings of beef strip loin steaks[END_REF]. Brand is also linked to the image of a product, such as the "Label Rouge" in France, which represents a superior quality in terms of palatability and credence (Hocquette et al., 2014). In addition, the information given by labels could raise consumers' expectations of extraordinarily high quality. The labelling of a superior fatty acid composition or information that the cattle were raised on natural pasture or certain specialized breeds such as Aberdeen Angus beef, could create a sense of luxury and pleasure among consumers, as they traditionally associate them with good quality [START_REF] Grunert | Extrinsic and intrinsic quality cues in Chinese consumers' purchase of pork ribs[END_REF]. In terms of "luxury", some beef brands aim to produce highly marbled products (e.g. Japanese Wagyu) with more than 10% or even 20% fat. Despite the fact that these brands are not intended for everyday consumer consumption or the mainstream market, and do not have a strong focus on the health and nutrition issues that consumers value, they still have a market share due to the premium and luxury they are associated with, which can give consumers a sense of social importance when consuming these products (Pethick et al., 2021).

Origin

Origin has often been considered by consumers as an important predictor to product value, while this quality cue does not seem to have an impact on eating quality [START_REF] Becker | Consumer perception of fresh meat quality in Germany[END_REF]. In fact, as pointed out by [START_REF] Loureiro | A choice experiment model for beef: What US consumer responses tell us about relative preferences for food safety, country-of-origin labeling and traceability[END_REF], origin can only become a symbol of superior quality if consumers associate this origin with higher quality and safety. Despite the lack of direct influence of origin on quality, an indirect relationship actually exists through the emotional connection established for consumers that influences perceived intrinsic cues, which in turn influence expected quality [START_REF] Banović | Beef quality perception at the point of purchase: A study from Portugal[END_REF]. Consumers also believe that local breeds are closer to the terroir (Raulet et al., 2021). In Europe, quality origin ('PDO' for protected designation of origin and 'PGI' for protected geographical indication) represents excellence in European agricultural food production and is valued by consumers for the unique combination of human and environmental factors that are based on specific quality characteristics derived from a specific geographical origin (Hocquette et al., 2014). As in 2019, the European Union had a total of 1421 PDO and PGI registered products.

Image

In contrast to monogastric animals, the arguments against beef production do not so much concern food competition with humans, but are more related to environmental sustainability in terms of greenhouse gas (GHG) emissions and use of land and water (Clinquart et al., 2022). Another issue in relation to beef production is animal welfare, such as the animal suffering caused by factory farming systems to boost meat productivity. The environmental, ethical and culture aspects related to how the product is produced and processed all contribute to the quality perception of consumers. These image value attributes are therefore useful indicators of the quality of a beef product. Since livestock production is considered as a primary source of greenhouse gas emissions, the "low-carbon diet" has become a new trend in the catering sector [START_REF] Shen | Exploring Consumers ' Purchase Intention of an Innovation of the Agri-Food Industry : A Case of[END_REF]. A "carbon label" can remind consumers of the GHG impact of the food product. This image has a positive association with consumers' perception of the quality of the food product [START_REF] Camilleri | What ' s your beef ? How ' carbon labels ' can steer us towards environmentally friendly food choices[END_REF]. The animal welfare assessment system developed following the European Welfare Quality project could improve the quality of the product image if the assessment can be applied on the products and the information could be provided to consumers. Indeed, animal welfare on-package labels can boost consumer appreciation and purchase intention [START_REF] Cornish | The price of good welfare: Does informing consumers about what on-package labels mean for animal welfare influence their purchase intentions[END_REF]. Some animal welfare regulations during animal transport and slaughter to reduce animal stress can be a decisive factor for image-related quality attributes. Furthermore, some consumers are more willing to pay for on-farm slaughtered beef products [START_REF] Carlsson | Consumer willingness to pay for farm animal welfare: Mobile abattoirs versus transportation to slaughter[END_REF]. In fact, image quality is a broad concept since almost all the extrinsic attributes can constitute the holistic image of a product. In other words, consumer perception of food quality at the time of searching/purchasing is based on the overall image of the product, which is indeed a major driver of purchase decision-making [START_REF] Aboah | Consumers use of quality cues for meat purchase: Research trends and future pathways[END_REF]. More importantly, consumer perception is also associated with ethical and environmental sustainability, and therefore improving the image value of beef products could be a key strategy for the industry.

Intrinsic quality traits

Intrinsic quality traits mainly include three categories: 1) appearance, which is part of the physical characteristics of the product that visually define a given category of beef product such as muscle cut, meat color, fat color, fat trim, marbling and exudate [START_REF] Acebron | The importance of intrinsic and extrinsic cues to expected and experienced quality: an empirical application for beef[END_REF]; 2) sensory quality, which is the perceived overall quality of beef (e.g. eating experience) and preferences for individual sensory responses during beef consumption (e.g. taste, tenderness, juiciness, flavor, aroma, freshness and leanness) (Grunert et al., 2004); 3) health quality, which is associated with credence quality, including safety, nutritional composition and healthiness of the product [START_REF] Lähteenmäki | Claiming health in food products[END_REF]. In contrast to extrinsic quality factors, most intrinsic factors are more relevant for predicting the quality of experience during consumption [START_REF] Marreiros | A conceptual framework of consumer food choice behaviour[END_REF]. The contributions of factors such as brand and price are likely to decline over time due to fierce domestic market competition, so other factors, such as the sensory quality of beef, will continue to become increasingly important to consumers [START_REF] Henchion | Meat consumption: Trends and quality matters[END_REF].

Appearance

Consumers can detect differences in quality through the visual appearance of the beef product, Indeed, the appearance of fresh meat is of great importance for consumer purchase [START_REF] Borgogno | The role of product familiarity and consumer involvement on liking and perceptions of fresh meat[END_REF]. A whole raw steak on display could give the feeling of a good quality premium food. At the time of purchase, meat color and fat color are critical indicators of freshness and quality for consumers [START_REF] Smith | Economic implications of improved color stability in beef[END_REF]. Bright, cherry-red meat color and white fat color are more desirable than dark meat and yellow fat to consumers [START_REF] Ardeshiri | How Australian consumers value intrinsic and extrinsic attributes of beef products[END_REF]. In addition, marbling represents the visual appraisal of intramuscular fat (IMF) content and consumer perception of marbling is negative to a large extent due to the sign of excess fat [START_REF] Morales | Beef acceptability and consumer expectations associated with production systems and marbling[END_REF], which is not as "trimmable" as preferred by most consumers. Nevertheless, quality preferences vary from one individual to another, Killinger et al. (2004) found that consumers who preferred low marbling appeared to want lean products, whereas those who preferred high marbling favored products of superior eating quality (Killinger et al., 2004b). Overall, when consumers select beef products, they rule out the influence of extrinsic quality traits, with the appearance of the product playing a predominant role at this point. For instance, as it is well known in the beef industry, Pale Soft Exudative (PSE) and Dark Firm Dry (DFD) meat products are unacceptable to consumers in shops due to their appearance defects, which are obviously related to low quality [START_REF] Adzitey | Pale soft exudative (PSE) and dark firm dry (DFD) meats: causes and measures to reduce these incidences-a mini review[END_REF]. However, appearance cannot guarantee consistent eating quality at all times. For example, it has been observed that around 15% of the retail beef in some cities in the United States does not meet the expectations of the bright cherry red lean designation (Killinger et al., 2004a). Additionally, another important factor is the morphological integrity or intactness of the primal cuts: for some consumers, when these muscle cuts are taken directly from the carcass without any processing, this implies naturalness and safety. On the other hand, meat that has been processed, even for the purpose of tenderization, may induce negative consumer attitudes (Yang et al., 2021).

Technological and convenience quality attributes

Technological and convenience quality attributes are also factors that consumers take into consideration when selecting beef products at the time of purchase. Technological quality is associated with the storage (e.g. shelf life) and processing (e.g. cooking yield) of food, which are influenced by the chemical and microbiological properties (e.g. water/fat holding capacity, antioxidant capacity, bacterial growth) and storage requirements (shelf life, temperature, light, package) of the meat (Clinquart et al., 2022). These quality attributes are also related to the practicality and usability of a product, which is known as convenience quality. For example, a product design based on consumer benefits (e.g. time and effort saving), such as ready-to-cook foods or foods that can be kept at a normal temperature for a long time, would play a positive role in shaping consumer quality perception. Despite these trends, the proper evaluation and indication of technological and convenience quality attributes need to be better explored and formalized (Prache et al., 2021).

Eating quality

Due to the limited ability of objective and accurate measurements to capture the variance of meat tenderness from actual consumer eating experience, sensory evaluations of meat by trained panelists or untrained consumers have been developed and are widely used in meat sensory research. Meat tenderness depends mainly on three primary factors: (1) background toughness related to connective tissue; (2) degree of muscle contraction; (3) integrity/degradation of the myofibrillar structure during aging and tenderization [START_REF] Hopkins | Protein degradation post mortem and tenderisation[END_REF]. In early research on beef sensory evaluation, tenderness was assessed by muscle fiber, connective tissue and IMF characteristics in addition to global tenderness evaluation (Miller, 2017). The perception of tenderness through direct measurements (consumer and/or sensory panel) includes three aspects: the ease with which the teeth penetrate the meat at first, the ease with which the meat splits into fragments during chewing, and the amount of residue left after chewing. This illustrates the complexity of tenderness in its definition and measurement. Consumer satisfaction with meat tenderness is based on the interaction between the physical/textural characteristics of the meat and the "mouthfeel" -an experience related to the sensations of biting and chewing (Warner et al., 2021). Beef muscle contains approximately 75% water and the ability to hold water and bind it in the meat during processing is strongly associated with beef texture and palatability [START_REF] Warner | The Eating Quality of Meat-IV Water-Holding Capacity and Juiciness[END_REF]. Meat juiciness is defined as the perceived amount of juice and the level of lubrication when meat is masticated in the mouth. It is mainly affected by the inherent properties of the meat such as water holding capacity, fat content and prerigor muscle metabolism; the physiological state of the tasters such as taste sensation also has an impact on the perception of meat juiciness. Therefore, as a unique subjective property of meat, a relevant measure of juiciness is achieved by sensory evaluation with consumers and/or panelists. The evaluation of meat juiciness can be performed in two steps: (1) initial juiciness, which is the initial impression of meat fluids released by the first chews of the meat and which is related to the water content of the meat; (2) sustained or overall juiciness, the perception of juiciness during sustained mastication known as associated with fat content, which is considered to be the result of the stimulating effect of fat on salivary flow with different individual tasters [START_REF] Winger | Juiciness-its importance and some contributing factors[END_REF]. As early as 1972, meat scientists found that juiciness accounted for part of the variance (less than 19%) in meat texture [START_REF] Harries | MEAT TEXTURE: I. Subjective Assessment of the Texture of Cooked Beef[END_REF]. In the first Meat Descriptive Sensory Evaluation published by the American Meat Science Association (AMSA), juiciness was used as a key factor in evaluating meat eating quality [START_REF] Cross | Guidelines for cookery and sensory evaluation of meat[END_REF]. With the development of sensory evaluation, juiciness plays a consistent role in meat eating quality. In the MSA system, 10% of the variability in consumer acceptance is explained by juiciness (Watson et al., 2008). For European consumers, juiciness contributes to approximately one quarter of the overall palatability of beef (Liu et al., 2020a). Flavor is a very complex sensation detected by humans which involves a combination of olfactory and gustatory sensations that detect basic taste and aromas [START_REF] Delwiche | The impact of perceptual interactions on perceived flavor[END_REF]. Physical factors (i.e. breed, sex and age) and chemical traits (i.e. fatty acid profile) have heavy impacts on the reactions within beef during the cooking process with regard to the production of volatile aroma compounds and the taste of the beef (Kerth & Miller, 2015). Flavor has always been considered as an important component of beef eating quality to consumers. Efforts have been made and documented to formally improve beef flavor for more than two hundred years. In the evolution of beef sensory quality research, flavor was included in the sensory description system in 1995 by the American Meat Science Association [START_REF] Stock | Research guidelines for cookery, sensory evaluation, and instrumental tenderness measurements of fresh meat[END_REF], twenty years after the introduction of tenderness and juiciness. Nevertheless, meat scientists still regarded beef flavor as the second most important attribute for beef eating quality and consumer acceptance, with tenderness being the first most important (Miller et al., 1995). In recent decades, in the MSA system, flavor liking has become as important as tenderness (Watson et al., 2008). Furthermore, with the improvement of tenderness in recent decades, flavor is considered the most important determinant of variability in beef eating quality (Liu et al., 2020a). Beef flavor has been expanded to describe specific components such as speciesspecific flavor (beef broth) or descriptive attributes formed from the Beef Lexicon (fat flavor, bloody, grainy, grassy, cardboard, painty, fishy), and these attributes are related to consumer sensory attributes (Kerth & Miller, 2015). In current studies, flavor liking is used in the MSA system with untrained consumers, and typical flavor and abnormal flavor are used in beef evaluation with panelists.

Microbiological quality

As indicated by Clinquart et al. (Clinquart et al., 2022), microbiological quality is essential for beef safety and health quality. Indeed, foods of animal origin (e.g. beef, chicken and pork) are major reservoirs of many foodborne pathogens such as Shiga toxin-producing E. coli, Salmonella and Campylobacter. Illness and even death that are caused by meat-related foodborne pathogens raise great concern for the conventional meat industry [START_REF] Heredia | Animals as sources of food-borne pathogens: A review[END_REF]. In Europe, the prevalence of Salmonella in cattle is about 2% [START_REF] Gutema | Corrigendum: Prevalence and Serotype Diversity of Salmonella in Apparently Healthy Cattle: Systematic Review and Meta-Analysis of Published Studies, 2000-2017[END_REF]. Bacterial contamination of meat occurs during the muscle to meat conversion, transport and slaughter, processing, storage and cooking, pre-slaughter stress is identified as a factor affecting Salmonella and pathogenic E. coli contamination of animals [START_REF] Niyonzima | Risk Factors and Control Measures for Bacterial Contamination in the Bovine Meat Chain: A Review on Salmonella and Pathogenic E.coli[END_REF]. A hygienic operating environment on the slaughter floor and chilling are essential elements in controlling all biological hazards. The microbiological issue of beef products can be serious when the meat is raw or undercooked. The meat product, especially processed beef (ground beef), must therefore meet at least the microbiological criteria set out in the relevant regulations (Clinquart et al., 2022). There is no doubt that microbiological quality can affect human health, while synthetic pesticides, antimicrobials and growth hormones used during animal production to treat infections and prevent diseases and also to optimize growth are another problem that threaten human health [START_REF] Ma | Use of antimicrobials in food animals and impact of transmission of antimicrobial resistance on humans[END_REF], and should therefore be rejected for consumption, especially for sensitive individuals.

Nutritional quality

Meat plays a crucial role in human evolution through the supply of essential macro and micronutrients, including high biological value proteins, fatty acids, iron, zinc, selenium and vitamins B3, B6 and B12. Many factors such as animal type, farming system, muscle type, processing and cooking have an impact on the concentration of these macro and micronutrients. Consumers eat meat because it is delicious in taste and necessary for its good nutritional quality (Ellies- Oury et al., 2019). Thus, consumers tend to prefer organic food, which ensures that synthetic fertilizers, pesticides and hormones are avoided in the production process and that the use of veterinary drugs is minimized (Prache et al., 2021). In addition, previous studies have demonstrated that organic beef has higher nutritional value than conventional beef in terms of improved bioactive compound content and a better balanced fatty acid (FA) composition, with a higher level of poly-unsaturated fatty acids (PUFAs) especially n-3 PUFAs (Prache et al., 2021). In view of the importance consumers place on nutritional value, they would be willing to pay a premium for organic meat [START_REF] Sarma | Consumers willingness to pay for organic beef: Evidence from Dhaka City[END_REF], especially for a better composition of beneficial FAs [START_REF] Flowers | Consumer preferences for beef with improved nutrient profile[END_REF]. In addition, concern about chronic nutrition-related complications is in contradiction with the desire to consume meat, which might have a higher fat content for better eating quality. In general, consumers already perceive meat as a healthy component in their diet. With the evaluation of consumer expectations, an increased interest in credence quality and health quality has been observed, which were identified above as often being related to the quality of the production process (Henchion et al., 2017). Consumers consider high animal welfare standards or natural grass feeding to be associated with increased safety, healthiness and eating quality of food [START_REF] Fallon | Animal welfare guidelines for beef cattle farms[END_REF]. On the other hand, consumers are increasingly concerned about foodrelated risks and prefer natural foods (i.e. non-invasive technologies or non-chemical processes) to artificially produced foods [START_REF] Buch | Evaluation of efficacy of turmeric as a preservative in paneer[END_REF]. Similarly, some consumers are opposed to novel products such as cell-based meat, due to concerns about unnaturalness and high degree of artificial production, with no assurance that cell-based meat will be safe and healthy (Chriki and Hocquette, 2020).

Main factors affecting beef eating quality

As one of the most commonly consumed protein sources, beef is an important food in the World [START_REF] Holman | Red meat (beef and sheep) products for an ageing population: a review[END_REF]. However, a global decline in beef consumption has been observed over the last two decades in Europe [START_REF] Farmer | Review: Beef-eating quality: A European journey[END_REF]. Beef consumption is highly associated with beef eating quality and consumer satisfaction. The complexity of guaranteeing beef eating quality and the inability to select beef with consistent palatability have therefore been regarded as major factors in explaining the decline of beef consumption (Hocquette et al., 2018). This is because beef quality, especially palatability, is characterized by inherently variability and depends on many interacting factors that are difficult to manage, such as antemortem factors including sex, age, maturity and breed of animals, carcass fat level, FA composition of cuts, and postmortem factors involving the slaughter process, carcass handling, aging and storage after cooking (Devlin et al., 2017). This section mainly focuses on the factors that affect the beef eating quality. The ranges of factors affecting each set of core quality attributes are summarized in Table 1.

Antemortem factors affecting beef eating quality

Breed

Bos taurus and Bos indicus are the two main cattle breed groups in the world. They are genetically adapted to survive with high productivity in adverse conditions, including heat, drought and poor-quality pastures. It is well known that the meat produced by Bos indicus cattle tends to be of lower quality [START_REF] Eblex | Quality Standard Mark Scheme for Beef and Lamb[END_REF]. Indeed, some beef cuts from Bos indicus cattle can be tough due to the genetical effect on the calpain-calpastatin system, muscle fiber size and metabolic properties, which result in inhibited protein degradation and ultimately decreased sensory tenderness [START_REF] Wright | Brahman genetics influence muscle fiber properties, protein degradation, and tenderness in an Angus-Brahman multibreed herd[END_REF]. This has led some labeling systems to exclude Bos indicus meat from their certified brands, thus hindering the presence of Bos indicus meat in major markets [START_REF] Bressan | Discrimination of meat produced by bos taurus and bos indicus finished under an intensive or extensive system[END_REF]. In fact, it has been observed that other differences such as IMF deposition and FA profile of meat produced by Bos taurus and Bos indicus cattle depend mainly on feeding system [START_REF] Bressan | Genotype × environment interactions for fatty acid profiles in Bos indicus and Bos taurus finished on pasture or grain[END_REF]. Breed-related differences in beef eating quality have long been discussed with respect to grow path and age at physiological maturity, which are mainly reflected in muscle structure, the content and solubility of connective tissue and the amount, composition and distribution of adipose tissue, especially IMF in beef (Bonny et al., 2017). The beef produced by the Wagyu breed, notably characterized by its intense marbling [START_REF] Motoyama | Wagyu and the factors contributing to its beef quality: A Japanese industry overview[END_REF], has a more intense flavor and juiciness than that of the Angus breed [START_REF] Frank | Sensory and flavor chemistry characteristics of Australian beef: Influence of intramuscular fat, feed, and breed[END_REF]. Intramuscular adipose tissue matures late and accumulates as the animal grows and matures, with IMF being deposited after intermuscular fat, which is itself deposited after subcutaneous fat (Pethick et al., 2006). Therefore, at similar levels of maturity, early maturing breeds (e.g. traditional British beef breeds such as Angus and Hereford) have a tendency to deposit more IMF and can be slaughtered at lower weights than with a higher fat content, compared to late-maturing beef breeds (e.g. continental European breeds such as French Limousin, Charolais, Blonde d'Aquitaine and Belgian Blue), which have relatively less intermuscular fat. With a different level of marbling, the beef eating quality is therefore different for these two types of breeds. Beef quality is multi-determined and must be analyzed based on many factors to avoid a biased comparison. For example, in general, the eating quality of meat from beef breeds is considered better than dairy breeds. However, untrained consumers reported hardly any differences in taste quality between meat from dairy and meat breeds, except for a few muscles (Bonny et al., 2016). Although breed has an important effect on beef sensory quality, beyond differences in carcass characteristics, breed might explain only a small part of the variability in beef quality or sometimes may not explain it at all. For instance, Conanec et al. (2021) observed very few differences in beef sensory quality between beef aged under the same conditions and produced by young bulls from 15 European breeds reared under relatively similar conditions (Conanec et al., 2021).

Sex

There are several differences between sex categories related to hormone level and muscle composition and in interaction with genotype [START_REF] Bonfatti | Genetic associations between daily BW gain and live fleshiness of station-tested young bulls and carcass and meat quality traits of commercial intact males in[END_REF]. Heifers are identified as more tender than bulls and steers with less intramuscular connective tissue content and smaller muscle fiber diameter (Venkata Reddy et al., 2015). In contrast, bulls grow more rapidly and produce carcasses with less fat and more redoxidative muscle than steers (Seideman et al., 1982). Steers are rated less tough and more palatable with more IMF compared to bulls [START_REF] Nogalski | Italian Journal of Animal Science Carcass characteristics and meat quality of bulls and steers slaughtered at two different ages Carcass characteristics and meat quality of bulls and steers slaughtered at two different ages[END_REF]. Moreover, it was found that even after adjusting for different carcass traits, meat from bulls had lower eating quality scores than meat from females and steers (Bonny et al., 2016). Sex also influences meat color especially in combination with age, female animals tend to deposit more pigment with age than males [START_REF] Monin | Facteurs biologiques des qualités de la viande bovine[END_REF]. However, in general, due to higher physical activity and myoglobin concentrations, the meat from intact males is darker than that of females and castrated males [START_REF] Neethling | Exogenous and Endogenous Factors Influencing Color of Fresh Meat from Ungulates[END_REF]. Nonetheless, in practice, the meat from females usually comes from dairy cows or cull cows slaughtered at a later age, which usually results in a darker color (Clinquart et al., 2022).

Animal age and maturity

In general, increasing age and maturity is correlated with a decrease in eating quality. With increasing age and maturity of the animal, the collagen content increases and the heat stability of the collagen declines, the shear force and toughness of the cooked beef proportionally increases [START_REF] Hopkins | Genotype and age effects on sheep meat production 3. Meat quality[END_REF]. Meat color and fat color are generally influenced by animal age, with L* and a* values higher for older animals than younger ones [START_REF] Kopuzlu | Effects of slaughter age and muscle type on meat quality characteristics of Eastern Anatolian Red bulls[END_REF]. Meanwhile, older animals tend to contain more fat and the percentage of IMF increases with a concomitant increase in the percentage of monounsaturated fatty acids (MUFAs) and a decrease in that of polyunsaturated fatty acids (PUFAs), and this is associated with better flavor intensity [START_REF] Watkins | Sheepmeat Flavor and the Effect of Different Feeding Systems: A Review Project investigating interactions between Pater Natalis and Rangifer Tarandus View project Vitamins in legumes View project[END_REF], but lower healthiness due to higher proportions of saturated fat. In addition, it was found that the decrease in tenderness appears to be less pronounced with beef from animals over 18 months of age compared to animals under 18 months of age, although this is animal and muscle dependent [START_REF] Hopkins | The eating quality of meat: II-Tenderness[END_REF]. Moreover, the flavor intensity of beef tends to increase up to the age of 18 months and thereafter reaches a plateau [START_REF] Flores | The Eating Quality of Meat: III-Flavor[END_REF]. [START_REF] Kopuzlu | Effects of slaughter age and muscle type on meat quality characteristics of Eastern Anatolian Red bulls[END_REF] also found for Eastern Anatolian Red bulls that beef tenderness, juiciness, flavor and overall acceptability increased until the animals reached 19 months of age [START_REF] Kopuzlu | Effects of slaughter age and muscle type on meat quality characteristics of Eastern Anatolian Red bulls[END_REF].

Feeding system, fat content and marbling

The feeding system has effects on beef quality since the nutrient composition and energy intake of the diet can affect the animal's growth rate, degree of maturity and carcass composition, particularly the amount of IMF and the fatty acid profile, which corresponds to the beef sensory and nutritional quality valued by consumers [START_REF] Janssen | Influence of Grain-and Grass-Finishing Systems on Carcass Characteristics, Meat Quality, Nutritional Composition, and Consumer Sensory Attributes of Bison[END_REF]. Diet composition and finishing management have different effects on beef quality traits, especially for different animal categories. Specific analyzes are therefore necessary to determine the impact of feeding on beef sensory quality in specific circumstances, such as when animals are inconsistently characterized. In the beef industry, different finishing systems are used, resulting in beef product variations. In general, beef produced in extensive production systems is considered to have a healthier fatty acid composition, and pasture-based feeding strategies are developed for this purpose as consumers prefer grass-fed beef as it is perceived to be healthier and "greener" (Stampa et al., 2020). However, scientists found that rearing systems (indoor rearing vs. outdoor grazing) had no major impact on Warner Bratzler shear force (WBSF), texture profile, WHC, and most of the sensory attributes of m. longissimus dorsi lumborum from Podolian beef [START_REF] Marino | Effect of grazing and dietary protein on eating quality of Podolian beef[END_REF]. However, in general, due to higher IMF accumulation, grain-finished beef from feedlot systems is perceived as superior to that of grazing systems and/or forage/pasture-finished cattle, which tend to produce leaner beef [START_REF] Duckett | Effects of winter stocker growth rate and finishing system on: III. Tissue proximate, fatty acid, vitamin, and cholesterol content[END_REF]. In addition, from the perspective of eating quality, some consumers prefer grain-fed/finished beef because pasture/forage-fed/finished beef contains specific pastoral flavors such as "grassy", "wild" and "barny" and lacks the normal beef flavor (reviewed by Pogorzelski et al., 2022). In contrast, forage/pasture-finished beef generally has an increased conjugated linoleic acid (CLA) and PUFA (polyunsaturated fatty acid) to SFA (saturated fatty acid) ratio (Elmore et al., 2004), which is better for human health, especially in reducing the incidence of many diseases such as heart and cardiovascular diseases. Additionally, pasture quality is an important element in differentiating beef quality. Therefore, meat from pasture-fed cattle may not only of comparable quality to meat from grain-fed animals [START_REF] Latimori | Diet and genotype effects on the quality index of beef produced in the Argentine Pampeana region[END_REF], but may even be more tender [START_REF] Realini | Effect of pasture vs. concentrate feeding with or without antioxidants on carcass characteristics, fatty acid composition, and quality of Uruguayan beef[END_REF]. One of the traits most influenced by feeding practices is IMF, which is well known to affect beef eating quality. It has been reported that grain-finished beef is considered to have a more acceptable flavor than forage-finished beef [START_REF] O'quinn | Identifying consumer preferences for specific beef flavor characteristics in relation to cattle production and postmortem processing parameters[END_REF] due to a higher IMF content. The fat content of a beef carcass is composed of adipose tissue deposited in the abdominal cavity (perirenal, mesenteric and omental), intermuscular, subcutaneous and intramuscular (Pethick et al., 2006). Of these, IMF content plays a key role in beef eating quality [START_REF] Boito | Influence of subcutaneous fat thickness on the carcass characteristics and meat quality of beef cattle[END_REF], although the relationships between them may depend on confounding effects such as animal breeds, animal sex and age, and feeding systems. IMF content refers to the lipid deposit in muscle and is an objective measure of the total triglyceride and phospholipid content present on a microscopic level [START_REF] Pethick | Growth, development and nutritional manipulation of marbling in cattle: a review[END_REF]. The visible portion of the IMF is termed "marbling" and is widely used as an indicator of IMF content and meat quality in beef grading systems in the USA and Australia (Bonny et al., 2017). While marbling accounts for nearly 75% of the variation in IMF (Ferguson, 2004), chemical IMF% and marbling level are similar in their prediction of eating quality in m. longissimus thoracis and lumborum and other cuts [START_REF] Irshad | Factors Influencing Carcass Composition of Livestock: a Review[END_REF]. Fat is not defined as a basic sensory trait but provides meat with specific mouthfeel and lubrication between muscle fibers that could increase the perception of tenderness and juiciness, and in particular provides meat with a flavor profile and aromas (Miller, 2017). Numerous studies have investigated the relationship between IMF/marbling and beef sensory quality. It has been reported that 10-15% of the variance in tenderness evaluation could be explained by marbling [START_REF] Dikeman | The relationship of animal leanness to meat tenderness[END_REF], and that 2% to 56% of the variation in flavor could be explained by IMF content (Hocquette et al., 2011). Although no evidence shows that excess fat leads to a progressive increase in flavor and palatability [START_REF] Toldrá | The Storage and Preservation of Meat: III-Meat Processing[END_REF], higher IMF content could lead to diminishing returns on beef sensory traits. Undoubtedly, a range of acceptability for IMF and marbling could improve beef eating quality (Bonny et al., 2017). However, significant but varied associations with sensory quality attributes are often observed as this relationship is highly dependent on confounding factors, including animal breed, age and sex. Nevertheless, several studies agree that there is a curvilinear relationship between IMF content and beef flavor: flavor intensity increases with IMF content, then reaching a plateau at higher levels of IMF content (Thompson, 2004;Hocquette et al., 2011).

Pre-slaughter stress

Prior to slaughter, animals are exposed to certain situations that can trigger a stress response that can reduce the eating quality of the meat. Improper handling during transport and at the abattoir can lead to muscle glycogen depletion and inadequate acidification and ultimately high pH, resulting in dark cut beef and reduced sensory tenderness [START_REF] Gruber | Relationships of behavioral and physiological symptoms of preslaughter stress to beef longissimus muscle tenderness[END_REF], juiciness and flavor [START_REF] Hemsworth | Human-animal interactions at abattoirs: Relationships between handling and animal stress in sheep and cattle[END_REF]. However, higher pH is not always the only reason for the reduced quality of stressed cattle. This has been confirmed by several studies: pre-slaughter stress was found to have a negative impact on consumer-assessed eating quality, even if the ultimate pH of the carcass was compliant (pH≤5.7) [START_REF] Ferguson | Have we underestimated the impact of preslaughter stress on meat quality in ruminants?[END_REF] and, with a compliant pH, WBSF was higher in stressed cattle [START_REF] Gruber | Relationships of behavioral and physiological symptoms of preslaughter stress to beef longissimus muscle tenderness[END_REF]. There is no doubt that pre-slaughter stress is associated with lower beef eating quality and it has been demonstrated that mixing and transporting animals prior to slaughter was associated with lower eating quality for some cuts and that a two-week rest period in the slaughterhouse prior to slaughter is beneficial in improving consumer perception of beef sensory scores [START_REF] Loudon | The impact of pre-slaughter stress on beef eating quality[END_REF]. The beef industry and some quality grading systems, such as MSA, have developed pathways to minimize the adverse effects of physical activity and emotional stress prior to slaughter. For instance, different lairage periods are recommended according to the transport journey to enable animals to rest, rehydrate and replenish their glycogen stores [START_REF] Del Campo | Finishing diet, temperament and lairage time effects on carcass and meat quality traits in steers[END_REF]. In the MSA system, some pre-slaughter pathways that may maximize stress could be penalized such that cattle sold in the saleyard prior to slaughter are deducted 5 points from the final MSA meat quality score (Watson et al., 2008).

Postmortem factors affecting beef eating quality

Post-harvest aging and pH/temperature decline

Post-harvest aging is a value-adding process which involves storing the carcass at cold temperatures for varying periods of time, profoundly affecting the biophysical and biochemical modification conditions of the carcass through regulating postmortem energy metabolism, proteolysis and apoptosis [START_REF] Kim | Understanding postmortem biochemical processes and post-harvest aging factors to develop novel smart-aging strategies[END_REF]. These processes lead to a progressive increase in tenderness and flavor with the disintegration of muscle structure and the release and accumulation of peptides and free amino acids. Based on theoretical knowledge, several practical adjustments could be implemented to improve beef palatability with some treatments such as aging, with some breeds showing optimum tenderness at short aging periods and other breeds requiring longer aging to achieve similar consumer acceptance [START_REF] Monsón | Influence of breed and ageing time on the sensory meat quality and consumer acceptability in intensively reared beef[END_REF]. Several beef grading systems use aging time as a parameter to guarantee/predict beef quality. For example, according to MSA, five days of aging is required as a minimum aging period; for the French Label Rouge, ten days is generally considered (reviewed by Pogorzelski et al., 2022). Longer aging times up to a certain level are generally good for better palatability: for m. longissimus dorsi, it takes 4.3 and 10 days to reach 50 percent and 80 percent of total aging respectively. The aging process affects muscles differently: slow-twitch muscles are thought to age more slowly than fast-twitch muscles [START_REF] Dransfield | Optimisation of tenderisation, ageing and tenderness[END_REF]. The tenderness of m. psoas major and m. semitendinosus needs 7 and 14 days to improve whilst m. longissimus lumborum can achieve the most tender score at 21 days [START_REF] Nair | Musclespecific effect of aging on beef tenderness[END_REF].

Extensive consumer studies have shown that beef eating quality is negatively affected when the carcass enters rigor mortis, which refers to contraction of muscle fibers [START_REF] Jacob | Techniques to reduce the temperature of beef muscle early in the post mortem period-a review[END_REF]. If the temperature drops too quickly (below 12 degrees Celsius) and the pH is high (above 6) during rigor, this combination known as "cold shortening", could cause muscle contractions that could increase toughness by up to four times. In contrast, the combination of a high temperature of carcass (above 35 degrees Celsius) and rapid pH decline (below 6), called "heat shortening", could cause muscle protein denaturation and muscle shortening, which could lead to increased meat toughness and dryness [START_REF] Warner | Factors influencing the incidence of high rigor temperature in beef carcasses in Australia[END_REF]. Therefore, the control of pH and temperature decline within the optimum pH/temperature window (pH of 6 between 15-35℃) would be an effective way to limit the extent of muscle shortening and optimize eating quality. Meanwhile, after muscle toughening, tenderization also takes place during the postmortem period [START_REF] Hopkins | The relationship between tenderness, proteolysis, muscle contraction and dissociation of actomyosin[END_REF] based on certain biochemical reactions such as proteolysis. Among these, calpains play a primary role in meat tenderization during postmortem aging and could be optimally activated under regulation of the physiological pH of skeletal muscle [START_REF] Goll | The calpain system[END_REF]. Calpain activity is optimized with an intermediate pH decline to 6.0 at 1.5 hours postmortem (Hwang & Thompson, 2001). In addition, being compatible with pH, temperature plays a key role during postmortem aging, with a too low temperature having negative impacts on tenderization by slowing down enzyme activity. Meat eating quality was found to be greatly improved when carcasses reaching 21°C at pH 6 [START_REF] Kim | Understanding postmortem biochemical processes and post-harvest aging factors to develop novel smart-aging strategies[END_REF]. Aging of 86% can be achieved when carcasses are held at 30°C for 24 hours [START_REF] Devine | Electrical stimulation[END_REF]. Nevertheless, for microbiological growth and food safety reasons, high temperature aging is not practically useful for the industry. Therefore, an optimal pH/temperature intervention can be conducted postmortem to optimize the tenderization. Aging methods can generally be classified as wet and dry or a combined method in a stepwise dry/wet aging process. Dry aging is less applied than wet aging due to higher cost, more stringent operational requirements, lower sealing yields and longer aging time. However, in contrast to the "wet-aged flavor" which is sour, metallic and bloody, dry aging is increasingly appealing to consumers because of the perceived "dry-aged flavor" as nutty, roasted or butter, which is due to the concentration of typical aroma compounds that dry aging provides. Dry aging is also found to improve the eating quality of beef with a lower marbling level (reviewed by [START_REF] Gagaoua | Current research and emerging tools to improve fresh red meat quality[END_REF].

Electrical stimulation (ES)

Two mechanisms could explain the effect of ES on tenderization. The primary effect is to reduce cold shortening by accelerating glycolysis and rapid pH drop to avoid the temperature drop at which cold toughness occurs [START_REF] Dikeman | Sensory and Meat Quality, Optimization of[END_REF]. The secondary effect is to accelerate proteolysis by stimulating the release of Ca ions at a higher temperature [START_REF] Hwang | The biochemical and physical effects of electrical stimulation on beef and sheep meat tenderness[END_REF] and to increase disruption of muscle structure. Based on these two effective tenderization mechanisms, ES has therefore been applied in the worldwide meat industry for decades to achieve optimal tenderization, especially in combination with pH/temperature controls. It has been reported that when carcasses were electrically stimulated and held at 35°C for 3 hours, a fast drop in pH to 6 and significant increases in μ-calpain activity and ultimately in tenderness were observed [START_REF] Rhee | Effect of low voltage electrical stimulation and temperature conditioning on postmortem changes in glycolysis and calpains activities of Korean native cattle (Hanwoo)[END_REF]. In addition to the beneficial effect of ES on tenderness, some improvements are observed regarding juiciness and flavor and overall satisfaction [START_REF] Arroyo | Effect of Pulsed Electric Field treatments at various stages during conditioning on quality attributes of beef longissimus thoracis et lumborum muscle[END_REF], as the perception on those sensory traits in electrically stimulated meat is more impacted by the fat content [START_REF] Juárez | Beef texture and juiciness[END_REF]. The voltage of ES has long been investigated, with the use of high-voltage ES (3600 V) being first investigated [START_REF] Chrystall | Electrical stimulation and lamb tenderness[END_REF], followed by low-voltage ES (32 V) [START_REF] Taylor | Low vlotage electrical stimulation of beef carcasses[END_REF], which was more used in the industry due to safety concerns. In fact, high and low voltage ES with different durations can achieve the same tenderization effect [START_REF] Gursansky | Tenderness enhancement of beef from Bos indicus and Bos taurus cattle following electrical stimulation[END_REF]. Recent research has focused on combining chilling methods (tenderstretch, super chilled storage and long aging time) with new technologies such as the so-called new generation medium voltage ES. The tenderness of meat subjected to medium voltage electrical stimulation has all been improved due to various reasons such as physical disruption of the muscle structure [START_REF] Zhang | Effect of new generation medium voltage electrical stimulation on the meat quality of beef slaughtered in a Chinese abattoir[END_REF] and myofibrillar degradation [START_REF] Ji | Effect of medium voltage electrical stimulation and prior ageing on beef shear force during superchilled storage[END_REF].

Carcass suspension

Several hanging methods have been used to improve meat tenderness during postmortem aging. Achilles tendon is the most traditional and widely used carcass suspension method, although it cannot prevent the majority of muscle shortening, but with the appropriate aging process, Achilles tendon still can achieve the tenderization potential of beef cuts. In comparison with Achilles tendon, Tenderstretch increases tension and results in more tender meat but this varies between muscles, with improved eating quality in most hindquarter muscles [START_REF] Hopkins | Tenderizing mechanisms: mechanical[END_REF]. In general, different muscles could respond differently to postmortem aging and, therefore, muscle-specific aging strategies could improve tenderness and overall eating quality [START_REF] Nair | Musclespecific effect of aging on beef tenderness[END_REF]. In fact, Tenderstretch could effectively shorten aging time and improve beef tenderness by up to 40% [START_REF] Nian | Effect of castration and carcass suspension method on the quality and fatty acid profile of beef from male dairy cattle[END_REF], and indeed performs better on improving beef sensory quality (flavor, juiciness and overall liking) than that of Achilles tendon [START_REF] Moran | Effect of pelvic suspension and post-mortem ageing on the quality of three muscles from Holstein Friesian bulls and steers[END_REF]. In black: major/direct factor of variation; in grey: weaker/indirect factor of variation; in white: not a fac-tor of variation. This analyses are based on the studies of Clinquart et al. and Preche et al. (Clinquart et al., 2022;Prache et al., 2021) and expertise of authors.

Main methods for measuring and predicting beef quality

In order to ensure in advance a good quality of beef at consumer level, a relevant method is to predict beef quality in scenarios where the beef carcass and muscle cuts can be measured and evaluated by combining these different quality traits (for instance at farm level, at abattoir level or by some specific approaches such as consumer sensory testing). Perceived quality, particularly at the time of tasting, depends on a combination of parameters that have been largely evaluated by consumers or more generally by human panels in recent decades of research. In fact, many traits are initially and still largely measured by objective methods [START_REF] Dikeman | Sensory and Meat Quality, Optimization of[END_REF]. In the beef sector, there are mainly three categories for beef quality measurement/prediction: (1) instrumental methods (intrusive mechanical measurement and non-destructive instrumental measurement), ( 2) the omics approach and (3) the carcass/cut grading scheme [START_REF] Farmer | Review: Beef-eating quality: A European journey[END_REF].

Mechanical measurement of beef quality

Physical texture measurement

Evaluation of beef quality is complicated, especially with respect to sensory quality, which in reality can only be measured by consumers or sensory panels [START_REF] Purchas | Tenderness measurement[END_REF]. However, since consumer evaluation is time-consuming and costly. it cannot be widely used for all quality measurements. A widely-used method of evaluating meat quality is to measure the physical texture of meat products. The physical texture of beef is mainly related to mechanical attributes, which are generally characterized by hardness, cohesiveness, viscosity, springiness and adhesiveness [START_REF] Hocquette | Sensory Assessment of Meat[END_REF]. Mechanical measurements of the strength required to break down the meat are mainly categorized as shearing, biting, compressing a standardized piece of meat. The most commonly used measurement for meat toughness/tenderness is WBSF. The Slice shear force (SSF) is a faster alternative to WBSF but is less used [START_REF] Derington | Relationships of slice shear force and Warner-Bratzler shear force of beef strip loin steaks as related to the tenderness gradient of the strip loin[END_REF]. For overall physical texture, there is the texture profile analysis (TPA) and some devices are used such as the MIRINZ tenderometer with a biting action for measuring overall tenderness of meat [START_REF] Purchas | Tenderness measurement[END_REF]. The WBSF was found to be more effective in classifying beef as tender (68% accuracy) than the SSF (47%), compared to consumer perceived sensory tenderness (80%) [START_REF] Battaglia | Comparison of methods for measuring shear force and sarcomere length and their relationship with sensorial tenderness of longissimus muscle in beef[END_REF]. Many studies have tried to relate the meat physical texture measurement to consumerrated tenderness/mouth-feel-taste, with physical measurements being able to explain a variable variation in tenderness assessed by human panels but no more than 60% [START_REF] Purchas | Tenderness measurement[END_REF]. For instance, Platter et al., (2003) found that WBSF can only explain 23% of the total variance of consumer scored tenderness. Various correlations between WBSF and consumer evaluated tenderness have been observed, ranging from low (e.g. r=-0.19, -0.26) (Rodas-González et al., 2009) to high values (e.g. r=-0.72, -0.82) (Liang et al., 2016). Different factors such as aging process, cooking temperature [START_REF] Sasaki | Characterization and classification of Japanese consumer perceptions for beef tenderness using descriptive texture characteristics assessed by a trained sensory panel[END_REF] and muscle cuts [START_REF] Shackelford | Relationship between shear force and trained sensory panel tenderness ratings of 10 major muscles from Bos indicus and Bos taurus cattle[END_REF] might contribute to these inconsistencies. Except for the above, the lack of strong correlations between physical shear force and consumer perceived tenderness indicates that they seem to be two non-equivalent issues, the latter being not only related to mechanical force but also associated with sensations generated by moisture and fat within the meat.

Juiciness measurement

According to a National Beef Tenderness Survey conducted in the United States at the food service and retail level, over 94% of rib and loin beef were rated tender or very tender. Such a large proportion of tender beef has magnified the importance of juiciness and flavor to the consumer eating experience [START_REF] Mckillip | Repeatability and Accuracy of the Pressed Juice Percentage Method at Sorting Steaks into Juiciness Categories[END_REF]. This is the reason why the importance of beef sensory traits has renewed attention from meat scientists in recent years. For many years, tenderness was considered as the dominant factor in determining eating quality and with the clarification of a higher contribution of flavor liking to overall consumer satisfaction, the importance of juiciness should not be neglected (Liu et al., 2020a). The measurement of juiciness has previously focused on total water content, WHC and water fractions of meat, although the consistency between sensory juiciness and these parameters varies [START_REF] Pearce | Water distribution and mobility in meat during the conversion of muscle to meat and ageing and the impacts on fresh meat quality attributes-A review[END_REF]. One of the reasons could be that the meat evaluated by consumer or panels has been cooked, which means that with physical/chemical alterations and intra-and extra-myofibrillar water movements, the perception of juiciness may be altered. Cooking loss, drip loss, and compressionbased methods have been usually used to quantify expressible moisture in meat. Cooking loss has been reported to be able to explain 60-80% of the juiciness variance [START_REF] Bouton | Objective assessment of meat juiciness[END_REF], but it has also been reported that cooking loss cannot explain the juiciness of cooked meat due to heat-induced changes [START_REF] Pearce | Water distribution and mobility in meat during the conversion of muscle to meat and ageing and the impacts on fresh meat quality attributes-A review[END_REF][START_REF] Young | Critical control points for meat quality in the Australian sheep meat supply chain[END_REF]. Compression-based methods have evolved from filter paper press methods from the Carver hydraulic press apparatus, the Instron-based press method to the pressed juice percentage (PJP) method with various capabilities to predict juiciness scored by a sensory panel [START_REF] Lucherk | Assessment of objective measures of beef steak juiciness and their relationships to sensory panel juiciness ratings1,2[END_REF]. PJP was observed to be strongly correlated with sensory juiciness scored by trained and untrained consumers (r=0. 69, 0.45), respectively. IMF content can be a good indicator of juiciness. Thompson (2004) found that consumers were satisfied and dissatisfied with beef juiciness when IMF was above 20% or below 2% respectively. However, it is difficult to define a threshold of juiciness for consumer perception based on IMF content due to the different distribution of IMF (Holman & Hopkins, 2021).

Flavor measurement

Flavor is perceived by consumers through two pathways, namely odor detected by the nose and taste perceived by the mouth and tongue. There are receptors on the olfactory bulb in the nose and mouth that detect volatile compounds, when they come into contact with the olfactory bulb and are recognized by these receptors, flavor is thus perceived. In addition to these volatile compounds, there are volatile aromatic compounds generated in mouth during chewing or swallowing of meat. However, the amount or types of receptors and the amount or concentration of volatile compounds needed for perception vary between individuals (Miller, 2017). The perception of flavor is therefore complicated to define due to the individual diversity of the taster. This is the reason why meat flavor is further described and assessed by highly trained descriptive attributes with panelists, which are the most accurate methods for measuring meat flavor. Mechanical measurement of flavor on the basis of consumer perception is challenging due to the complexity of the meat matrix and consumer perception. In recent decades, significant progress has been made in identifying and quantifying meat flavor compounds [START_REF] Resconi | Gas Chromatographic-Olfactometric Aroma Profile and Quantitative Analysis of Volatile Carbonyls of Grilled Beef from Different Finishing Feed Systems[END_REF]. Thousands of volatile compounds have been identified as constituting the aromas of meat odor/flavor using mechanical and/or chemical measurements such as olfactometry, flame ionization detection (FID) and thiobarbituric acid reactive substances (TBARS). TBARS have been shown to have a predictive ability for the consumer's flavor liking threshold, but this is highly dependent on the method used for TBARS determination. Fatty acid profile can contribute to consumer flavor liking, as CLA, SFAs and MUFAs have been associated with flavor liking, although some effects are muscle dependent (Listrat et al., 2020). Additionally, the electronic nose (e-nose) and electronic tongue (e-tongue) are also useful tools for evaluating meat quality attributes (Tan & Xu, 2020).

Non-destructive instrumental methods for beef quality prediction

There has been a demand to predict beef quality by non-destructive instrumental methods which are considered as having many clear-cut advantages, such as ease of use, non-destructiveness, fast, cost-effectiveness, reproducibility and a high potential accuracy (Hocquette et al., 2012). Ongoing work with various emerging technologies has been conducted with the aim of predicting beef quality directly or indirectly, i.e. predicting consumer sensory attributes directly related to quality, such as tenderness or flavor, or predicting indirect quality-related parameters that have been shown to have an impact on meat quality such as meat color, pH, IMF content or marbling [START_REF] Farmer | Review: Beef-eating quality: A European journey[END_REF].

Near-InfraRed Spectroscopy (NIRS)

NIRS is regarded as one of the most effective methods for predicting the quality attributes of meat [START_REF] Prieto | Application of near infrared reflectance spectroscopy to predict meat and meat products quality: A review[END_REF]. Recent research with respect to the eating quality of beef has focused on the estimation and prediction of meat chemical composition, meat color, pH and sensory tenderness and other sensory attributes. NIRS has the ability to predict the chemical composition, technological parameter and sensory feature of meat [START_REF] Ripoll | Near-infrared reflectance spectroscopy for predicting chemical, instrumental and sensory quality of beef[END_REF]. The overall protein content, water content and fatty acid content can be predicted with a good accuracy (coefficients of determination R 2 in the range of 0.6-0.8, over 0.9, over 0.9, respectively) [START_REF] Andueza | Phenotyping of Animals and Their Meat: Applications of Low-Power Ultrasounds, Near-Infrared Spectroscopy, Raman Spectroscopy, and Hyperspectral Imaging[END_REF][START_REF] De Marchi | Use of near infrared spectroscopy for assessment of beef quality traits[END_REF]. Although NIRS has been shown to be a useful tool to predict FA profile and content in a fast, cheap and non-invasive way, some weak points are still observed such as not all minor FA contents can be accurately determined, which is partly due to the high water content disturbing the solutes absorb infrared light [START_REF] Andueza | Prediction of beef meat fatty acid composition by visible-near-infrared spectroscopy was improved by preliminary freeze-drying[END_REF]. Freeze-dried beef was therefore used in a study and it was observed that the capacity of NIRS to predict FA profile without water content is improved, it ultimately proved that NIRS is an effective tool for screening purposes with the determination of most of the FA particularly for freeze-dried beef [START_REF] Ripoll | Near-infrared reflectance spectroscopy for predicting the phospholipid fraction and the total fatty acid composition of freeze-dried beef[END_REF]. NIRS is capable of measuring color variables (L*, a* and b*) of meat and meat product with a variable accuracy (R 2 ranging between 0.45 and 0.90), due to different conditions for instance the predictive accuracy is different between intact meat and processed meat [START_REF] Andrés | The use of visible and near infrared reflectance spectroscopy to predict beef M. longissimus thoracis et lumborum quality attributes[END_REF][START_REF] Andueza | Phenotyping of Animals and Their Meat: Applications of Low-Power Ultrasounds, Near-Infrared Spectroscopy, Raman Spectroscopy, and Hyperspectral Imaging[END_REF]. In addition, NIRS can correctly detect 80-95% dark cutting beef depending on the instrument used and the degree of oxygenation [START_REF] Farmer | Review: Beef-eating quality: A European journey[END_REF]. Another application of NIRS is the non-invasive pH measurement of beef muscle, which is considered as a remarkable asset to measure pH without destroying the integrity of muscle. Andres et al. [START_REF] Andrés | The use of visible and near infrared reflectance spectroscopy to predict beef M. longissimus thoracis et lumborum quality attributes[END_REF] reported that by using NIRS, the 24 hours pH of Muscle Longissimus Thoracis was successfully measured with a very high accuracy (R 2 = 0.97). Several studies also suggested that the sensory quality of meat might be accurately predictable by NIRS but with a relatively poor accuracy (R 2 ranging between 0.10-0.58) [START_REF] Liu | Prediction of color, texture, and sensory characteristics of beef steaks by visible and near infrared reflectance spectroscopy. A feasibility study[END_REF][START_REF] Prieto | Application of near infrared reflectance spectroscopy to predict meat and meat products quality: A review[END_REF], although [START_REF] Ripoll | Near-infrared reflectance spectroscopy for predicting chemical, instrumental and sensory quality of beef[END_REF] reported that the beef tenderness can be predicted by NIRS with a high accuracy (R 2 =0.98). Moreover, weak correlations were observed for tenderness between NIRS prediction and sensory evaluation and Warner-Bratzler shear force (WBSF) method [START_REF] De Marchi | The relevance of different near infrared technologies and sample treatments for predicting meat quality traits in commercial beef cuts[END_REF][START_REF] Prieto | Ability of near infrared reflectance spectroscopy (NIRS) to estimate physical parameters of adult steers (oxen) and young cattle meat samples[END_REF]. Therefore, NIRS is likely not to be relevant and reliable yet to predict meat sensory quality at the current stage [START_REF] Andueza | Phenotyping of Animals and Their Meat: Applications of Low-Power Ultrasounds, Near-Infrared Spectroscopy, Raman Spectroscopy, and Hyperspectral Imaging[END_REF].

Raman spectroscopy

Raman spectroscopy (RS) is a non-invasive technique based on inelastic dispersion of light at a molecular level. Raman spectra can yield molecular structural, chemical composition and qualitative information of a substance. Within the meat sector, RS has been applied on the measurement of shear force, tenderness and juiciness [START_REF] Fowler | Preliminary investigation of the use of Raman spectroscopy to predict meat and eating quality traits of beef loins[END_REF], pH and meat color, drip loss and IMF and major fatty acid with a medium accuracy [START_REF] Cama-Moncunill | Prediction of Warner-Bratzler shear force, intramuscular fat, drip-loss and cook-loss in beef via Raman spectroscopy and chemometrics[END_REF]. The well-known advantages of RS are related to the non-invasive, fast and portable handheld process, another advantage especially compared with NIRS is that water does not disturb the light absorption of other matters during RS measurement [START_REF] Andueza | Phenotyping of Animals and Their Meat: Applications of Low-Power Ultrasounds, Near-Infrared Spectroscopy, Raman Spectroscopy, and Hyperspectral Imaging[END_REF]. In addition, due to the heterogeneity and complexity of the meat matrix, the evaluation of calibration and validation models are necessary to indicate the real performance of handheld RS tools [START_REF] Beganovic | Critical Review on the Utilization of Handheld and Portable Raman Spectrometry in Meat Science[END_REF]. This is also essential for other spectroscopy technologies.

Computer vision techniques

Computer vision (CV) techniques have been utilized to visually assess meat quality in the processing line as being non-invasive and consistent to assess color, IMF and eating quality most importantly [START_REF] Taheri-Garavand | Meat quality evaluation based on computer vision technique: A review[END_REF]. It was reported that CV has the ability to assess marbling and predict quality attributes with R 2 values of tenderness (0.72), WBSF (0.83), juiciness (0.60), flavor (0.78) and overall acceptability (0.82), respectively. Furthermore, the prediction of meat quality at carcass base is also feasible based on computer vision assessment with 90% accuracy [START_REF] Jackman | Prediction of beef eating qualities from colour, marbling and wavelet surface texture features using homogenous carcass treatment[END_REF]. In addition, CV can also be used to classify meat color and fat color with a good performance such as the 97.4% of the classification accuracy in fat color estimation

Hyperspectral imaging technique

Hyperspectral imaging (HSI) is a promising technique for the objective assessment of meat quality attributes such as color, tenderness, marbling, maturity and texture, through a integrate system of spectroscopy and imaging techniques. Several studies demonstrated that hyperspectral imaging technique could predict meat tenderness and WBSF quite well with R 2 values ranging around 0.9. Through some appropriate statistical methods such as discriminant analysis, the classification of tenderness between tender and tough meat can reach the accuracy of 75% to 96% [START_REF] Cluff | Optical scattering with hyperspectral imaging to classify longissimus dorsi muscle based on beef tenderness using multivariate modeling[END_REF][START_REF] Naganathan | A prototype on-line AOTF hyperspectral image acquisition system for tenderness assessment of beef carcasses[END_REF]. Besides, hyperspectral imaging technique is used to measure water and fat content of meat with R 2 value of 0.89 and 0.84. It is well known that water and fat content determine to a large extent the juiciness and flavor of meat and thereby the eating quality. Therefore, the relative studies can be conducted to examine the predictive ability of hyperspectral imaging for other meat quality attributes such as juiciness, flavor and overall acceptability in the future. In addition, another application of hyperspectral imaging technique is the measurement of color. It was reported that the Hunter L, a* and b* can be correctly assessed with R 2 value of 0.92, 0.90 and 0.88 [START_REF] Wu | Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review-Part I: Fundamentals[END_REF].

Multispectral imaging technology

Multispectral imaging (MSI) can capture image data within specific wavelength ranges widely across the electromagnetic spectrum from Ultra-Violet to Near InfraRed, which enables MSI to provide more information [START_REF] Bandara | A multispectral imaging system to assess meat quality[END_REF]. By contrast with other spectrum technologies, MSI could provide both spectral and spatial properties of a region in samples ranging in different size. In addition, despite the short number of wavelengths, MSI can record data at different positions in the electromagnetic spectrum, allowing for the collection of very detailed information regarding sample chemical properties [START_REF] Aït-Kaddour | Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis[END_REF][START_REF] Jacquot | Potential of Multispectral Imager to Characterize Anisotropic French PDO Cheeses: A Feasibility Study[END_REF].

Good performances of MSI have been observed to authenticate and discriminant beef with different muscle types and breeds [START_REF] Aït-Kaddour | Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis[END_REF][START_REF] Kulmyrzaev | Potential of a custom-designed fluorescence imager combined with multivariate statistics for the study of chemical and mechanical characteristics of beef meat[END_REF]. Therefore, MSI has a promising potential to predict IMF content, intramuscular connetive tissue structural features and collagen content with a good accuracy as well as sensory quality such as tenderness [START_REF] Aït-Kaddour | Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis[END_REF]. All of those studies indicated that MIS is a promising technology to provide prediction of physical and chemical features of bovine muscles with the advantage of fast measurement, which is more suitable for industrial production lines, especially compared with other technologies such as hyperspectral imaging [START_REF] Ma | Feasibility of combining spectra with texture data of multispectral imaging to predict heme and non-heme iron contents in pork sausages[END_REF].

Ultrasound

Ultrasound is another technique applied for the measurement of meat composition with mainly respect to IMF content and fat depth. Aass and Fristedet et al. reported that ultrasound can be used to measure IMF in lean cattle [START_REF] Aass | Ultrasound prediction of intramuscular fat content in lean cattle[END_REF]. [START_REF] Nunes | Beef quality parameters estimation using ultrasound and color images[END_REF] found that the performance of ultrasound was satisfactory for the prediction of IMF only with the middle level of fat percentages (3-5%) [START_REF] Nunes | Beef quality parameters estimation using ultrasound and color images[END_REF]. It was found that ultrasound is useful to predict IMF and kidney fat content (R 2 =0.62, 0.72, respectively) in young bulls [START_REF] Beriain | Effectiveness of using ultrasound readings to predict carcass traits and sensory quality in young bulls[END_REF]. Furthermore, ultrasound is also useful for the prediction of fatty flavor (R 2 = 0.56) but not applicable for other sensory traits [START_REF] Beriain | Effectiveness of using ultrasound readings to predict carcass traits and sensory quality in young bulls[END_REF]. Besides, this technology can also be used to measure backfat thickness but with a poor accuracy (R 2 = 0.43) [START_REF] Roehe | Research towards an Integrated Measurement of Meat Eating Quality[END_REF].

Computerized tomography imaging

Computerized tomography (CT) imaging has shown a good potential to assess muscle, fat and bone content of living animal through real time scanning [START_REF] Nagdalian | Analysis of the content of mechanically separated poultry meat in sausage using computing microtomography[END_REF]. It was reported that a reliable prediction for subcutaneous fat, IMF and total fat content can be achieved by CT imaging with R 2 values of 0.92, 0.86 and 0.93, respectively [START_REF] Prieto | Predicting beef cuts composition, fatty acids and meat quality characteristics by spiral computed tomography[END_REF]. However, the ability to further predict sensory attributes of beef is very poor with the determinant coefficients lower than 0.30 [START_REF] Farmer | Review: Beef-eating quality: A European journey[END_REF][START_REF] Prieto | Predicting beef cuts composition, fatty acids and meat quality characteristics by spiral computed tomography[END_REF].

Omics approaches

Genomics

The criteria for defining consumer beef eating quality are based on several traits (e.g. tenderness, juiciness and flavor, etc.), which are quantitative traits determined by sets of components regulated by the joint action of numerous genes and environmental regulations (growth, rearing and processing factors) [START_REF] Picard | Gene and protein expression as a tool to explain/predict meat (and fish) quality. New aspects of meat quality[END_REF]. Each individual component contributing to the palatability phenotype is consequently difficult to control and costly to measure. All beef eating quality traits are difficult to improve based merely upon phenotypic selection, but there may be effective candidate genes for genomic selection if genetic markers that account for a significant variance for those quality traits are identified [START_REF] Leal-Gutiérrez | Genetic basis of improving the palatability of beef cattle: current insights[END_REF]. Within the meat sector, numerous genes have been identified as being involved in valuable estimates of genetic parameters. They provide key insights into the regions that underpin variation in physical meat characteristics, including muscle fibers, connective tissue, IMF, meat color, fat color, shear force and sensory meat quality traits such as tenderness, juiciness, flavor, chewiness, etc. [START_REF] Leal-Gutiérrez | Whole Genome Sequence Data Provides Novel Insights Into the Genetic Architecture of Meat Quality Traits in Beef[END_REF]. So far, 19 sensory-related traits including tenderness and color have been confirmed with notable representations of related biomarkers on chromosomes [START_REF] Gagaoua | Current research and emerging tools to improve fresh red meat quality[END_REF]. Despite its relevant potential to predict meat quality variation, some limitations have still been noted, the most common being that, thanks to numerous association studies, predictive information can be obtained but not deep scientific knowledge of the underlying mechanisms, at least in the earliest stages of omics development. Moreover, predictive reliability appears to be less consistent, in particular with human evaluated meat eating quality. For instance, recent heritability estimates for tenderness, juiciness and flavor scores range from 0.1 to 0.2 (Berry et al., 2021). This indicates that the proportion of variability in beef eating quality explained by genetic factors is moderate to weak.

Proteomics

An emerging body of literature has examined the proteomic pathways involved in meat eating quality variations [START_REF] Gagaoua | Dark-cutting beef: A brief review and an integromics meta-analysis at the proteome level to decipher the underlying pathways[END_REF]. All these works also contribute to the elucidation of the biological mechanisms involved in muscle to meat conversion and in meat qualities [START_REF] Yang | Application of proteomics to understand the molecular mechanisms determining meat quality of beef muscles during postmortem aging[END_REF]. Despite the many factors regulating beef eating quality, and therefore the large number of biomarkers involved in the regulation of quality by these factors, with more and more results from proteomic studies, robust candidate biomarkers can still be identified due to their consistent associations with meat qualities. For example, [START_REF] Gagaoua | The associations between proteomic biomarkers and beef tenderness depend on the end-point cooking temperature, the country origin of the panelists and breed[END_REF] found some biomarkers that related to muscle structure (MyHC-I, MyHC-IIa, MyHC-IIx), oxidative stress (DJ-1, PRDX6) and proteolysis (CAPN1) that were consistently associated with tenderization of longissimus thoracis muscle. Despite various results depending on animal breeds (Aberdeen Angus, Limousin and Blond d'Aquitaine), end-point cooking temperature of beef (55 or 74°C), and consumer origin (France and UK), some of these biomarkers performed as robust predictors for tenderness [START_REF] Gagaoua | The associations between proteomic biomarkers and beef tenderness depend on the end-point cooking temperature, the country origin of the panelists and breed[END_REF]. Protein network research has revealed the functional annotation of 124 proteins in longissimus dorsi muscle, which are crucial in the production of high-quality beef [START_REF] Gagaoua | Current research and emerging tools to improve fresh red meat quality[END_REF]. More and more integrated proteomics studies have been carried out to create a repertoire of biomarkers, especially for beef quality defects (i.e. dark, firm and dry beef). The ultimate goal of these biomarkers is to guarantee the eating quality for consumers by proposing a list of validated biomarkers for the development of routine bioanalytical tools to be used by breeders and producers to improve the potential merits of breeds and to detect potential quality during the pre-and postmortem periods [START_REF] Gagaoua | Current research and emerging tools to improve fresh red meat quality[END_REF].

Metabolomics

Skeletal muscle is characterized by a set of functionally cooperative genes designed to address the spatiotemporal requirements of each muscle. Gene expression is then regulated, including protein modification, during muscle development, growth and maturation. In the later stages, muscle metabolites determine the muscle characteristics, which are the major phenotypic components of meat eating quality. During the development and physiological specialization of muscle, many well-known factors all impact on the genome, transcriptome and proteome profiles of muscle, making it very difficult to understand the precise mechanisms behind meat quality variations through these molecular markers [START_REF] Muroya | MEATabolomics: Muscle and meat metabolomics in domestic animals[END_REF]. Nonetheless, changes in muscle metabolome profiles [small hydrophilic molecules/metabolites such as polyphenols, organic acids (carnitine, creatine and carnosine), amino acids, vitamins and minerals, etc.] can be quantified by metabolomics as potential indicators reflecting the metabolic process and screened to predict sensory quality [START_REF] Jeong | Metabolomics Analysis of the Beef Samples with Different Meat Qualities and Tastes[END_REF]. For example, Ma et al. (2017) reported that an increase in the amount of free amino acids was associated with the degree of proteolysis, which suggests more tender meat, but also with more precursors of aromatic compounds that play a role in the sensory aspects of cooked meat. Furthermore, Antonelo et al. (2020) found a positive correlation between carnitine and consumer acceptance of beef steaks, while strong negative correlations were observed between carnitine and creatine and consumer sensory scores for tenderness, juiciness and overall liking.

Grading schemes for beef eating quality

With the advancement of international trade of beef carcasses, carcass classification standards and beef quality grading schemes are required to provide a description of carcasses and muscle cuts with the definition of quality to purchasers and destination markets (Bonny et al., 2018). Based on this objective, two categories of grading schemes based on carcass and muscle cut, have been used to classify carcass grade and predict beef quality. This section will introduce the main beef grading systems that are relatively well developed around the world, the other grading systems developed by local beef organizations also listed in Table 2.

Carcass-based grading schemes of beef eating quality

1. The USDA grading system Since early 1900s, carcass classification in the United States was implemented and beef carcass would be assigned into groups with uniform quality, according to the United States Department of Agriculture (USDA) Beef Quality grading system specifications [START_REF] Leal-Gutiérrez | Genetic basis of improving the palatability of beef cattle: current insights[END_REF]. Although the need to assess beef eating quality was recognized with the implementation of the USDA grading system 100 years ago, the eating quality assessment was not originally involved in the grading system (Kerth & Miller, 2015;Polkinghorne & Thompson, 2010). Insofar, the USDA grading system encompasses two categories of classification, which are the gradings for yield and quality. The yield grading is an indication of cutability or yield of boneless, trimmed retail cuts. The yield grade of a carcass is determined by external fat, kidney, heart and pelvic fat, ribeye area and hot carcass weight. The quality grading system uses carcass maturity and IMF (marbling) to segment beef carcasses into 8 quality classes [top quality: Prime (high marbling and youthful), Choice, Select, Standard (progressively less marbling); more mature category: Commercial (high marbling), Utility, Cutter and Canner (progressively less marbling)]. Graders evaluate the amount and distribution of marbling on Musculus longissimus dorsi between the 12th and 13th ribs. Degree of marbling is the primary determination of quality grade and roughly includes seven degrees (slightly abundant, moderate, modes, small, slight, traces and practically devoid). Maturity is the physiological age of the animal rather than the chronological age. Carcass maturity is assessed based primarily on the cartilage ossification of lumbar and sacral vertebral and secondly on the rib bone characteristics, color and texture of ribeye muscle. There are five levels of maturity and proceed from A to E (USDA, 1997). Carcasses could be allocated into a category according to the permutations of marbling level and maturity. For instance, when a carcass is categorized as Choice, the marbling level could be "Moderate" with a less youthful maturity level of B, it can also be a combination of lower level of marbling "Small" with the most youthful maturity level of A. In the USDA system, only Prime, Choice, Select, Standard are sold directly to consumers, these four grades can predict well the flavor and juiciness of all major cuts, whilst best indicate tenderness of the middle part of meat especially rib and lion but not for end meats (chuck and round) (Smith et al., 2008). In fact, as early as the middle 1900s, the studies about the consumer preference for beef eating quality based on USDA quality grade were conducted. However, far less relevant connection was obtained for consumer preference among USDA Grades (Smith et al., 2008). Recent studies also indicated that American consumers were unable to detect differences on eating quality among different USDA grades with tenderloin steaks [START_REF] O'quinn | Consumer assessment of beef tenderloin steaks from various USDA quality grades at 3 degrees of doneness[END_REF]. In addition, it has been observed that USDA maturity grade has no impact on eating quality for grain finished cattle up to 30 months and that only marbling grade is important to eating quality [START_REF] Acheson | Effects of USDA carcass maturity on sensory attributes of beef produced by grain-finished steers and heifers classified as less than 30 months old using dentition[END_REF].

Meat and Livestock Commission (MLC) Blueprints

In 1990, the Meat and Livestock Commission initiated a national specification (Blueprints) aimed at developing guidelines and recommendations for better beef quality. The Blueprints are drawn up from the accumulated knowledge based on a thorough review of the scientific literature and commercial knowledge and keep updating with new results (Meat and Livestock Commission, 1991). This specification is applied to define and increase beef quality in the British beef industry, the specific supply chains and certain brands with specific adding values. It is aimed primarily at improving meat tenderness, but the relative recommendations to address other attributes are also included. For overall quality optimizing, the basic factors used in the system includes genetics, sex, age, growth rate, diet, EUROP grid conformation and fat class. In addition, due to the significant effects of post-slaughter factors on beef quality, the Blueprints also focus highly on post-slaughter factors, such as slaughter techniques, hanging method, electrical stimulation, chilling rate, pH/Temperature decline, etc. [START_REF] Casabianca | Qualification of the origin of beef meat in Europe Analysis of socio-technical determinants based on French practices[END_REF][START_REF] Farmer | Review: Beef-eating quality: A European journey[END_REF].

The JMGA grading system

In order to use generic terminology to describe carcass produced and sold in different regions for price comparison and market reporting, Japanese meat grading association (JMGA) was established in 1976, to administrate the grading of meat quality and yield within the national carcass evaluation system (Polkinghorne & Thompson, 2010).

There are five quality grades (1)(2)(3)(4)(5) within JMGA system, the carcass grading at rib site between the 5 th and the 6 th ribs, and including the measurement of marbling, meat colour, brightness, texture; fat colour, lustre, texture, firmness, thickness, eye muscle area and rib thickness. Marbling is a critical parameter in the Japanese quality grading system. The beef marbling standards (BMS) ranging from 1 to 12 in increments of one. Based on the BMS, there are five grades for marbling classification: excellent , good (BMS 5-7), average (BMS 3-4), below average (BMS 2) and poor (BMS 1) [START_REF] Strong | Differences in carcass grading schemes used in the USA, Japan and Australia[END_REF]. The JMGA yield grade refers to the proportion of meat produced by animal that can be eaten and is determined by eye muscle area, rib thickness, cold left carcass weight and subcutaneous fat thickness through a regression calculation. The yield grading is conducted at rib site between the 6 th and 7 th ribs. Yield grades are segmented as A (72% and above -cut yield above average), B (69-72% and average yield) or C (under 69% and below average range). The yield grade (A to C) and quality grade (1 to 5) are then combined in the carcass stamp as an A1 or B3 to represent the quality of a carcass (Polkinghorne & Thompson, 2010).

Cut-based grading scheme -the MSA grading system

The innovation of MSA

Unlike the aforementioned carcass grading schemes, the MSA is a beef quality grading system, aimed at delivering an eating quality guarantee to consumers. There are two points that the MSA system being distinct from the other grading schemes: (1) the grading of beef quality is based on each MSA muscle cut, rather than the whole carcass; (2) the definition of eating quality depends on responses of untrained consumers (Bonny et al., 2018). Beef consumption decline have raised concerns since the 1970s [START_REF] Polkinghorne | Evolution of the meat standards Australia MSA beef grading system[END_REF], inconsistent palatability is regarded as the primary reason: the industry failed to guarantee the eating quality of beef and consumers were unable to select beef products with consistent palatability align with an appropriate price [START_REF] Bindon | Cattle supply, production systems and markets for Australian beef[END_REF]. Under such a background, the MSA system was initiated to predict beef eating quality for consumers by performing eating quality and consumer taste testing research. In MSA, a Total Quality Management System approach is used to grading carcass, incorporating both pre-and post-slaughter characteristics of beef carcasses under the measurements within mandatory criteria in the chiller (Bonny et al., 2018;[START_REF] Polkinghorne | Overview of Meat Standards Australia (MSA) and cuts based grading schemes[END_REF].

Importance of consumer evaluation

Consumer evaluation of sensory quality was not widely applied in scientific research prior to the 1980s due to the unrepeatable results (Miller, 2017). As a matter of fact, to guarantee and deliver consistent eating quality, the eating quality should be evaluated and shown to be consistent across the consumer base, who is the end user of the supply chain [START_REF] Casabianca | Qualification of the origin of beef meat in Europe Analysis of socio-technical determinants based on French practices[END_REF]. Numbers of studies have shown that untrained consumer evaluation has the capacity to reflect the actual consumer performance with a great accuracy when tasting samples with a wide range of quality variance (Hwang et al., 2008). Moreover, consumer sensory evaluation has been evolved as a pivotal tool in meat quality research since it highly relevant to relate beef palatability with actual consumer experience and preference (Miller, 2017). Therefore, in the MSA system, the ultimate determination of beef eating quality depends on consumers, furthermore, on the target consumer market (Watson et al., 2008).

Consumer testing protocol

The consumer testing conducted in MSA according to the protocol outlined by Watson et al. (Watson et al., 2008). Each muscle cut is sectioned into 10 portion of samples and each sample is tasted by one different consumer. One consumer tasted seven samples: the first "link" sample was from striploin or rump muscle in order to guarantee an average eating quality level, the remaining six samples are each from one different muscle cut and served to consumer in accordance with a 6 × 6 Latin square, expected to deliver a large diversity of eating quality to each consumer and to balance out potential halo or serving order effects. Consumer evaluation of beef palatability traits were marked on 100-mm line scales (0 = low intensity/liking; 100 = high intensity/liking) for four sensory attributes: tenderness, juiciness, flavor liking, and overall liking, and after that consumers are asked to rate the tasted sample as unsatisfactory (2star), good everyday (3star), better than everyday (4star) or premium (5star) (Watson et al., 2008).

Meat quality score and quality grade

In the MSA system, the potential eating quality of muscle cuts and carcasses can be calculated and predicted. The evaluated scores of tenderness, flavor liking, juiciness and overall liking during consumer testing are weighted currently as 0.3, 0.3, 0.1 and 0.3 (0.4, 0.2, 0.1 and 0.3 previously) by linear discriminant analysis and after combined into a single composited eating quality score, namely meat quality (MQ4) score. The MQ4 score determined by consumer evaluation reflects the sum of the consumer eating experience on one muscle cut (Watson et al., 2008). In addition, the eating quality of a whole carcass, termed MSA Index, can also be predicted in MSA. The MSA index is the sum of the weighted MQ4 scores of all MSA cuts (39 muscle cuts), the weighting of each cut was calculated as the percentage of the total weight of the MSA cuts in the carcass. The MSA Index is used for producer feedback systems to value the potential eating quality of beef carcasses and enable producers to monitor the impact of genetical and breeding practice and management on the eating quality of each carcass (McGilchrist et al., 2019). Furthermore, beef samples are graded as being unsatisfactory (2star), good every day (3star), better than every day (4star) and premium quality (5star) by untrained consumers. These categories could be corresponded with MQ4 score and this connection enables the allocation of muscle cuts into four quality grades. Through discriminant analysis, the calculated MQ4 scores could generate the boundaries between each of the quality grades. The current boundaries are 45.5 for the ungraded/2star and 3star categories, 63.5 for the 3-and 4star categories, and 76.5 for the 4-and 5star categories (Watson et al., 2008).

The prediction of meat quality

Along with the definition and description of eating quality at the consumer level, the prediction of beef quality is of critical importance at the consumer end. At early stage of the MSA, it was found that the carcass grading by using only carcass parameters cannot predict the eating quality of one carcass from a variety of production systems. Besides, different muscle cuts have different eating quality within different carcasses and also vary as a result of multiple factors (Bonny et al., 2016). As such, the Critical Control Points (CCPs) have been used in the MSA prediction model from the breeding, production, pre-slaughter, processing and value-adding aspects of the supply chain that impact on eating quality and consumer preference through the large-scale consumer testing (Bonny et al., 2018). The parameters used in the prediction model based on inputs aspects including: animal type and production (Bos indicus content, hormone growth promotants, milk fed vealer classification and sex, sale yard and selling method), carcass characteristics (carcass weight, ossification score, hump height, USDA marbling score, rib fat depth and ultimate pH), post-slaughter factors (hanging method and aging time), the predictive factors of outputs compass predicted grade by MSA muscle cuts and cooking method. Finally, the multiple regression function using the above inputs were then used to develop prediction model and further predict the MQ4 score for specific muscle cuts (39 cuts in total) over a combination of a defined ageing period and one of eight different cooking methods. In addition to the factors used in the prediction model, meat color, fat color and loin temperature of carcass are also important during the postmortem measurement to exclude carcasses that undergone dark cutting and heat-and/or cold-shortening (Watson et al., 2008).

The International Meat Research 3G Foundation

As discussed in the previous section, there are various grading schemes to evaluate beef quality with different standards. In addition, different rearing and feeding systems, environmental conditions, animal type and breeds, processing practices would add variability for the quality evaluation across countries. So as to the scientific research on meat quality evaluation on one hand or to the further industrial applications (e.g. international beef trading and marketing) on the other hand, it is necessary to develop and/or share a set of generic principles and/or establish an international database with a significant number of assessments on beef quality traits from different countries (Hocquette et al., 2020). The MSA protocol has always been considered as a good standard with critical steps including rigorous beef carcass assessment and untrained consumer evaluation of beef palatability. The MSA protocol developed for use in scientific research on sensory evaluation and consumer tests have been widely conducted in many countries since 2002 such as in Ireland [START_REF] Brandon | Testing the meat standards Australia (MSA) model on Irish beef[END_REF], the United States [START_REF] Polkinghorne | Targeting the consumer demand for beef in Australia, Japan, Korea, Ireland and the United States[END_REF], South Korea [START_REF] Thompson | Managing meat tenderness[END_REF], Northern Ireland [START_REF] Farmer | Adaptation of Meat Standards Australia Quality System for Northern Irish Beef[END_REF], Japan (Polkinghorne et al., 2011), France (Legrand et al., 2013), Poland [START_REF] Guzek | Influence of cut and thermal treatment on consumer perception of beef in Polish trials[END_REF], South Africa [START_REF] Strydom | Do demographic and beef eating preferences impact on South African consumers' willingness to pay (WTP) for graded beef[END_REF] and China [START_REF] O'reilly | Influence of demographic factors on sheepmeat sensory scores of American, Australian and Chinese consumers[END_REF]. In the last two decades, independent and/or collaborative studies have concluded that this consumer focused and cooking & cut based quality grading scheme is applicable for those countries. It would be therefore greatly useful to have a platform for data pooling and comprehensive analyses to maximize research efficiency and benefit of the global beef industry (Hocquette et al., 2020). As such, a collaborative, non-profit and independent foundation, the International Meat Research 3G Foundation (3G Foundation), is established to improve consumer satisfaction on beef quality by promoting the worldwide collaborative meat research knowledge throughout bovine supply chain. To achieve this goal, the platform is established to coordinate and support the global scientific research on beef quality evaluation and prediction by collecting a large amount of data based on a standard methodology (MSA) for further data sharing and modeling, and the ultimate integrative investigations on beef quality prediction. Based on the collaborative international research, quality evaluation facilities (protocols, tools, software) are more widely available, beef grading systems are improved based on the widely application of consumer sensory and the commercial description and trading of beef will be achieved. As illustrated in Figure 2, the foundation is supported by the United Nations Economic Commission for Europe (UNECE), which encourages progress on specifications and regulations, technical cooperation, and exchange of informative resources with expertise and practices, the 3G foundation is linked to the UNECE Specialized Section on Meat. In summary, the foremost mission of the 3G Foundation is to provide informative and technological support to scientific study, in which the data of animal characteristics and consumers evaluation of beef palatability is collected based on the same standard protocol -the Meat Standards Australia methodology. There are three departments in the Foundation that execute diverse roles in order to assure the foundation's normal operation. The council is a body that makes decisions, monitors the activity and evaluates the work of the Foundation. There are six members in the council, the chairman Jerzy Wierzbicki, who is the president of Polish Beef Association; Dr. Rod Polkinghorne, the leader of Meat Standards Australia project; Dr. Declan Troy, who is the director of Teagasc (the Agriculture and Food Development Authority) etc. To conduct scientific research and provide database and research outputs, the scientific reference group is established and composed of scientist representatives from different countries and organizations such as Dr. Jean-François Hocquette from INRAE (France National Research Institute for Agriculture, Food and Environment), Dr. Linda Farmer from AFBI (Agri-Food and Biosciences Institute Northern Ireland). To share research findings and take diversity into consideration at the international level, the global DataBank is established to provide secure housing for a huge amount of data collected from thousands of consumer sensory evaluations, there are data sharing protocol and agreements as a base to support the development of DataBank. Apart from the scientific search, the 3G Foundation also provides beef industry the prediction model of beef palatability with the consideration of local livestock characteristics as well as regional consumer preferences ( Hocquette et al., 2020). So the management board mainly for bustiness functions and commercial activities including administration (finance, marketing and IT function for DataBank) and service (contract, consulting and data collection/analysis) is another important branch of the Foundation. Thanks to the international cooperation based on the International Meat Research 3G Foundation, with the expanding of the European database and meta-analyses, the reliability of an existing prototype of an MSA-like European model will be increased, as a result, a robust quality predictive model based on normal untrained consumers will be useful for the whole European bovine chain in the future.

The formation of carcass grading scheme in Europe

In Europe, in view of the diverse geographical and climatic environments that result in breed distribution diversity and various rearing and process practices in different regions, cattle are distinctively variable. When beef animal and carcasses began to be traded for meat between countries within Europe, a generic standard to sort carcass for carcass quality classification was required. Since the early twentieth century, categorization of carcass for trading and formalization of grading schemes were commenced in some countries, such as in the USA, the USDA has implemented standards for carcass grading for several decades but mainly focused on beef quality classification. However, this is in contrast with the situation in Europe, where commercial systems rarely include direct beef quality evaluation. Nevertheless, the emphasis on carcass quality does not mean that the quality of meat in Europe is not important; it does, to some extent, reflect the level of acceptance that meat eating quality could be reflected by the assessment of typical carcass physical characteristics. Since 1960s, carcass classification was more tended to be focused on several important economic traits: carcass weight, gender, fatness and conformation, notably, the concept of eating quality/palatability was not involved in the carcass description system since then. The same scheme but with different scales was implemented in France, Germany, Ireland, UK, Denmark and the Netherlands [START_REF] Fisher | Beef carcass classification in the EU: an historical perspective[END_REF].

The current grading system in Europe

For many years, as the result of aiding beef trade and price reporting for the marketing and administrative purpose throughout Europe, a Europe-wide beef carcass classification system was needed [START_REF] Fisher | Beef carcass classification in the EU: an historical perspective[END_REF]. The EUROP grid is therefore established and applied to classify carcasses within the Europe since 1981 according to the assessment of carcass muscling shape and fat level, described respectively by conformation score and fat score [START_REF] Fisher | Beef carcass classification in the EU: an historical perspective[END_REF]. Conformation score is assessed on an E to P basis, with E being a convex and shapely carcass, R being an average shape or straight profile, and P being a plainer carcass with a concave profile. In addition, in EUROP grid for conformation score, S is used as "superior" to indicate the profiles extremely convex with exceptional muscle development (double muscled carcass type) in Europe but not often used in France. Fat is assessed on a 1 to 5 basis, with 1 being very lean and 5 being very fat (Commission Regulation (EC) 1249/2008, 2008). The EUROP grid is conducted with Video Image Analysis or by trained graders. Since the EUROP grid is widely applied and regarded traditionally important for the European beef industry, European beef carcasses are traded based on the carcass weight and carcass conformation and fat scores [START_REF] Tarrés | Carcass conformation and fat cover scores in beef cattle: A comparison of threshold linear models vs grouped data models[END_REF]. In addition, the European classification system places emphasis on the description of production yield rather than beef eating quality, carcasses with different European classification scores being assigned to differentially priced and different sales markets. However, meat experts have gradually realized the weakness of the EUROP grid nowadays within Europe, since European classification scores are poorly related to the eating quality at consumer level and cannot reflect carcass composition (Pabiou et al., 2011) and consumer satisfaction (Bonny et al., 2016). Several reactions have been implemented to improve the current situation, for instance the introduction of other eating quality indicators such as marbling score (Monteils et al., 2017).

Certified and official quality labels in Europe

Guaranteed quality, originality, safety, traceability and processing standards are valuable for consumers' perception of the product and eventually for their willingness to pay. As a result, quality assurance schemes based on certification are good strategies for development of products in the market. In the meat sector, there are mainly two types of certifications. One category is the certified labels that are supervised by state, government and/or industry and the other category is the official quality labels that follow industry regulation and guidelines. The European Union has developed the policy to protect agricultural specificities and tradition and to promote high-quality food products, especially those resulting from heritage (i.e. long-term traditions). In Europe, quality assurance is becoming increasingly important especially with the appearance of food safety crises from time to time. European consumers are expecting more products that could be guaranteed by certified and official quality labels of origin or quality detailed below. For a certified product, the sign of "Product Conformity Certification" (PCC) is necessary to be indicated and the product will be inspected by an independent third-party agency, who is under the responsibility of the Ministry of Agriculture that to make sure the product meets the regulation standards. In France, certified products must differ from standard products by at least two specific characteristics.

EU certified labels

There are several EU certified labels that apply to cattle products, the most labels that being well known in EU market are Protected Designation of Origin (PDO), Protected Geographical Indication (PGI), Traditional Specialty Guaranteed (TSG) and EU Organic logo labels (Bonny et al., 2017). The PDO and PGI labels are mainly geographical indications that provide intellectual property rights for specific products whose attributes (specific qualities) are inextricably tied to the specific region where the product originated, which enable the products to be endowed with a reputation linked with the production territory. The differences between PDO and PGI labels are mostly related to how much of the product's raw ingredients must come from the production region and how much of the manufacturing process must take place in this specific region. In addition, the PGI label is also related to specific production methods and techniques. The TSG label emphasizes the traditional production methods, such as how the product is produced or what it is made of, without being related to a specific geographical region. The TSG label could protect the product from forgery and misuse. The EU Organic logo gives an identity to the product that fully complies with the conditions and regulations of the organic farming sector established by the European Union. A product labeled with EU Organic logo should contain at least 95% organic ingredients and the remaining 5% would meet strict conditions. This label mainly indicates the product is produced in a special way that is related to environment but have nothing to do with eating quality [START_REF] Commission | Quality schemes explained | European Commission[END_REF].

Label Rouge

"Label Rouge" is an official regulated sign of premium quality in France that certifies raw or processed agricultural products (food or nonfood) that possess specific characteristics with "a superior level of quality" than equivalent products [START_REF] Hocquette | Sensory Assessment of Meat[END_REF]. A Label Rouge product indicates its production processing has to follow the published specifications. In meat sector, if a beef product wants to be marked with Label Rouge, the meat-producing animal and the meat product have to be managed according to some specific guidelines involving such as from breed chosen, on farm conditions, transport to slaughter and postmortem aging. With the successive sorting in the process route, the quality of the product would be guaranteed. The guaranteed quality for Label Rouge has been mainly focused on eating quality. For labeled products, the quality must be evaluated as superior by tasting panelists in comparison with equivalent standard products (Raulet et al., 2021). But other value-adding factors such as image-value quality attributes (ethical, cultural and environmental dimensions associated with production processing) will become probably more and more important in the quality scheme in the near future. In 2017, as requested by the French Ministry for Food and Agriculture, primarily aiming to improve product quality, sectoral strategies were introduced to better account societal and environmental concerns in the animal food industry (Raulet et al., 2021). However, only about 5% of red meat including 2% of beef is sold using the Label Rouge system, an overwhelming majority (85-93%) of the products are not officially quality labeled in France (Hocquette et al., 2014). Nonetheless, Label Rouge is well recognized by French consumers with a good reputation of premium quality than other generic products. In consideration of the benefits could be provided by this wellrecognized brand for primary producers and retailers and also for French consumers who expect superior quality products, the Label Rouge will occupy more proportion in the French marketplace. In 2018, the French National Food Conference (États Généraux de L'alimentation) announced the five-year roadmap, with the goal of having 40% of red meat with the Label Rouge mark in the next five years (Ellies- Oury et al., 2019). Furthermore, with the implementation of EGalim (États généraux de l'alimentation), which is the "law for balanced trade relations in the agricultural and food sector and healthy, sustainable food accessible to all (loi pour l'équilibre des relations commerciales dans le secteur agricole et alimentaire et une alimentation saine, durable et accessible à tous)". Fifty percent of the products will bear signs of sustainable, origin and quality (including organic products) in public catering from 1 January 2022. In this way, the Label Rouge mark will occupy a bigger portion in the French market.

Some examples of beef brands in Europe

Charal products

Charal is a French beef brand created by a slaughterhouse and meat processing based company. Charal focuses its features on 1) eating quality, which is guaranteed by standard production system (i.e. "Herbopack" strategy to feed animals as long as possible on pasture grass); 2) traceability, which makes it possible to identify the exact origin of a piece of meat from the producer to the consumer; 3) safety, which is achieved by the inspection of CCPs throughout the production and processing chain and convenience system; 4) convenience, except from row beef products, Charal also provides ready-to-eat beef products such as burgers to consumers. All these features form the brand image of Charal, that is reliable and consistent. In addition, eating quality based classification has been attempted to sort animals from different production system by this brand for the purpose of improving beef eating quality (Bonny et al., 2017).

Or Rouge

Or Rouge is a Limousin beef brand that launched by a private French company that commits to provide tender, healthier and more flavor beef to consumers. The superior and specific quality of Or Rouge beef product is guaranteed through using Limousin cows that are reared in a specific terroir. In addition to specific grazing and production practices, efficient actions (e.g. carcass management and assessment) have been taken for quality control, which can be regarded as a good progress by contrast with the main focus on production yield in the beef industry. As a brand with excellent breed advantage and more importantly has been dedicated to superior eating quality assurance, marbling assessment has been gradually conducted in one local chiller of the French company who created the "Or Rouge" brand (Liu et al., 2021a). This is in line with the national strategy since, in 2018, the "Etats Généraux de l'Alimentation" (French Food Conference, 2018) proposed that marbling score would be included to the French beef grading scheme. In addition to carcass grading, carcass management such as the Tenderstretch method was recently adopted by this company to improve the tenderness of loin and hind quarter cuts (Bonny et al., 2018;[START_REF] Legrand | Suspension pelvienne et maturation de la viande de boeuf[END_REF]

Celtic Pride Beef

Celtic Pride Beef is a Welsh brand created in 2003 and in response to consumer demand for premium Welsh beef that met the animal welfare requirements. The cooperation between different divisions including farm producers, grassland management expertise and feed manufacturers has been developed to establish a strong Welsh brand and it is successful since nowadays the Celtic Pride Beef brand is one of the most well-known beef brands in Welsh. The main aim of this brand is to provide consistent high palatability for consumers, which is achieved by following a set of specific production and processing rules. For example, all the meat-sourced animals must be born and reared in Wales, the farming and rearing practices should follow the farm assurance standards of husbandry and welfare, certain growth and finishing regulations are applied to the animals during growth and before slaughter. In addition, extended maturation control is applied on premium cuts such as for Celtic Pride Gold, a minimum 35 days of aging time is used for improving beef sensory quality in particular with flavor. Celtic Pride Beef is certified by Protected Geographical Indication (PGI), which means that beef products can be traced back to their origin farm and/or location.

Waitrose beef brand

Waitrose beef is a high-quality beef brand and supplied by Waitrose, which is a British supermarket chain in the United Kingdom. Waitrose beef is sourced from a variety of continental and native British breeds that reared by a select group of British farmers who understand and share Waitrose's values and are dedicated to the brand mission of high quality, food safety and animal welfare. According to various regulations such as Assured British Meat and UK Register of Organic Food Standards (UKROFS), specific feeding, recording, transporting and processing practices have been conducted, which means for instance the origin of Waitrose beef cuts is possible to be obtained and high standards feeding and transporting system would minimize the negative effect caused by transit stress. For Waitrose premium cuts, vacuum aging is applied for postmature development of tenderness and flavor [START_REF] De Barcellos | Quality assurance and vertical alliances: case studies in the UK and Brazilian beef chains[END_REF]. There are several beef brands in Waitrose beef supply chain, such as essential Waitrose British beef that produced from prime cattle and Aberdeen Angus beef and Hereford beef are sourced from cattle sired by registered pedigree Aberdeen Angus bulls and Hereford bulls (Bonny et al., 2017).

New livestock systems to provide better beef eating quality

The concept of agroecology and its evolution

The term "agroecology" first appeared in 1928 was defined as an application of ecology in agriculture and until 1960s, it had been mainly focused on the applications of field crops, soil biology, plant physiology and protection, pest management, human agricultural management in agriculture related with ecological/environmental factors [START_REF] Wezel | A quantitative and qualitative historical analysis of the scientific discipline of agroecology[END_REF]. Since the beginning of the 1980s, agroecology has been established as a scientific discipline to apply ecological theory on agroecosystems, which is viewed as a domesticated ecosystem intermediates between natural and fabricated ecosystems [START_REF] Puia | Agroecologie: Ecosistem si Agroecosistem[END_REF]. With the advancement of industrialization, pollution issues and the importance of protecting natural resources had been realized. Consequently, sustainability became a topic within agroecology and a strong link was developed between agricultural production and natural resources [START_REF] Altieri | Agroecology: A new research and development paradigm for world agriculture[END_REF][START_REF] Wezel | A quantitative and qualitative historical analysis of the scientific discipline of agroecology[END_REF]. After entering the 21st century, the concentration of agroecology tends to shift from agroecosystems to food systems. As it was defined by Francis in 2003 that agroecology is "the integrative study of the ecology of the entire food systems, encompassing ecological, economic and social dimensions" [START_REF] Francis | Agroecology: The ecology of food systems[END_REF]. In addition to food systems, agroecology is currently dealing more often with the strategy of sustainability and biodiversity. The evolution of agroecology with different concepts is based on the enlarging scales and dimensions. Since the invent of the concept of agroecology until the present, the focus of agroecology evolved from the plot/field scale to a larger scale such as a local regional territory which has agricultural practices [START_REF] Wezel | A quantitative and qualitative historical analysis of the scientific discipline of agroecology[END_REF]. As of the current time, according to some scientists that agroecology enters the complete dimension of food system to transcend beyond the tangible spatial scale to eventually achieve agricultural sustainability. The full dimension incorporates natural, societal, economic and political aspects, food production systems and local, regional, national and global geographical scales [START_REF] Francis | Agroecology: The ecology of food systems[END_REF][START_REF] Gliessman | Agroecology: The ecology of sustainable food systems[END_REF]. In the last few decades, the industrial livestock farming system has led to a massive increase in agricultural production but meanwhile has posed many problems with respect to environment damage, animal welfare issue and agrobiodiversity loss. In this context, agroecology is currently used as not only a scientific discipline but also an agricultural movement or practice to provide a new way of considering agriculture and the interactions between plants, animals, humans and environment within the agricultural system by covering agronomy, ecology, sociology and economics, and for the further management and development of sustainable farming systems [START_REF] Dalgaard | Agroecology, scaling and interdisciplinarity[END_REF][START_REF] Wezel | A quantitative and qualitative historical analysis of the scientific discipline of agroecology[END_REF]. Furthermore, the development of agroecology with the incorporation of new dimensions may facilitate the research work with the public and policy makers to address the current pressing issues about agriculture sustainability, global natural resource use, climate change, animal welfare and food security [START_REF] Wezel | A quantitative and qualitative historical analysis of the scientific discipline of agroecology[END_REF].

Innovative livestock systems

To long-term reconcile natural resource, ecosystem and food production, many agricultural farming systems (e.g. agroecological farming, organic farming and sustainable intensification system) have emerged in response to the growing ecological concerns about industrial agriculture [START_REF] Dumont | Prospects from agroecology and industrial ecology for animal production in the 21st century[END_REF]. Due to the extensive land use and the drive for agricultural expansion, livestock farming system contributes significantly to the agroecosystem stability and sustainability [START_REF] Wirsenius | How much land is needed for global food production under scenarios of dietary changes and livestock productivity increases in 2030?[END_REF]. Moreover, the increasing demand for food with the increasingly growth of human population, income and urbanization puts pressure on all food-producing sectors and indicating the need for livestock farming and production system to be more productive and sustainable (Aydin, 2021). Along with the goal to improve the efficiency, autonomy and sustainability of livestock system, many agroecological principles and processes involving feed resources, animal species and management practices have been proposed and implemented in new livestock systems. For instance to strengthen the resilience of farming system, mixed animal production systems are applied in the same area, to reduce the environmental footprint and agrochemicals, inputs could be reduced and natural process will be spontaneously stimulated and productive efficiency will be increased [START_REF] Dumont | Prospects from agroecology and industrial ecology for animal production in the 21st century[END_REF].

Biodiversity enhances the resilience of farming system

To meet the needs of a growing world population for food and agricultural products, livestock farming systems have long been intensified, specialized and geographical concentrated. Indeed, those new productive models facilitate increased agricultural production but led to some problems such as biodiversity loss [START_REF] Dumont | Incorporating diversity into animal production systems can increase their performance and strengthen their resilience[END_REF]. Agricultural biodiversity is decreased in many ways, such as the selection of animals and plants based on productivity would reduce the variety of species and breeds; the interference of management practices and inputs, and standardized production, farm and region management approaches will reduce environmental heterogeneity, which is less conducive to the long-term adaptation of uncertainly global change (Magne et al., 2019). Biodiversity is a crucial attribute that is highly valued in agroecology since its capacity to promote farm performance and to enhance the resilience of livestock farming system via mechanisms that operate at different levels [START_REF] Dumont | Incorporating diversity into animal production systems can increase their performance and strengthen their resilience[END_REF]. Indeed, a sustainable development of functions and services that livestock farming systems provided is highly depended on biodiversity maintenance. With only external inputs such as concentrated feed and veterinary products, one animal production system cannot be secured in the long term especially under the uncertainty of climate and diseases. But within one system, a higher biodiversity is certainly good for reinforcing internal stability to cope with perturbations through its own buffer, adaptive and transformative capacities [START_REF] Bommarco | Ecological intensification: Harnessing ecosystem services for food security[END_REF].

Multi-species grazing system is a good strategy to promote biodiversity and reinforce resilience. Keeping different animal species within one farming system provides a strategy to spread risk in response to feed shortage, disease outbreak and market demand and price fluctuations. Complementary feeding strategies within one grazing system allow for more efficient use of resources (e.g. land, water and pastures) from multiple spatial niches [START_REF] Dumont | Incorporating diversity into animal production systems can increase their performance and strengthen their resilience[END_REF]. Several studies have found the benefits of mixed grazing system where sheep and cattle were reared together, increased liveweight gain and meat production was observed (D'Alexis et al., 2014;Jerrentrup et al., 2020). In general, parasites are different between livestock species, mixed grazing with cattle can reduce the parasite suffer for sheep [START_REF] Mahieu | Effects of sheep and cattle alternate grazing on sheep parasitism and production[END_REF]. In addition, one species could even potentially improve the food availability for another species due to their distinct grazing behaviors, and in combination with the complementary pasture use, the pasture nutritive value and overall biodiversity within the mixed grazing system can be maintained to a larger extent (Dumont et al., 2018;Jerrentrup et al., 2020).

Reducing inputs contributes to more efficient farming operation and autonomy

In the field of agroecology, autonomy is defined as the independence of farmers and farming systems from the exterior. This concept is particularly important for farmers since the implementation of an agroecology transformation of livestock farming system requires greater autonomy in terms of inputs and the economic efficiency (Magne et al., 2019). The achievement of autonomy highly depends on the control of the flows of material between farming systems and environmental and socio-economic components of territories and supply chains (Magne et al., 2019). Reducing the use of external materials and resources is a promising viewpoint to operate farming practice in an agroecological manner and ultimately achieve autonomy to the greatest content. The reduction of inputs regarding feed inputs for animals, chemical inputs for plants and technical inputs for farm management should be involved in the framework of new livestock farming systems to help farmers to be independent on outside suppliers and to make their own decisions. There are several trains of thought to reduce inputs: 1) decreasing the import of feed and chemical supplies from outside the farm, 2) increasing the efficiency of animal feeding and nutrients utilization and 3) optimizing farm labor and workload in farm management [START_REF] Dumont | Prospects from agroecology and industrial ecology for animal production in the 21st century[END_REF].

Decreasing the feeding supplies from outside could firstly reduce feed costs for farmers, especially under the condition of fluctuation of feed price. In a rangelandbased feeding system, when grass is used as the main feed resource, the natural process and agroecological service of the farm could be extensively preserved and a feeding self-sufficient mechanism can be achieved. Although pasture-based feeding systems require a large area to compensate for the low forage productivity, the market for grass-fed beef products is favorably positioned in terms of economic efficiency, i.e. consumers are willing to pay premium for beef products that are produced in a natural environment. In addition, in grassland-based feeding systems, animal health risks are relatively lower than other systems [START_REF] Dumont | Prospects from agroecology and industrial ecology for animal production in the 21st century[END_REF], which indicates a decreasing veterinary medicine input and a better or at least a maintained feeding efficiency.

Improving feeding efficiency offers the potential to improve producer profits while reducing the environmental footprint [START_REF] Kenny | Invited review: Improving feed efficiency of beef cattle -The current state of the art and future challenges[END_REF], which is good for sustainable farming by promoting the reconnection between animals-plants-soil and, good for autonomy in the end (Dumont et al., 2018). Many empirical studies have explored how to improve feeding conversion and utilization by appropriate practices. For instance, sequential feeding wheat grain and protein-mineral concentrates to laying hens could improve feed conversion and promote the utilization of local feedstuff, which therefore facilitates feeding cost reduction [START_REF] Faruk | Sequential feeding using whole wheat and a separate protein-mineral concentrate improved feed efficiency in laying hens[END_REF]; in a sheep and cattle mixed farming system, the animal growth and production is increased with the increasing grass utilization and feeding efficiency, which results subsequently in self-sufficiency (Jerrentrup et al., 2020), since increasing the efficiency by animals self-use of nutrients can assist lower the amount of nutrients imported from outside. In addition, farm labor is considered as an important input during the farm operating. If there is only one species in a farm, the routine workload will be heavy and repetitive. But with a mixed animal farming system, the farm management intensity will be decrease since the workload for each species will be alleviated and it will be more flexible for farmers to take care of them both in a more professional way.

Local and regional management practices reinforce agriculture sustainability

Agroecological development of livestock farming is considered very important for sustainable agriculture and has long been calling in Europe especially in France (Beudou et al., 2017). The adaption of farming and management practices based on local autonomy, historical and cultural background, terroir feature and production and consumption in local food systems is required in the designation of new livestock systems. From the perspective of economic efficiency, local breeds are likely to be replaced by more productive commercial breeds with better quality traits. However, from an agroecological point of view, local breeds are more ideally suited to local harsh environment and economically marginal condition due to a stronger ability to survive and maintain breeding and producing level with less veterinarian intervention and feed costs [START_REF] Dumont | Prospects from agroecology and industrial ecology for animal production in the 21st century[END_REF]. In fact, even in terms of economic efficiency, local breeds characterized by significant local image/identity could obtain higher quotation since consumers would trust those products that have superior palatability and nutritional value (Casabianca & Matassino, 2006). Most importantly, management practices based on local and regional characteristics could help a long term sustainable agricultural development. Animal production systems should be considered not only as a whole, but also in terms of the diversity of their local and regional conditions. Take the famous French breeds Limousin and Charolais as good examples that Limousin originated mainly from the Limousin regions of France, which is located at the west side of south-central French Massif Central and Charolais originated in the area of Charolais around Charolles in the Saône-et-Loire department in the Bourgogne-Franche-Comté region of eastern France and they are named as such as their originated regions. The broad-based governance of their own breed in their own region enables participation in appropriate decision making and efficient actions, leading to the reinforcement of local breed and sustainable development of the local brand. In addition, appropriate management strategies could achieve an optimization for both animal production and biodiversity conservation. In a grassland-based beef production system, through a biodiversity-friendly practice on grassland type, livestock stocking rates and herd management on up to 40% of the farm area, it was found that beef production could be greatly maintained [START_REF] Jouven | Simulating grassland utilization in beef suckler systems to investigate the trade-offs between production and floristic diversity[END_REF].

Mixed grazing systems

Mixed grazing systems are where grazing by more than one herbivore species in one pasture area. Mixed grazing seems to be a tendency of agroecological process that can be effective at optimizing animal performance. It was confirmed in a study that in comparison with single species grazing system, mixed sheep/cattle grazing system has a better meat production due to the higher animal body growth, which is obtained from the complementary use of plants, reduced parasite burdens and limited additional costs (D'Alexis et al., 2014). Mixed grazing strategy is highly related to farmers' benefits since grassland-animal system outputs are increased with decreasing inputs such as less fertilizer usage or land management intensity. In addition, mixed grazing reduces animal density by different feeding strategy with different species, which brings benefits to environment such as soil coverage and erosion prevention by less grass eaten (Joly et al., 2021). In order to implement a mixed grazing system in the local pasture region, a multidisciplinary study was conducted by a group of scientists and technicians from the Unité Mixte de Recherche sur les Herbivores research sector. To better meet the objectives of organic grassland breeding system, that is to produce meat in mountainous grassland in an autonomous manner with minimum inputs, or, to test if mixed breeding and grazing could improve the multi-performance of the whole system, two strategies are introduced: 1) specific crossed animals are used for a better adaption of grass feeding and meat production and 2) mixed species grazing system for a higher efficiency of grass and other farm resources usage. The whole experiment was set up in 2015 on the Laqueuille site of the Herbipôle experimental unit and includes 3 independent systems: two mono-specific sheep and cattle systems and a mixed system grazing sheep and cattle (60% and 40% of livestock units respectively) together. All three systems use crossed animals that are originated from a hardy breed dam (Salers for cattle, Limousin for sheep) and a grass-grazing and early-maturing breed sire (Angus for cattle, Suffolk for sheep). The three systems are subject to a multi-criteria evaluation by taking into account technical, economic and environmental performance including animal health, product quality, changes in production and grassland flora, ecosystem service, workload and working conditions, and carbon footprint. The success of experimentation was highlighted in the first year but the obstacle inherent to mountain grazing system was also observed. Currently, the majority of suckling cattle systems produce grazing animals that are exported and intensively fattened, which prohibits their marketing in organic farming. Similarly, the majority of suckling sheep systems practice fattening in sheepfolds to avoid climatic and sanitary hazards. The improvement of precocity made possible by crossing with an early breed could make it possible to reduce the age of young cattle at slaughter to about 12 months and thus limit the capitalization and the induced risks observed in the dominant models. Slaughtering young cattle is a major issue if fattening is to be encouraged in mountain pasture systems. The breeding system is therefore intended to be farrow-to-finish. With only grass feeding, which might not be an ideal strategy for the local breeds with high food demanding but low growth rate, such as Limousin and Charolaise, to be productive enough due to the limited energy intake. In this context, less demanding breeds such as Angus, who are well adapted with grass feeding and fattening but are also characterized by early maturing mechanism, would be a good option for the objective to have animals who can produce meat with good quality at a younger age. Improving productivity also relies on the adaptation of local ecosystem services, hardy breeds such as Salers are needed for the mountainous pasture environment, which might be hard to other animals to maintain normal physical function and productivity. The experimental site has been converted to organic farming. The issues addressed are more relevant to organic livestock systems, but the results will also be of interest to conventional grassland systems, which are being questioned about their environmental impacts and input levels. This could be resolved by the aforementioned specific crossed animals, who contribute to the strategy of less demanding on food and drug input, moreover, mixed grazing with cattle and sheep could also reduce parasitism through a dilution effect, as some parasites are specific to only one animal species. Mixed grazing system, on the other hand, appears to have become more complicated. Grass-fattening with minimum inputs necessitates careful grass management and animal monitoring to detect parasite risk and target possible therapies at an early stage. Other management, such as assessing the quality of the meat produced on the farm, necessitates more confirmation of the mixed farming and grazing systems' application and scalability.

The current state of conventional meat and meat alternatives

The current challenge faced by conventional meat industry

Meat plays a crucial role in human evolution through rich nutrition supply of high biological value protein, fatty acid, iron, zinc, selenium, vitamin B12 and energy. Due to the vital function meat has exerted in the development of human health, meat has been highly recommended in daily nutritional-balanced diet, which therefore leading to a significant increase in the demanding and consumption of meat [START_REF] Pereira | Meat nutritional composition and nutritive role in the human diet[END_REF]. With estimates of the world's population reaching 10 billion in the year 2050, to meet the per capita demand for meat from the growing population, the meat industry will need to increase production by approximately 73% (FAO, 2009;United Nations, 2017). However, conventional meat production seems reaching a plateau due to the inefficient production process and increasing problems concerning sustainability, health and safety, environment protection and animal welfare.

Environmental sustainability

While animal foods only account for 17% of the global calorie consumption, meat and dairy products account in total for around 30% of worldwide protein consumption. Conventional meat industry is facing major environmental sustainability challenges in terms of land use, freshwater consumption and pollution, greenhouse gas emission and biodiversity loss [START_REF] Poore | Reducing food's environmental impacts through producers and consumers[END_REF]. On one hand, of the 13.2 billion ha of land on the planet, 28% (3.7 billion ha) is covered by forest (FAO, 2011) and 38% (4.92 billion ha) by agriculture, with 1.56 billion ha for crop production and 3.36 billion ha for grasslands [START_REF] Alexandratos | World agriculture towards 2030/2050: the 2012 revision[END_REF]. While it is true that livestock production accounts for 70% of agricultural land, it should be emphasized that this predominantly comprises of non-arable land such as pastures and grasslands. Permanent grassland and rangelands occupy 73 million hectares in Europe, accounting for 40% of total agricultural land [START_REF] Peyraud | The role of grassland based production system in the protein[END_REF]. On the other hand, livestock system could convert plant biomass that is not suitable for human consumption into plant by-products and crop residues and certain livestock systems are net producers of edible proteins [START_REF] Röös | Limiting livestock production to pasture and by-products in a search for sustainable diets[END_REF]. According to [START_REF] Mekonnen | A Global Assessment of the Water Footprint of Farm Animal Products[END_REF], 1 kilogram of beef requires 15,000 liters of water to produce. In Australia, over 20 times more water is required to produce 1kg of beef compared to rice, grains, pulses, fruits and vegetables [START_REF] Steinfeld | Livestock's long shadow[END_REF]. However, this strategy, which was mainly focused on industrial segment without considering the effect of biological cycle. In fact, rainwater accounts for 95% of this footprint, which is part of the global water cycle, so does not represent a net water consumption by animals. Even if there were no animals in the system, the cycle would continue. Only water consumed by animals and agricultural irrigation competes with human demands, beef production requires an average of 550-700 liters of those water for per kilogram beef production according to different calculations [START_REF] Doreau | Production de viande et ressource en eau[END_REF]. At the farm level, a range of 25 to 234 liters of those water for per kilogram live weight production is required in several beef production systems in New South Wales and Australia [START_REF] Ridoutt | Water footprint of livestock: Comparison of six geographically defined beef production systems[END_REF]. Given these wide disparities, it is evident that a critical examination of the significance of those numbers in the field of water use is required. However, different assessments are utilized to estimate the "water footprint" with the same language but not the same principles and significances [START_REF] Boulay | The WULCA consensus characterization model for water scarcity footprints: assessing impacts of water consumption based on available water remaining (AWARE)[END_REF]. There are also a misunderstanding concerning the efficiency to transform plant proteins into animal proteins. While it is true that in the late 1990s, animals consumed 74 million tons of edible plant protein and produced 54 million tons of animal protein [START_REF] Steinfeld | Livestock -Environment Interactions[END_REF], resulting in a ratio of 1.4 kg of edible plant protein per kg of animal protein. With the increased crop yields and other efficient adjustment for feedstuffs, some livestock production systems can contribute to food protein production by producing more animal-edible protein than they consume edible plant protein [START_REF] Manceron | Feeding proteins to livestock: Global land use and food vs. feed competition[END_REF]. Indeed, in Australian, substantial beef production can provide up to 7.9 kg of edible animal protein from one kilogram of plant protein [START_REF] Wiedemann | Resource use and greenhouse gas intensity of Australian beef production: 1981-2010[END_REF]. Since animals consume minimal amounts of animal feedstuffs that may possibly be fed to humans, meat produced from plant-based systems yields more human-edible protein than what they consumed in the system. In the last five decades, to further enhance animal production, selective breeding strategies have been extensively implemented and animals have been pushed to their biological limitations to produce more food and on the other hand, resulting in a loss of natural biodiversity, which is against agroecological sustainability. Moreover, with the expanding urbanization and climate change, the land available for agriculture without more deforestation will continue to decline. The high-water footprint with water pollution and water scarcity calls for a more sustainable way to improve the efficiency of conventional meat production. In addition to the negative effect of livestock production on water pollution, greenhouse gas emission associated with global warming is also a big issue that draws a great societal concern. Livestock industry contributes 12-18% of total greenhouse gas emission including 37% of all methane emissions mainly associated with ruminants, making it a substantial contributor to global climate change [START_REF] Gomez-Zavaglia | Mitigation of emerging implications of climate change on food production systems[END_REF]. A high meat diet releases more than double the daily carbon dioxide equivalent of a vegetarian diet [START_REF] Scarborough | Dietary greenhouse gas emissions of meat-eaters, fisheaters, vegetarians and vegans in the UK[END_REF]. A reduction of meat consumption and/or the adoption of other meat alternatives such as cultured "meat" has been therefore suggested to reduce GHGs emissions and their impact on global warming and climate change. However, the environmental impact of conventional meat and other meat substitutes is still controversial. Other studies have found different GHG estimates associated with direct livestock emissions, which is smaller (e.g. less than 3% of anthropogenic GHG) than the previous estimations [START_REF] Mitloehner | Clearing the Air: Livestock's Contributions to Climate Change[END_REF]. In addition, the long term impact of cultured "meat" production on environment still needs to be further investigated [START_REF] Lynch | Climate Impacts of Cultured Meat and Beef Cattle[END_REF].

Safety and health concerns

Meat has long been highly demanded and consumed due to its nutritional richness. Despite the nutritional benefit, with the increasing prevalence of some diseases such as cancer and cardiovascular, scientific research on meat associated with increased risk is being driven. Many evidence-based studies demonstrated that high consumption of red meat (beef, pork and lamb) particularly processed meat, is associated with an increased risk of several major chronic diseases [START_REF] Wolk | Potential health hazards of eating red meat[END_REF]. Several components and mechanism have been proposed to explain the related risk of certain diseases, for example, saturated fatty acid is considered a big contributor to cardiovascular diseases. Fatty cut of meat is therefore recognized as a negative sign of being an unhealthy food, while in fact a moderate fat within meat is the source of good palatability. What we should be clear with is that a higher intake of red meat may be a marker of risk due to an unhealthy lifestyle rather than the risk factor itself [START_REF] De Abreu Silva | Higher red meat intake may be a marker of risk, not a risk factor itself[END_REF]. Meat and meat products are the main carriers of human foodborne diseases caused by pathogens such as Salmonella, Campylobacter and Escherichia coli [START_REF] Lianou | Meat Safety-I Foodborne Pathogens and Other Biological Issues[END_REF]. Most bacterial illnesses were caused by animal products (18% of poultry, 13% of beef and 18% of dairy). Overall, meat and poultry products (beef, game, pork, and poultry) accounted for an estimated 22% of illnesses and 29% of deaths [START_REF] Painter | Attribution of foodborne illnesses, hospitalizations, and deaths to food commodities by using outbreak data, United States, 1998-2008[END_REF]. The conventional conduits such as noncompliant animal production process (e.g. intense livestock farming), increasing meat demand and international trade (especially with fresh meat) and conventional meat distribution could lead to meat safety problems and health concerns of consumers [START_REF] Sofos | Challenges to meat safety in the 21st century[END_REF]. During the onset of the COVID-19 pandemic, food safety and health concern become a big issue for consumers, the conventional meat industry is facing the challenge of meat safety and shortages, this might provide new opportunities for meat alternatives such as plantbased meat substitutes and so-called clean "meat" -artificial "meat".

Animal welfare

Animal welfare issue in relation with conventional meat production is concerned by consumers especially in developed countries. More than 70 billion land animals are slaughtered for meat production annually. To do a brutal arithmetic, more than 6 million living animals around the world will be slaughtered in the next hour [START_REF] Townsend Eaton | Suffering of Animals in Food Production: Problems and Practical Solutions.The Palgrave Handbook of Practical Animal Ethics[END_REF]. Furthermore, to boost the meat productivity, conventional meat industry has been criticized for causing immense animal suffering [START_REF] Leroy | Animal Killing and Postdomestic Meat Production[END_REF]. Although certain regulations for ensuring animal welfare are being developed, some guidelines are hard to be fully implemented with a large-scale animal meat production such as in a factory farming system [START_REF] Munteanu | Can Cultured Meat Be an Alternative to Farm Animal Production for a Sustainable and Healthier Lifestyle?[END_REF]. The situation might be better in developed countries with proper intervals to avoid unnecessary animal suffering, such as with the development of Compassion in World Farming (CIWF), which is an animal welfare campaigning and lobbying organization, animal welfare is gaining ground with this global movement with partners and supporters (i.e. celebrities, television media) who are concerned about farm animal welfare. However, in some developing countries, animal welfare concern remains at a lower level due to the lack of government regulation, initiatives (i.e. CIWF) and public common sense [START_REF] Nizamuddin | Animal welfare in Asia: specific flaws and strengths, future trends and objectives[END_REF]. With the increasing meat demand driven largely by those countries, animal welfare issue tends to become worse. To address animal welfare problem, one option that highly esteemed by the meat alternative industry is to eliminate convention animal production and to shift toward a meat-free diet. This is anyway a matter of debate and for sure, animal welfare issue related to conventional meat production should be on the ethical agenda worldwide. Herein, we should consider the European Welfare Quality project as a good example to highlight the improvement of animal welfare issue. The European Welfare Quality project created standardized methods for assessing animal welfare and it provided methods to combine this information so that farms and slaughterhouses could be classified into one of four levels (from poor to good animal welfare conditions). This animal welfare assessment system is unique in that it places a greater emphasis on directly animal-based measures such as animal body condition, health aspects and behavior. The development of this program was of significance for the practical improvement of animal welfare in Europe but also had positive and profound impacts on animal welfare development in other regions.

Consumer feedback and demand

As the end point of conventional meat supply chain, consumer feedback and demand should receive high priority. Consumer demands are not easy to be met due to their multifactorial complexity. But it is very important to take consumer feedback into consideration during the food production and processing. In addition to addressing the aforementioned environment, safety and ethical problems that conventional meat production caused according to consumers' opinions, consumers' demand for premium palatability is also challenging for the conventional meat industry. In other words, the conventional meat production has difficulty to meet increasing consumer demand on meat quality (Hocquette et al., 2018). This might be due to the lack of attention on consumer feedback and demand, and efficient quality control and predicting systems. In Europe, meat/carcass price and trade are not based on meat quality, which is associated with consumer feedback and demand, but only depends on carcass classification standard, which has no relationship with meat quality and consumer eating experience (Bonny et al., 2016a;[START_REF] Liu | European conformation and fat scores of bovine carcasses are not good indicators of marbling[END_REF]. Although there are several quality-and consumer-based meat quality grading systems such as the MSA system in Australia, for most countries including European countries and China, a formal meat grading system based on quality at the consumer end is still lacking. A substantial meat consumption without a guarantee of palatability will lead to a loss of consumer trust in traditional agriculture and a shift to other alternatives, which particularly aim to meet the different consumer demands according to their feedback and the expression of their opinions.

The current meat alternatives

According to the aforementioned challenges that conventional meat industry faces, a changing in the market share of conventional meat might occur in the future with the rapid development of novel meat alternatives, mainly including plant-based, mycoprotein-based, insect-based and cell-based products, which are advertised for their potential ability to alleviate the environmental, safety and health, as well as animal welfare problems associated with conventional meat production.

Plant-based meat substitutes

Plant-based meat substitutes are meat-like products that approximate meat with similar texture, taste, flavor, color and nutritional profile by using ingredients derived from plant such as pulses, wheat gluten, soy protein and fungi (Kyriakopoulou et al., 2018). The flavoring of plant-based dishes dates thousands of years back to China, this type of products being made by simple derivatives from soybeans (i.e. tofu, tempeh) or wheat (i.e. seitan). They can be regarded as the first generation of meat substitutes that was used simply to replace meat in the daily diet mainly for economic or religious constraints. With the advancement of processing technology, in the current market, plant-based meat substitutes are no longer about protein products, they are rather products as near equivalent replacements of real animal meat with respect to texture, taste and flavor [START_REF] Santo | Considering plant-based meat substitutes and cell-based meats: a public health and food systems perspective[END_REF].

Other non-animal-based meat substitutes

Mycoprotein can be used as a meat alternative protein that derived from single-cell protein, which is produced from macrobacteria, yeast, fungi and microalgae. Mycoprotein presents its capacity of being a good meat substitute due to several advantages. Mycoproteins are rich in nutritious such as essential amino acids and vitamins. Proteins produced by certain mycoproteins, such as fungi, have a structure and diameter similar to that of muscle fiber in meat [START_REF] Asgar | Nonmeat protein alternatives as meat extenders and meat analogs[END_REF], a meat similar texture and sensory quality of mycoproteins-based products can be achieved by proper formulations and processing. In addition, the lower cost of production of mycoproteins by using agro-industrial waste without environmental limitation is a major advantage in terms of ecofriendly and sustainability. Mycoprotein is also proved to be safe and healthy concerning foodborne diseases and functional response of human health [START_REF] Hashempour-Baltork | Mycoproteins as safe meat substitutes[END_REF].

Insect-based meat substitutes

Insect-based meat substitutes are a meat analogue by using insects as food resource. Edible insects are more and more advertised as alternative protein sources for their lower environmental impacts [START_REF] Smetana | Sustainability of insect use for feed and food: Life Cycle Assessment perspective[END_REF] and improved protein profile as an animal driven product in contrast with plant-based products [START_REF] Bußler | Recovery and techno-functionality of flours and proteins from two edible insect species: Meal worm (Tenebrio molitor) and black soldier fly (Hermetia illucens) larvae[END_REF]. However, due to cultural barriers in developed countries to eat insects as a food component, consumers' willingness to consume insect-based food is inhibited especially in Western countries [START_REF] De Koning | Drivers and inhibitors in the acceptance of meat alternatives: The case of plant and insect-based proteins[END_REF][START_REF] Modlinska | Relationship between Acceptance of Insects as an Alternative to Meat and Willingness to Consume Insect-Based Food-A Study on a Representative Sample of the Polish Population[END_REF]. Despite the impact of food neophobia, insect-based products are still a promising meat alternative due to their nutritional quality traits [START_REF] Lee | Status of meat alternatives and their potential role in the future meat market-A review[END_REF]. In fact, in South America, Africa and Southeast Asia, eating insects is an ancient dietary habit and there are more than two thousand species of insects being commonly consumed [START_REF] De Carvalho | The potential of insects as food sources-a review[END_REF]. This implies that a big potential market can be explored for insect-based meat substitutes to address the increasing demand for high-quality proteins. With the development of other meat substitutes, insects-based food market is expanding steadily and is expected to exceed 522 millions of US dollar by 2023 [START_REF] Van Thielen | Consumer acceptance of foods containing edible insects in Belgium two years after their introduction to the market[END_REF].

Cell-based "meat"

Cell-based meat, also called cultured "meat", artificial "meat" and in-vitro "meat", is receiving plenty of publicity and becoming the hottest topic that garnering the interest of all sectors of society with its potential advantages in: 1) similarity with conventional meat; 2) improved animal welfare; 3) natural resource saving; 4) potential lower GHG emissions; 5) potential public health and safety; 6) providing more proteins to satisfy the increasing population and food demand; 7) reforestation and wild life protection (Bryant & Barnett, 2020;[START_REF] Lee | Status of meat alternatives and their potential role in the future meat market-A review[END_REF][START_REF] Newton | Social and Economic Opportunities and Challenges of Plant-Based and Cultured Meat for Rural Producers in the US[END_REF]Post, 2012). But these potential benefits are a matter of controversy (Chriki & Hocquette, 2020). Cell-based meat is produced using animal cell culture technology, which means from animal cells rather than from slaughtered animals. By using a range of groundbreaking techniques and technologies, according to its proponents, cell-based meat is the only one to produce genuine animal muscle-based meat [START_REF] Lee | Status of meat alternatives and their potential role in the future meat market-A review[END_REF]Siegrist & Hartmann, 2020) compared to other meat substitutes. Despite cell-based meat is closer to real meat compared to other meat alternatives, technically speaking, this novel product is not exactly real animal meat that is defined as "the animal flesh (especially mammals) that can be eat as food". Furthermore, in the EU legislation, cultured "meat" is considered as a novel product, not real meat (Règlement (UE) n° 2015/2283, 2015; Règlement (UE) n° 853/2004, 2004).

Will conventional meat be substituted by meat alternatives in the near future?

Consumer demand determines market price, volume and distribution of commodities. There are two types of demands regarding meat consumption in the future. First of all, meat quantity and quality will be highly demanded to meet the growing worldwide population and the increasing consumers' expectations for superior palatability; secondly, with the development of agroecology and the increasing human consciousness of environmental and ethical issues, the adding value of meat products concerning environment, ethics and sustainability will be highly demanded by consumers. Products of similar prices that are in line with those consumer demands will dominate the market. If conventional meat products are as expensive as they have always been but without the guarantee of good eating quality, consumer demand for cheaper alternatives will consequently increase. The products likely to compete strongly with conventional meat will be the meat substitutes made based on plants, insects and mycoproteins. Indeed, these products react rapidly to be the closest to the current consumer trends of plant-based and label-friendly products, which are also attractive to manufacturers due to their low cost and simplicity of production. Moreover, commercialization of those products can be easily achieved on a large scale since they could enter the minced and ground meat market with less palatability demanding [START_REF] Bonny | What is artificial meat and what does it mean for the future of the meat industry[END_REF]. This could lead conventional meat/prime cuts into the highest end of the market. This area is actually very difficult to be replaced by meat alternatives since the sensorial quality of those products still differ from consumers' expectations established based on conventional meat. Although progresses have been made with the ongoing technical development, difficulties and barriers concerning the exact imitation of conventional meat, production costs, government regulations still remain before a large-scale commercial application (Zhang et al., 2020).

Nutrition, safety and human health

There is no doubt that conventional animal source meat and meat products are the best sources of protein with good palatability. However, animal source foods (e.g. beef, chicken and pork) are major reservoirs of many foodborne pathogens such as Shiga toxin-producing E. coli, Salmonella and Listeria monocytogenes. Illness and even death that caused by foodborne pathogens related to meat raises great concern for the conventional meat industry [START_REF] Heredia | Animals as sources of food-borne pathogens: A review[END_REF]. In 2013, 73 outbreaks caused by Shiga toxin-producing E. coli were reported in the EU, with beef and its products being the main vector [START_REF] Efsa | The European Union summary report on trends and sources of zoonoses, zoonotic agents and food-borne outbreaks in 2013[END_REF]. Avian and swine flu are important public health issues that can affect human health, while synthetic pesticides, antimicrobial and growth hormones use during animal production to treat infections, prevent diseases and promote growth is another problem that threaten human health [START_REF] Ma | Use of antimicrobials in food animals and impact of transmission of antimicrobial resistance on humans[END_REF]. In addition, the concern about nutrition-related chronic complications is in contradiction with the desire to consume meat, which might have a higher fat content for the better eating quality. Be free from animal origin production, the absence of risk concerning foodborne pathogens, antibiotics residues and zoonotic diseases gives meat alternatives, especially plant-and cell-based meat substitutes, a potential advantage in terms of safety and healthiness. However, the nutritional barrier is difficult for plant-, insect-and mycoprotein-based food to overcome. Despite it is claimed that those products are being rich in proteins, which are approved to be safe and natural, they are actually not considered to be the same proteins that conventional meat contain. Besides, some plant-based proteins are not safe for those who carry food allergies and several anti-nutrients are present in plant-based foods (e.g. protease inhibitors, lectin, polyphenols and phytic acid) [START_REF] Asgar | Nonmeat protein alternatives as meat extenders and meat analogs[END_REF]. There are safety risks associated with edible insects, such as anti-nutrients (e.g. chitin and toxic substances) and allergens (e.g. allergenic cross-reactive proteins in arthropods) [START_REF] Ribeiro | Allergic risks of consuming edible insects: A systematic review[END_REF]. In addition, the ingredients and components that meat alternatives use to imitate the taste of real meat might have adverse health effect for consumers, such as heme which makes meat taste but is also well known to be associated with cardiovascular disease and type 2 diabetes. By comparison, all the processes involved in the production of cultured "meat" are highly controlled under aseptic conditions and should be under the regulation of food regulating authorities regarding food quality and safety management, without the adverse effects of foodborne pathogens, contamination, antibiotic abuse and food poisoning. Therefore an improvement in safety and healthiness might be possible (Zhang et al., 2020) even if this has to be checked. Although the USDA claimed that cultured "meat" is safe for human health [START_REF] Maler | Meat Alternatives: The State of the Industry, Barriers to Adoption, and Overcoming Barriers[END_REF], for the majority of consumers, as demonstrated by recent surveys conducted by scientists, there is an overall uncertainty which remains for the safety and healthiness of cultured "meat" (Verbeke et al., 2015;Wilks & Phillips, 2017[START_REF] Liu | Chinese consumers' attitudes and potential acceptance towards artificial meat[END_REF][START_REF] Chriki | Brazilian consumers 'attitude towards what is called « cellbased meat[END_REF]. A large body of literature discussed that food safety might be the most important factor for consumers to accept meat alternatives [START_REF] Escribano | Stated preferences for plant-based and cultured meat: A choice experiment study of spanish consumers[END_REF]. Nevertheless, safety and healthiness are complex concepts, but for consumers, potential risks can lead to a loss of trust in a novel product. In contrast with real meat, all the meat alternatives are processed foods, which are generally considered by consumers as unnatural, less safety and unhealthful [START_REF] Maler | Meat Alternatives: The State of the Industry, Barriers to Adoption, and Overcoming Barriers[END_REF], this indicates that meat alternatives still have many hurdles to overcome in terms of nutrition, safety and human health.

Animal welfare

To comply with the claimed advantages of consuming meat alternatives in terms of animal welfare, and to avoid large-scale animal rearing and slaughter, any animal component should not be involved in the production process. Accordingly, the production of plant-, insect-and mycoprotein-based meat alternatives can be perceived as ethical, since animals especially ruminants are excluded from the production process. However, to date, the use of animal-derived component such as fetal bovine serum (FBS) is still an essential component for culture meat production even if all private companies involved in this process claim it will not be the case anymore. FBS is a universal growth supplement for cell and tissue culture media and is touted as a "natural cocktail" for cell attachment, growth and proliferation since it contains hundreds of proteins and thousands of small molecule compounds (including hormones and growth factors) that are required for cell culture [START_REF] Gstraunthaler | A plea to reduce or replace fetal bovine serum in cell culture media[END_REF]. However, the use of FBS, which is drawn from fetuses collected from slaughtered pregnant cows, is incompatible with animal welfare and ethical requirements. Therefore, the development of serum-free and animal component-free medium is one of the biggest barriers for cultured "meat" to overcome and to convince the general public with the prospect of animal protection. To date, progress has been made in this area, with some culture meat companies stating that they have achieved the substitution of animal serum from the production process by artificial culture media, but no details were provided [START_REF] Fusaro | Future Meat Technologies Reduces Cultured Chicken Breast Cost to $7.50[END_REF]. Indeed, some serum-free additives and media could be commercially available in large scale due to their good performances for the maintenance of stem cells and the in vitro growth of various types of cells [START_REF] Hoang | Standardized xeno-and serum-free culture platform enables large-scale expansion of high-quality mesenchymal stem/stromal cells from perinatal and adult tissue sources[END_REF][START_REF] Meat | Growth Medium without Fetal Bovine Serum (FBS)[END_REF]. It is likely that in the near future, animal welfare concern that faced by conventional meat production might be substantially reduced by the meat alternatives with increasingly technological advancement.

Sustainability of environment, climate and nature resource

Inefficient and unsustainable production of conventional meat has long been recognized by the general public even if the scientific community is much more nuanced. However, this encourages the development of meat alternatives, which have been claimed as high-efficient, eco-friendly and sustainable. Indeed, numerous reports indicate that these alternatives are more sustainable and eco-friendly than conventional meat, while some of them are real fake news. For example, it was reported that plant-based alternatives have been shown to cut GHG emissions by up to 90% compared to conventional meat [START_REF] Bridgeman | Plant-Based Meats Are Better for Your Health and the Environment[END_REF]. However, it was also indicated that typical plant-based meat substitutes create the same amount of GHG emissions as poultry and five times the emissions of vegetables and legumes [START_REF] Springmann | Not All 'Meatless Meats' Are Good For Your Health Or The Environment[END_REF]. This reflects that rigorous scientific comparisons and studies are indeed required in this hot topic. It was reported that sometimes under specified production conditions, cultured "meat" can cut energy consumption and land use by 99%, water use by 90%, and energy consumption by 40% compared to conventional meat production [START_REF] Tuomisto | Environmental impacts of cultured meat production[END_REF]. However, it was also reported that cultured "meat" may also needs much more energy than conventional meat [START_REF] Tuomisto | The eco-friendly burger[END_REF]. According to a more rigorous study regarding the comparation of the potential climate impacts between cultured "meat" and conventional cattle production, cultured "meat" is not as environmentally friendly as it was claimed to be [START_REF] Lynch | Climate Impacts of Cultured Meat and Beef Cattle[END_REF]. There is an indeed difference for greenhouse gas emission between these two systems, conventional cattle system produces carbon dioxide (CO2), but a great amount of methane (CH4) and nitrous oxide (N2O) while CO2 accounts for the majority of greenhouse gas emissions from energy generation during cultured "meat" production. With the accumulation in the atmosphere of CO2 in a long term, cultured "meat" system will cause far more warming than conventional cattle system since CH4 does not accumulate as CO2 due to a much lower half-life. In addition, as one part of the whole ecosystem, livestock system including animal rearing and production play essential roles for agroecology and in particular with maintenance of biodiversity (of plants, insects, animals and microorganisms in the soil). In addition, the soil of permanent pastures is known for its capacity to store carbon captured from the atmosphere, thereby reducing GHG emissions from livestock. The replacement convention meat production by meat alternatives will reduce positive impacts of the livestock systems, and therefore reducing ecologically sustainable development.

In fact, due to the different dimensional considerations about resources, energy use, manufactory process, many uncertainties are remained concerning sustainability of meat alternatives. As discussed before, depending on the different scenarios of meat consumption in a long term, cultured "meat" production is not prima facie climatically superior to conventional beef production; rather, its relative impact is determined by the availability of decarbonized energy generation and the specific production processes implemented [START_REF] Lynch | Climate Impacts of Cultured Meat and Beef Cattle[END_REF]. In fact, whether all these alternatives will be as superior to conventional meat as they claimed depends largely on the use of more advanced culture and processing techniques as well as on rigorous scientific inspection [START_REF] Smetana | Meat alternatives: life cycle assessment of most known meat substitutes[END_REF]. Nonetheless, for meat alternatives, a complementary strategy with traditional meat would be a good option rather than a competition between them. The first objective of meat alternatives is to produce more high protein products that taste like meat and solve some long-established problems which include sustainability, environmental issues, health concerns and animal welfare along the way. However, there are some many obstacles before these products can be brought to market on a large scale. One of them is food neophobia by consumers, caused by insect-based and cell-based meat alternatives, the former being perceived as dirty and disgusting and the latter unnatural and poisonous. Another obstacle concerns sensorial quality, despite plant-based meat analogues can be delicious but not as tasty as real meat. A third obstacle is the production cost of any meat substitute, the high cost of producing cultured "meat" being a major obstacle, this means cultured "meat" products will be expensive, which will decrease consumers' willingness to purchase. In addition, cultured "meat", as one of the "Top 10 Emerging Technologies in 2018″ is known by the general public since 2013 when a prototype was tasted in a famous restaurant in London ; since then, many production technologies are still unsolved and are struggling with their own technological obstacles or local regulatory laws; consequently, these technologies are not yet ready for mass production, hence have yet to find a position in the market and almost impossible to replace conventional meat in the near future [START_REF] Bonny | What is artificial meat and what does it mean for the future of the meat industry[END_REF]. But in the long term, as technology advances and the obstacles are likely to be solved step by step meeting the ambitious of those meat analogues, and the position of conventional meat will continue to be threatened, therefore effective initiatives should be implemented to address the problems it has been faced for so long.

Chapter 3. Investigations of beef carcass grading and consumer sensory evaluation with the development of

Meat Standards Australia in Europe

The MSA system has been regarded as a good standard with critical steps including rigorous beef carcass grading and untrained consumer evaluation of beef eating quality. The MSA protocols of sensory evaluation for use in scientific research has been developed in Europe, and a large number of consumer tests have been conducted with more than hundreds of thousands of consumer data collected so far. To this end, the International Meat Research 3G Foundation (3G Foundation), has been established to maximize research efficiency and to benefit the global beef industry. Beef quality evaluation facilities (protocols, tools) under the framework of MSA are now widely available for carcass grading and consumer evaluation in Europe (currently mainly in several European countries such as France and Poland). The past two decades have seen the extensive use of the MSA system in Europe with large amount of data collection, data pooling and meta-analyses which have been subsequently performed for research activities. Two studies were performed in this chapter based on a huge existing European database with consumer and animal data. A more focused study was performed with dataset generated in a private French meat plant. The main issues investigated in this chapter are: 1) is it possible to estimate marbling score (one of the most important predictive inputs of beef eating quality), from European conformation and fat scores? 2) is it possible to assess marbling at the 5 th rib in comparison with the preferred grading site of the 10 th rib in the Australian context?

3) what are the relative perceptions of beef eating quality attributes (tenderness, juiciness, flavor liking and overall liking) by European consumers? The overall structure of this chapter takes the form of following three studies.

Study 1: European conformation and fat scores of bovine carcasses are not good indicators of marbling

This study investigated the relationships between marbling score and the widely used scores (conformation score and fat score) for carcass classification in Europe.

The hypotheses to be tested in this study were: Marbling score will be weakly correlated with European conformation and fatness scores. Estimation of marbling score based on carcass characteristics of conformation and fat cover will be poorly feasible.

Study 2: Are marbling and the prediction of beef eating quality affected by different grading sites?

This study investigated the possibility to assess marbling, meat color and fat color at the 5 th rib through comparing the same assessment at the 10 th rib according to the Australian Beef Chiller Assessment System (ABCAS) used for the implementation of the MSA system in a French meat plant. The predictions of beef eating quality based on carcass grading from the 5 th rib and the 10 th rib are compared.

The hypotheses to be tested in this study were:

There will be no difference of marbling score on M. longissimus thoracis et lumborum between the 5 th rib and the 10 th rib. Marbling assessment can be conducted on M. longissimus thoracis et lumborum at the 5 th rib. Marbling assessment conducted on M. longissimus thoracis et lumborum at the 5 th rib will not affect the ultimate prediction on beef eating quality score.

Study 3: Contributions of tenderness, juiciness and flavor liking to overall liking of beef in Europe

This study evaluated the contributions of tenderness, juiciness and flavor liking to overall liking that are perceived by untrained European consumers according to the MSA protocols.

The hypothesis to be tested in this study was:

For European consumers, flavor liking would likely to be the most important contributor to overall liking.

European conformation and fat scores of bovine carcasses are not good indicators of marbling

This section is the version that was published in Meat Science.

Liu, J., Chriki, S., Ellies-Oury, M. P., Legrand, I., Pogorzelski, G., Wierzbicki, J., Farmer, L., Troy, D., Polkinghorne, R., & Hocquette, J. F. (2020). European conformation and fat scores of bovine carcasses are not good indicators of marbling. Meat Science, 170, 108233.

Introduction

Beef consumption decline is one of the serious challenges for the European beef industry (review of Hocquette et al., 2018). Broadening the overseas market seems to be an effective way to sustain its beef production. Improvement of the EUROP system could place European beef products in a better position on the international beef market. While the United States, Brazil and the European Union produce roughly 47% of the world's beef, the most advanced beef grading scheme is probably the Meat Standards Australia system (MSA) (review of Hocquette et al., 2018). Indeed, in addition to the USDA (United States Department of Agriculture) grading scheme, the MSA has been considered as the most accurate and easy to use beef palatability evaluation system (Polkinghorne & Thompson, 2010;Watson, Polkinghorne, & Thompson, 2008). The integration and evolution of the beef assessment system is particularly urgent for the sustainability of the European beef industry (Pogorzelski, Woźniak, Polkinghorne, Wierzbicka, & Półtorak, 2020).

Within European countries, carcass quality is currently evaluated on the basis of the animal type (young bovine animal [male or female between 8 and 12 months of age], young bull [non castrated male between 1 and 2 years of age], bull, steer, heifer, cow), carcass weight, as well as carcass conformation and fat scores [START_REF] Konarska | Relationships between marbling measures across principal muscles[END_REF]. These two carcass scores are based on visual assessment of carcass muscling and fatness by video image analysis and/ or expert graders [START_REF] Craigie | A review of the development and use of video image analysis VIA for beef carcass evaluation as an alternative to the current EUROP system and other subjective systems[END_REF][START_REF] Rius-Vilarrasa | Genetic parameters for carcass dimensional measurements from video image analysis and their association with conformation and fat class scores[END_REF]. Furthermore, the European classification system places emphasis on the description of production yield rather than beef eating quality, carcasses with different European classification scores being assigned to differentially priced and different sales markets. In contrast, the MSA system has been more focused on beef eating quality at the level of individual cuts rather than on the basis of entire carcass [START_REF] Konarska | Relationships between marbling measures across principal muscles[END_REF]. Meat experts have realized the weakness of the EUROP grid nowadays within Europe, since European classification scores are poorly related to the eating quality at consumer level and cannot reflect carcass composition (Pabiou et al., 2011) and consumer satisfaction (Bonny et al., 2016a), and wish to improve the current situation. For instance, Monteils et al. (2017) selected five indicators to better assess European carcasses, one of them being marbling, which has been fully implemented in MSA and USDA grading schemes.

Marbling is defined as "the intermingling (intramuscular fat) and dispersion of fat within the lean meat" (Von Seggern, [START_REF] Von Seggern | Muscle profiling: Characterizing the muscles of the beef chuck and round[END_REF]. It is determined by visual assessment in M. longissimus thoracis (LT): lean meat is infused with intramuscular fat, which gives meat an appearance of distributed white flecks or streaks of fatty tissue inside the muscle fibers (Kruk, Pitchford, Siebert, Deland, & Bottema, 2002). Research proved that marbling score has a relationship with tenderness (Chriki et al., 2013), as well as with juiciness and flavor, which increase with increasing intramuscular fat content (Dransfield et al., 2003;Hocquette et al., 2011;Thompson, 2004). Furthermore, consumer studies showed a strong relationship between beef marbling and consumer satisfaction scores in Australia (Watson et al., 2008) and USA [START_REF] Felderhoff | Beef quality preferences: Factors driving consumer satisfaction[END_REF]. In summary, marbling was negatively or positively considered to be associated with carcass classification in terms of yield grade, quality grade, and beef consumption [START_REF] Janz | The effects of modified carcass chilling on beef carcass grade and quality of several muscles[END_REF][START_REF] Moon | The relationship of physiological maturity and marbling judged according to Korean grading system to meat quality traits of Hanwoo beef females[END_REF]Platter, Tatum, Belk, Scanga, & Smith, 2003;Polkinghorne, Thompson, Watson, Gee, & Porter, 2008;[START_REF] Thompson | The relationship between marbling and sensory traits[END_REF]. Along this line of consideration, following the food conference of France ("Etats Généraux de l'Alimentation, EGA"), recommendations were made to the meat sector, in particular the introduction of marbling in French beef grading schemes. Despite this initiative, due to the wide application of the EUROP grid in Europe (which is compulsory), marbling has not been widely promoted and used so far in Europe. Especially in France, conformation has been a more important component, and French consumers prefer beef with less visual fat at the retail level (Ellies-Oury, Lee, Jacob, & Hocquette, 2019).

Therefore, relationships between marbling and European classification scores should be investigated in-depth. The aim of this study was thus to investigate the relationships between marbling and the most common scores for classification of carcasses especially in Europe. If it can be proved that the European classification scores are associated to marbling, the conclusion can be made that European conformation and fat scores may be relatively useful to estimate marbling. Alternatively, an improvement of the European classification system would be needed and additional indicators like marbling should be more widely used.

Materials and methods

Data collection

The dataset, containing 711 cattle used in this study, was obtained from France, Poland, Ireland and Northern Ireland, with records for carcass weight, sex, age, ossification score, MSA marbling score at the 10 th rib, rib fat thickness, Europe fatness class and European conformation score. Data were collected as described by Bonny et al., 2016c. European conformation and fat scores were both converted into a continuous 15-point scale and all ranged from 2 to 14 in this study (Table 1) as described by (Hickey, Keane, Kenny, Cromie, & Veerkamp, 2007). Distribution of carcasses according to European classification scores is shown in Table 1, the majority of carcasses having a European conformation score of U, R and O and a fat score of 3. Distributions of carcass characteristics are presented in Table 2. With a wide range of animal chronological age (from 266 to 6133 days), the database is quite representative with a broad distribution of animal maturity.

Other carcass traits were provided by expert graders accredited by AUS-MEAT based on the MSA grading scheme for carcass characteristics such as marbling and ossification scores (Bonny, Pethick, et al., 2016a).

Statistical analysis

All the statistical analyses were performed with the R software (version. 1.1.436). Analysis of variance (ANOVA) was performed using aov() function to determine the significant differences of carcass characteristics. Pearson correlation was calculated with the ggplot2 package using the "pairs.panels" function. Principle component analysis (PCA) was performed using the package "FactoMineR" [START_REF] Husson | Multivariate exploratory data analysis and data mining[END_REF]) to find out the potential relationships and structure within the dataset and to visually represent relationships between interest variables. Multivariate regression models were developed by using the "lm" (linear model) function and "lm.beta" R package [START_REF] Behrendt | Lm. Beta: Add standardized regression coefficients to lm-objects[END_REF].

The mean-square prediction error (MSPE) and mean prediction error techniques (MPE) were used to compare the actual marbling score and the predicted marbling score, as calculated by (Rook, Dhanoa, & Gill, 1990 (1); Yan, Porter, & Mayne, 2009 (2)).

= = MSPE n Oi Pi 1 ( ) i n 1 2
(1)

= MPE MSPE o / ¯(2)
Where Oi is the observed value, Pi is the predicted value, o¯is the mean of actual value and n is the number of pairs of values of observed and predicted values being compared.

MSPE was used to reflect the differences between prediction and observation data [START_REF] Brown | Relationship between in vitro gas production profiles of forages and in vivo rumen fermentation patterns in beef steers fed those forages[END_REF]. MPE was used to describe the prediction value [START_REF] Yan | Prediction of methane emission from beef cattle using data measured in indirect open-circuit respiration calorimeters[END_REF].

Results

Carcass and fatness traits from different sex

As shown in Table 2, female cattle were divided into two groups: young and old (less and more than 987 days of age respectively) based on the threshold defined by Bonny et al. (2016b). Old females had the greatest marbling score. Young females had greater rib fat thickness score, EU fat score and conformation score than old females. Young bulls had the lowest marbling score and EU fat score compared to females and steers. In addition, there was no significant difference in marbling score, rib fat and EU fat score between young females and steers. There was no significant difference in conformation score among old females, steers and young bulls. These four cattle groups (young bulls, steers, young and old females) significantly differed in age and ossification score.

Correlations between carcass and fatness traits

Correlations between marbling score, rib fat thickness, European conformation score, European fat score, carcass weight, age and ossification score are indicated in Table 3. Relationships between these traits are represented in Fig. 1: carcass traits of fatness (rib fat thickness, European fat score) and of conformation as well as carcass weight are gathered in the first quadrant (upper right part of the plot) which indicate that all these traits are associated together. Indicators of animal maturity (ossification score and age) are gathered in the second quadrant (lower right part of the plot) indicating that indicators of animal maturity and carcass traits are almost independent. Marbling score lies between these two groups of variables indicating that marbling score actually is at least partially explained by all traits related to both animal maturity and carcass characteristics (weight, conformation and fat cover).

All these correlations are confirmed by data displayed in Table 3. Indeed, significant but slight correlations were obtained for all animals between marbling score and European fat score (r = 0.45), European conformation score (r = -0.16), rib fat thickness (r = 0.26), ossification score (r = 0.25), age (r = 0.21) and carcass weight (r = 0.15) (P < .001). For young females, marbling score had weak relationships with conformation score (r = 0.20, P < .05), and moderate relationships with European fat score (r = 0.34) and rib fat score (r = 0.33) (P < .001). For old females, marbling score had moderate associations with conformation score (r = -0.46, P < .001), European fat score (r = 0.45) and rib fat score (r = 0.47) (P < .01). For steers, marbling score had positive correlations with European fat score (r = 0.59, P < .001), rib fat score (r = 0.25, P < .01), however, no relationship between marbling score and conformation score was observed. For young bulls, low correlations between marbling score and conformation score (r = -0.27, P < .001), European fat score (r = 0.36, P < .001) and rib fat score (r = 0.18, P < .01) were observed.

There was no significant correlation between European fat score and conformation score, which confirms the different meaning of these two scores. European conformation score had a strong correlation with carcass weight for young bulls (r = 0.78, P < .001); however, for old females, conformation score had no relationship with carcass weight. On the one hand, ossification score had significantly positive correlations with marbling score for young females (r = 0.38, P < .001) and steers (r = 0.35, P < .001) but not for old females and young bulls. On the other hand, ossification score had significantly positive correlations with European conformation score for steers (r = 0.39, P < .001) and young bulls (r = 0.30, P < .001), but not for females.

Linear and multiple prediction equations for marbling using animal and carcass traits

Multivariate regression was used to assess the relationship between marbling and the most common scores for classification of carcasses. Regression equations developed from European classification scores are presented in Table 4. The linear relationships between marbling and European fat score or between marbling and conformation score used as single variables in eq. 1 and 2 were poor with very low R 2 (0.21 and 0.03). When conformation score and European fat score are combined in a multiple regression (eq. 3), the resulting R 2 (0.24) was the sum of the first individual ones (0.21 and 0.03) indicating that both effects are additive. When rib fat score was added to the first three models, the accuracy of the resulting new models (eqs. 3 to 6) was not improved except to a lower extent when rib fat thickness and conformation score were combined together: R 2 increased from 0.03 to 0.11 (eq. 5), with a significant effect of rib fat thickness in this model, but not in models 4 and 6.

Finally, a multiple regression was performed with the six available variables: European fat score, conformation score, carcass weight, rib fat thickness, ossification score and age for all cattle, females, young or old females, steers and young bulls (eq. [7][8][9][10][11][12], explaining from 21% to 46% of the total variability in marbling. With the addition of these other indicators, the precision of the explanation model for marbling was thus improved. Eq. 7, which was established for the whole cattle within all sexes, explained 32% of the variation in marbling. The equation for young bulls explained a moderate but the greatest amount of variation (R 2 = 0.46) compared to the other models (for females R 2 = 0.34, for steers R 2 = 0.21). The effect of rib fat thickness was significant in eqs. 7 (for all animals) and 8-10 (for females only). This suggests that, especially for all cattle or female cattle, rib fat depth could be considered as a partial indicator of marbling.

In Table 5, the relatively large values (0.26 to 0.45) given for MPE and relatively low values given for R 2 (0.03 to 0.46) indicated that these models were less accurate to actual marbling scores. Further these equations all relate to the same data set and would require testing across further unrelated data to establish transportability.

Discussion

Relationships between marbling score and other carcass traits

In the research of Bonny, Pethick, et al. (2016a), there might be a negative relationship between marbling score and conformation score. As in Conroy, Drennan, [START_REF] Conroy | The relationship of various muscular and skeletal scores and ultrasound measurements in the live animal, and carcass classification scores with carcass composition and value of bulls[END_REF], European conformation score had a slightly negative correlation with marbling with an r value between -0.19 (Conroy et al., 2010) and -0.16 (this study). All together, these observations indicate that, in general, there is an extremely poor relationship between marbling score and conformation score. For steers, marbling score had no relationship with conformation score. However, compared to steers, within young females and old females, low but opposite relationships between marbling and conformation score were observed (r = 0.20, and r = -0.45, respectively). This indicates that, for old females, a better conformation score is associated with a relatively lower marbling score, which is not true for young females. This explains the weak relationships between these two traits when all animals are analyzed together.

By contrast, European fat score might have a potential to indicate marbling to some extent for all sexes (r = 0.45 on average), especially for steers (r = 0.59), which indicates that a better European fat score may be associated to more marbling, especially within steers. Despite this observation, modeling approaches (Tables 4 and5), including with data from steers alone, suggest that it is difficult to predict marbling based on European conformation score and fat score.

With data from all animals, although European conformation and fat scores had non-significant association with age and ossification score, conformation and fat scores had significant positive correlations with carcass weight, especially for conformation. In addition to that, marbling was observed to be positively correlated with carcass weight. It seems that the carcass with a greater weight probably would have better European conformation, fat and marbling scores. More precisely, marbling score was positively correlated with carcass weight only for young females and steers, not for young bulls and old females. It was observed that young bulls had the heaviest carcass weight but the lowest marbling score (Table 2), whereas old females had the heaviest carcass weight and also the greatest marbling score. This is due to the fact that cattle tend to accumulate more fat when they get older [START_REF] Gettys | An assessment of the relationship between tissue growth patterns and selected hormone profiles among sex phenotypes in cattle[END_REF], especially in the case of late-maturing breeds such as the Limousine breed (Malau-Aduli, Edriss, [START_REF] Malau-Aduli | Breed differences and genetic parameters for melting point, marbling score and fatty acid composition of lot-fed cattle[END_REF], which is the main breed of old females in this study, compared to young bulls (mainly early-maturing breeds). This indicates that a heavier carcass weight might not be always associated with more marbling score across cattle groups of different sexes.

In the current study, young females had the lightest carcass weight, but the greatest European conformation and fat scores; young bulls had the heaviest carcass weight, but lower conformation score, and the lowest marbling and fat scores. This indicates that a heavier carcass also might not be always associated with greater conformation and fat scores across cattle groups of different sexes. This result is likely to be related to a combination of different hormone levels between sexes and different ages between young females (almost 24 months) and young bulls (about 18 months). Different sexes of cattle have different hormone status, which affects muscle development and fat distribution [START_REF] Fritsche | Differences in natural steroid hormone patterns of beef from bulls and steers[END_REF]. The hormone patterns of female and male cattle obviously differ with females having more insulin but less growth hormone, androgen and estradiol than bulls, steers being intermediate [START_REF] Gettys | An assessment of the relationship between tissue growth patterns and selected hormone profiles among sex phenotypes in cattle[END_REF]. Insulin is known to stimulate lipogenesis; growth hormone and androgen preferentially direct nutrient resources into muscle and away from fat tissue, increasing conformation accordingly in entire males [START_REF] Gettys | An assessment of the relationship between tissue growth patterns and selected hormone profiles among sex phenotypes in cattle[END_REF]Pethick, Harper, Hocquette, & Wang, 2006). As a result, at a similar age, bulls should have greater conformation score than young females. However, bulls were significantly younger than young females in this study. Since conformation score and age are positively correlated for young bulls (r = 0.46), the studied young bulls are expected to get a better conformation score at 24 months of age. Besides, animal breed type and country of origin should perhaps have been taken into account in addition to feeding systems to explain the above observation. Nonetheless, estradiol was proved to increase carcass weight and decrease conformation score of steers [START_REF] Enright | Effects of long-term administration of pituitary-derived bovine growth hormone and estradiol on growth in steers[END_REF]. Therefore, in this case, the sexual hormone patterns could be one of the reasons why steers had a heavier weight but lower conformation score compared to young females, when these two sex groups have a similar age. In addition, a number of studies showed that heifers are more marbled and have more subcutaneous fat than steers [START_REF] Ardiyant | Effects of GH gene polymorphism and sex on carcass traits and fatty acid compositions in Japanese black cattle[END_REF] and bulls [START_REF] Florek | The quality of meat from carcasses of young bulls and heifers classified according to the EUROP system[END_REF]. This was consistent with the conclusion indicating that female cattle are inclined to deposit more fat than males, especially bulls (Venkata Reddy et al., 2015). [START_REF] Prado | Meat quality of the Longissimus muscle of bulls and steers ½ Nellore vs½ Simmental finished in feedlot[END_REF] observed that bulls had lower fat content than steers, which is confirmed by our study, showing that for each fat-relative trait, young bulls had always the lower scores. Modzelewska-Kapituła and Nogalski (2014) reported that the conformation scores of females, steers and bulls were almost similar (7.4, 7.3 and 7.5, P > .05) when animals had a similar age of 18 months. However, in our study, steers and young bulls had similar conformation scores as old females, but these three cattle groups had a significantly lower conformation score than young females, indicating that animal maturity is not the only source of variation in conformation score. Thus, neither carcass weight nor physiological age or animal maturity alone are major criteria to determine marbling score, European conformation and fat scores.

Carcasses with more marbling have a greater rib fat score [START_REF] Moon | The relationship of physiological maturity and marbling judged according to Korean grading system to meat quality traits of Hanwoo beef females[END_REF]. This is consistent with our observations of positive correlations between marbling score and rib fat thickness within each sex group, as between marbling score and European fat score. However, young females had a lower marbling score but greater European fat and rib fat scores compared to old females. Furthermore, in our study, European fat score and rib fat thickness are not related to animal maturity for young and old females. On the contrary, marbling score had a positive relationship with animal maturity for young females (r = 0.38), but not for old females. Due to the wide variation of animal origin, breed type and practices of feeding among others, the experimental data used in this study is slightly unbalanced. Therefore, one reason for the observed discrepancies may be that old females are cull cows with a shorter finishing period (and hence a lower feeding level before slaughter) compared to young females. In addition, young females consisted of 82% late-maturing breeds compared to old females (with 56% of late-maturing breeds only). More maturity results in more marbling in cattle, but late-maturing cattle breeds develop marbling later compared to early-maturing breeds (Pethick et al., 2006). Therefore, the aforementioned discrepancy between marbling score and European fat and rib fat score for young and old females might be due to different types of breeds combined with potential different finishing periods within these two groups. Thus, greater subcutaneous fat or European fat scores does not consistently fit with more intramuscular fat, as it is for carcass weight and conformation score, since animal breeds, maturity and feeding in addition to sex play major and distinct roles in carcass characteristics.

Is it possible to estimate marbling from animal and carcass traits?

Much work so far has focused on marbling measurements: grader training, image analysis, near infrared spectroscopy (NIR) and hyperspectral imaging technology [START_REF] Konarska | Relationships between marbling measures across principal muscles[END_REF][START_REF] Velásquez | An application based on the decision tree to classify the marbling of beef by hyperspectral imaging[END_REF]. There is a strong relationship between the results of these technologies and traditional marbling scoring systems. Nevertheless, some problematic disadvantages could not be overlooked: frozen thin slices of beef need to be prepared for NIR or the carcass grading process by using accredited graders might be a huge investment of finance and labor force. On the other hand, it was assumed that the use of European fat scores may provide an easily accessible source for post-slaughter marbling estimation based on the entire carcass. Actually, in the MSA and USDA grading schemes, marbling assessment on a single cut of M. longissimus thoracis (LT), as a reference muscle, is used to provide the whole carcass marbling level. The relationship and consistency of marbling scores between M. longissimus thoracis (LT) and other muscles were confirmed by [START_REF] Konarska | Relationships between marbling measures across principal muscles[END_REF], who indicated that MSA marbling scores taken at the LT grading site had a significant strong relationship with marbling scores of other muscles. [START_REF] Konarska | Relationships between marbling measures across principal muscles[END_REF] reported that European carcass measurements were not useful in predicting marbling due to the poor relationship between European fat score and rib fat thickness, and the lack of relationship between European fat score and marbling score. In contrast to this study, which included 48 carcasses and 4 cattle breeds (Charolais, Belgian Blue, Limousin and Gascon), the current study included 711 cattle and the beef breeds were made up of Angus, Hereford, Murray grey, Shorthorn, Belted Galloway, Belgian blue, Charolais, Blonde d'Aquitaine, Limousin, Montbeliarde, Romagnola and Simmental. The dairy breeds were made up of Holstein, Ayrshire and Normande. Consequently, more robust results could be observed: European fat score showed a moderate but significant correlation with marbling score (r = 0.45) and rib fat thickness (r = 0.51).

In this study, carcass weight, age, ossification and animal sex were taken into consideration to predict marbling, in order to ensure that all the analyses were relevant for each cattle group. Rib fat thickness was included to study the relationships with European fat and marbling score. We observed a slight association between marbling score and rib fat thickness (r = 0.26), as previously shown by [START_REF] Indurain | The relationship of carcass measurements to carcass composition and intramuscular fat in Spanish beef[END_REF] (r = 0.29). However, in combination with European conformation and fat scores, rib fat score did not have a significant effect on marbling estimation, whereas it has a significant effect in combination with conformation score alone. Compared with rib fat thickness, European fat score played a more significant role in marbling estimation (eq. [4][5][6]. Our results for females also indicate that greater scores of rib fat thickness and EU fat are associated with marbling level only when females are young. For older females, in addition to these two traits, a low conformation score is also associated with more marbling as in steers (eq. 11) and in young bulls (eq. 12). The expression of marbling is due to maintained fat synthesis in combination with declining muscle growth (Pethick et al., 2006). Therefore, if conformation score could be considered as an indicator of muscle growth potential (i.e. animals with no decline in muscle growth), which is associated with a delay in the accumulation of intramuscular fat, the negative correlation between conformation score and marbling score could be explained. However, this is valid only when fat synthesis is still maintained (i.e. when animals are not finished yet such as young bulls or not finished at all such as old cows, which are most often cull cows). The greatest accuracy for marbling prediction (46%) is for young bulls, indicating that marbling score of bulls is more related to European classification scores. Overall, taken into consideration the wide variation of the animal origin in this study, many confounding factors (such as age, country of origin, feeding system, especially breed type) may explain our results. [START_REF] Dow | Prediction of fat percentage within marbling score on beef longissimus muscle using 3 different fat determination methods[END_REF] determined fat percentage by using marbling score as an indicator. As marbling increased, total fat increased as well [START_REF] Dow | Prediction of fat percentage within marbling score on beef longissimus muscle using 3 different fat determination methods[END_REF], and vice versa according to the relationship between fat scores and marbling score. In this study, the hypothesis that European fat classification could have a strong relationship with marbling was very minimally supported by the results. European classification scores only explain a slight (32%, 46%, 34% and 21% for the entire cattle group, young bulls, females and steers, respectively) proportion of the variance in marbling estimation. It is clear that in most cases, the predictive ability and accuracy of European classification scores were poor, the greatest R 2 (46%) for young bulls can only barely be taken into consideration. Additionally, according to previous prediction studies, a good accuracy should be considered when a prediction model has an MPE < 0.10 [START_REF] Soulat | Prediction of beef carcass and meat traits from rearing factors in young bulls and cull cows[END_REF][START_REF] Yan | Prediction of methane emission from beef cattle using data measured in indirect open-circuit respiration calorimeters[END_REF], whereas the MPE value of 0.26 to 0.45 in this study cannot be considered accurate enough. Thus, these less accurate indication models make it difficult to rely on European classification scores to estimate marbling score. In other words, it is senseless to estimate beef eating quality based on carcass characteristics such as conformation or European fat scores.

Within Europe, beef carcasses are valued mainly based on carcass weight and European classification scores. Al-Jammas, Agabriel, Vernet, and Ortigues-Marty (2016) demonstrated that changes in carcass chemical composition (fat and protein) can be predicted from subcutaneous fat and carcass weight. However, it was confirmed that European conformation and fat scores are poorly related to changes in weights of carcass muscle and adipose tissue (Al-jammas, Agabriel, Vernet, & Ortigues-Marty, 2015). All together, these results suggest that conformation and fat scores are not good indicators of carcass tissue weight and marbling, an important quality indicator. Thus, marbling could not be estimated from European carcasses classification scores. It is also imperative to realize the importance of more accurate beef eating quality indicators for the European beef grading scheme and introduce those indicators (such as marbling) into the European beef grading system.

Conclusion

European classification scores only explain a slight (32%, 46%, 34% and 21% for the entire cattle group, young bulls, females and steers, respectively) proportion of the variance in marbling assessed on M. longissimus thoracis (LT). An indication model should be accurate enough and easily implemented. The lack of any stronger association between marbling and European classification scores suggests that it is currently not possible for the European beef industry to estimate marbling from carcass characteristics. Proper adjustment and integration along with a more accurate beef grading scheme such as the MSA model would be a better choice for the European beef market. Consequently, the EUROP grid could become more accurate and effective. A useful beef grading scheme will help to address decreasing beef consumption, provide consumers with a better and more consistent beef eating quality and guarantee better grade beef production at known value points.

Are marbling and the prediction of beef eating quality affected by different grading sites?

This section is the version that was published in Frontiers in Veterinary Science.

Jingjing Liu, Grzegorz Pogorzelski, Alix Neveu, Isabelle Legrand, David Pethick, Marie-Pierre Ellies-Oury & Hocquette, J. F. (2020). Are marbling and the prediction of beef eating quality affected by different grading sites?. Frontiers in veterinary science, 8.

For the European abattoirs, the preferred carcass grading site is at the fifth rib, and cutting at the tenth rib as in Australia could lead to a lower economic value of the carcass. Therefore, the objective of this study was to compare the grading scores of marbling and the meat and fat color on Musculus longissimus thoracis et lumborum (LTL) at the fifth and the tenth thoracic vertebrae. The consequences on the prediction of beef eating quality using the Meat Standards Australia (MSA) grading scheme were also evaluated for cull cows, which produce the majority of beef consumed in France. Carcasses from 208 French cattle, mainly Limousine cows, were graded according to the Australian Beef Carcase Chiller Assessment System (ABCAS) used for the implementation of the MSA system. The results indicate that there was no significant difference in the marbling score, between the fifth and the tenth ribs and hence in the MSA index and in the Global Quality [meat quality (MQ4)] scores calculated from marbling values from either the fifth rib or the tenth rib. However, the meat color at the tenth rib was significantly darker than that at the fifth rib (p < 0.01), and the fat color at the tenth rib was significantly yellower than that at the fifth rib (p < 0.001). The results of this study suggest that the grading of marbling can be conducted on M. LTL at the fifth thoracic vertebrae for routine use of the MSA system in France and, more generally, in Europe. However, further investigation and adjustment would be needed for other critical MSA scores (such as rib fat thickness) while respecting the European carcass quartering practices.

INTRODUCTION

A regular decline in beef consumption has become a big challenge for the European beef industry (1). France, the largest beef producer in Europe, has also experienced a decreasing trend in beef consumption (2). However, despite the declining consumption, FranceAgriMer (3) reported that the household interest in premium beef was growing. Ellies-Oury et al. ( 4) also demonstrated that an eating quality guarantee scheme would be of interest to French consumers.

To date, the most advanced beef grading scheme is probably the Meat Standards Australia (MSA) system, which has been known as the most well-established beef eating quality guarantee system (1). The aim of the MSA grading system is to ensure that, when consumers purchase a cut of beef, it will have the eating quality promised by the MSA label when it is cooked according to the recommended method (5). The meat quality (MQ4) score was developed to rank the potential eating quality of individual muscle cuts, and the MSA index is used to assess the average eating quality across the whole beef carcass (6).

In France, where 61% of the beef consumption originates from cull cows (7), the "Label Rouge" quality sign has been used to ensure the eating quality of cuts for the consumer, especially for beef produced from the late-maturing breeds (such as Charolais or Limousine breeds). Recently within the framework of the French national food conference (8), the French meat sector represented by Interbev decided to increase the proportion of labeled beef using the "Label Rouge" and other quality signs. The purpose is to assist consumers to make purchase decisions with reliable label guidance (4), which is consistent with the aim of the MSA grading scheme. Indeed, the latter is a reliable description system of eating quality that could form a basis for retail pricing and generate product confidence for consumers (9).

Subsequently, several research efforts have been conducted in Europe to disseminate the MSA methodology as a reference (10)(11)(12) under the auspices of the United Nations Economic Commission for Europe (UNECE) (13). Being one of the critical steps in the MSA system, carcass grading parameters contribute to the basis of the beef palatability prediction model (14). Carcass grading is performed under the guidance of the Australian Beef Carcase Chiller Assessment System (ABCAS). According to ABCAS, marbling, meat color, and fat color can be assessed on Musculus longissimus thoracis et lumborum (LTL) at any ribbing site from the fifth to the thirteenth rib (15) for cattle routinely slaughtered in Australia, most of which are young steers and heifers. This type of research is still lacking with regard to old cows and/or mainly late-maturing breeds for any potential application of the MSA in the European countries, particularly France.

In general, the most common grading site used by abattoirs in Australia is from the tenth rib to the twelfth rib (16). In contrast, in the European system, quarter carcasses are sometimes sold to the market without further processing. Consequently, the quartering site in most cases is at the fifth thoracic vertebrae, and so the cutting at the tenth thoracic vertebrae would often negatively influence the economic value of the hindquarters.

Thus, the present study aimed to investigate any difference in LT marbling scores, which were collected according to the ABCAS procedure between the fifth and the tenth thoracic vertebrae along with the meat color and the fat color. The potential impact of the different grading sites on the prediction of beef eating quality (through MQ4 scores for each cut and the MSA index for the whole carcass) was also examined. To complete the previous research with young animals from early-maturing breeds (15), this study was mainly conducted with cull cows (which are the major source of beef in France and, thus, have a strong economic significance) from a famous and highly distributed late-maturing breed (the Limousine), producing beef that were already commercialized with the "Label Rouge" quality sign.

MATERIALS AND METHODS

Animals and Experimental Design

The data used in this study were from 208 carcasses (including 157 Limousine breeds) provided by a commercial slaughterhouse in Limoges, France. All carcasses were assessed 24 h after postmortem. Carcasses were graded by using a single MSA-accredited grader for at least 20 min after the cutting to allow the meat to bloom. Assessments, primarily at the tenth rib site and the secondarily at the fifth rib site, were carried out by using the same grader, who is highly experienced with carcass grading according to ABCAS specifications. He is also an official grading trainer recognized by AUS-MEAT. The conditions of the carcass and the environment, including ribbing height and angle and grading practices, such as light angle, are consistent for all assessments conducted at the two rib sites. For the experiment, the AUS-MEAT marbling, MSA marbling, meat color, and fat color were assessed at the fifth and the tenth rib of the same half carcasses (16). The basic information of the current samples is presented in Table 1. European conformation and fat scores were both converted into a continuous 15-point scale as described in the previous study (17).

Data Collection

All assessments were conducted by following the specifications of the ABCAS and the AUS-MEAT Reference Standards, which include the ossification score at the carcass level, as well as the assessment of marbling, meat color, and fat color on M. LTL.

The AUS-MEAT marbling score reflects the number of marbling, ranging from 0 to 9 in increments of one. The MSA marbling score is used to provide a more precise marbling scale in comparison to the AUS-MEAT and is based on the United States Department of Agriculture system (18): it provides scores ranging from 100 to 1,190 in increments of 10. The MSA marbling score indicates not only the amount of marbling but also the size, fineness, and distribution of fat inclusions in muscles (19).

The fat and meat colors are scored according to the AUS-MEAT scale. Fat color is from 0 to 9. Meat color is from 1A, 1B, 1C, and then from 2 to 7 (which was converted into the following scale in this study: 1, 1.33, 1.66, 2 to 7).

Prediction of MQ4 Scores and the MSA Index

The MSA grading scheme allows the calculation or the prediction of a single palatability or MQ4 score that describes the complete eating experience of a consumer. It is defined as the combination of four sensory traits, namely tenderness, juiciness, flavor liking, and overall liking (20).

The MSA prediction model allows the prediction of MQ4 scores of individual muscles from the carcass for a range of aging time, hanging method, or cooking techniques using a multiple regression approach. The parameters used to predict MQ4 include, among others, animal sex, carcass weight, hanging technique, hump height, ossification score, marbling score, rib fat depth, ultimate pH, and days aged (20). In this study, MQ4 scores were predicted for three cuts [called CUB045 [M. longissimus Hump height (cm) 204 6.9 thoracis (LT)], STA045, and STP045 (M. LTL) in the MSA grading scheme] that represent different portions of M. LTL, where the marbling scores have been recorded. All carcasses were assumed to be Achilles hung (the most common method) and the cuts were assumed to be aged for 10 days and grilled. The prediction of MQ4 scores was made twice for each cut with the same inputs except for marbling scores, which were recorded either at the fifth rib or at the tenth rib, the predictions were made using the SP2009 version of the MSA model. Then, the MSA index was predicted at the carcass level using the same inputs. The MSA index corresponds, by definition, to the sum of the predicted MQ4 scores of all MSA cuts, the weight of each was calculated as the percentage of the total weight of the MSA cuts in the carcass. To enable a standardized index reporting, inputs are standardized for the calculation of the MSA index: all carcasses are assumed to be Achilles hung (the most common method), and all cuts are assumed to be aged for 5 days and cooked according to the most common cooking method for each cut (6). In this study, the MSA index was predicted twice with the same inputs except for marbling scores, with values recorded either at the fifth rib or at the tenth rib; the predicted outputs are with respect to the aging times of 10 and 20 days, the cooking methods of grill and roast, and the hanging methods of tenderstretch and Achilles tendon.

Overall, the MQ4 scores and the MSA index for 164 carcasses were predicted in total due to some missing data. A precise description and visual support of the MSA methodology and the prediction of MQ4 (MQ4 scores and MSA Index) are indicated in the study of McGilchrist et al. (6), Polkinghorne et al. (14), and Bonny et al. (20).

Statistical Analysis

All statistical analyses were performed by using the R software (version 3.5.2). Significant differences between means of raw data, MQ4 scores, and the MSA index for the two grading sites were determined by using an ANOVA with "aov" function.

A Pearson's correlation analysis of raw data, MQ4 scores, and the MSA index were performed by using "stat_cor" and "pairs. panels" functions to determine correlation coefficients between the carcass characteristics at both the sites (fifth and tenth ribs).

Linear regression models for MQ4 scores using two sets of marbling scores from the fifth rib and the tenth rib were done by using "lm" (linear model) function.

Scatter plots were made by using "ggscatter" function with "add reg.line" and "stat_cor".

RESULTS

Grading Scores, Predicted MQ4 Scores, and MSA Index Grading scores of the AUS-MEAT marbling, MSA marbling, meat color, and fat color which were assessed on the LT muscle at the fifth rib and the tenth rib are presented in Table 2. There were no significant differences between the values of AUS-MEAT and the MSA marbling scores at two different locations. In contrast to marbling, there were significant differences in the meat and the fat color between the fifth rib and the tenth rib. The meat color at the tenth rib was significantly darker than that at the fifth rib (p < 0.01). The fat color at the tenth rib was significantly yellower than that at the fifth rib (p < 0.001) (Table 2).

In addition to the marbling scores, no significant difference was observed between the fifth rib and the tenth rib for the predicted MQ4 scores of three muscle cuts, namely M. LT, M. LTL at the anterior striploin piece, and M. LTL at the posterior striploin piece (Table 2). Similarly, no significant difference was observed for the predicted MSA scores for other cuts (Table 2), as well as for the MSA index calculated from the MQ4 of the different cuts of the carcass (Table 2).

Correlations Between Carcass Characteristics and Grading Scores at the Two Grading Sites

Table 3 presents the correlations between the ossification score (which reflects animal maturity) and the ribeye assessment scores (AUS-MEAT marbling, MSA marbling, meat color, and fat color) either at the fifth rib or at the tenth rib.

Strong relationships for the marbling score were observed either for AUS-MEAT measurements and MSA measurements. Indeed, the correlation coefficients between the fifth rib and the tenth rib range from 0.74 to 0.91 (p < 0.001).

Furthermore, there was a moderate correlation for the meat color between the fifth rib and the tenth rib (r = 0.43, p < 0.001). A significant correlation for fat color between the fifth rib and the tenth rib was also observed (r = 0.70, p < 0.001). In addition, the ossification score had a significant and positive correlation with fat color scores at both the fifth and the tenth ribs (r = 0.39, r = 0.36, p < 0.001). However, ossification was not significantly associated with meat color.

Relationships of the MSA Index and MQ4 Scores Predicted From Marbling Scores Between the Two Grading Sites

For further implementation of the MSA grading scheme based on the grading at the fifth rib, it is crucial to determine the strength of the relationships between the MQ4 scores predicted by using the marbling score at the fifth rib or at the tenth rib. The results of the correlation analyses between the MQ4 scores for 10 cuts with 4 output combinations of the cooking method with hanging method, and with aging time are presented in Table 4. In addition, very small differences in the MQ4 scores between the fifth rib and the tenth rib can be observed as shown in Table 4. Also, the results of correlation analyses for the MSA index and the MQ4 score mainly for the three LT muscles are shown in scatter plots (Figure 1). Correlation coefficients were very high (from 0.88 to 0.98) and almost similar among the different output groups. For OUT005, extremely strong correlations (r = 0.98, p < 0.001) between the MQ4 scores were observed for the four groups. For CUB045, STA045, and STP045, strong and similar correlations between the MQ4 scores were observed by using the marbling inputs from either the fifth rib or the tenth rib, and with different aging times or hanging methods, the similar distribution and strong correlation for the MSA index and the MQ4 scores between the two grading sites can also be seen in Figure 1. Similarly, significantly strong relationships were observed for the MQ4 scores between the two grading sites for all other cuts with different output combinations, and the distribution and correlation are assumed to be the same strong as the three LT muscles. In addition, whereas the correlations between the two sets of MSA marbling scores was 0.79 (p < 0.001), there was a very significant strong correlation between the MSA indexes predicted from the marbling score of the fifth rib and the tenth rib (r = 0.97, p < 0.001) (Figure 1). Linear regression was used to determine the contribution of the marbling score either from the fifth rib or from the tenth rib to explain the variability in the predicted MQ4 score. As shown in Table 5, for one individual cut, the MQ4 scores for the two models regressed by using the marbling score at the two grading sites are almost similar. For STA045 and STP045, the coefficients of determination (R 2 = 0.45, R 2 = 0.50) are relatively higher in comparison to other cuts, which are understandable since the marbling assessment was carried out on the M. LT. By contrast, a much lower R 2 -value can be found for the other cuts, such as RMP131 and EYE 075 (R 2 = 0.02).

DISCUSSION

Marbling Scores at the Fifth and the Tenth Ribs

The marbling score was developed to estimate the intramuscular fat in the ABCAS system to be used in the MSA grading scheme with the ultimate objective to ensure the eating quality at the consumer end (20). In 2018, within the framework of the French national food conference (8), the French meat sector represented by Interbev recommended introducing the marbling score into the French beef grading scheme. As a result, the marbling assessment has been gradually introduced in one of the French local meat plants for the premium beef brand called "Or Rouge" based on the late-maturing Limousine breed. In this study, no significant difference in the marbling score was observed between the fifth rib and the tenth rib sites. In addition, the marbling scores assessed at these two sites were quite equally distributed up to 400, and higher levels were also observed. The level of marbling in French cattle is typically much lower than that in Australia (21), but the marbling level of the current sample is not very low and the marbling level between the two studied sites is indeed similar. In addition, we observed a strong relationship between the AUS-MEAT marbling score and the MSA marbling score, which suggests a strong consistency of the marbling score between the AUS-MEAT measurement and the MSA measurement [r = 0.88 (between MSAMB and AUSMB at the fifth rib), r = 0.91 (between MSAMB and AUSMB at the tenth rib)], as well as between the fifth rib and the tenth rib [r = 0.77 (between the AUSMB scores at the fifth and tenth ribs), r = 0.79 (between the MSAMB scores at the fifth and tenth ribs)]. These findings are consistent with that of Kruk et al. (22), who reported a high association between the AUS-MEAT marbling score and the MSA marbling score (r = 0.76). Similar to our results, Cook et al. (23) also found that the marbling scores from the thirteenth thoracic vertebrae to the fifth lumbar vertebrae were similar. Taylor and Johnson ( 24) observed that the intramuscular fat content at the fifth rib was slightly higher than that at the tenth rib, whereas the marbling scores between the fifth rib and the tenth rib were almost the same. In addition, it was found that the marbling score of M. LT highly correlated with that of M. longissimus lumborum (LL) (r = 0.83) (25). Our findings of marbling consistency between the different ribs were obtained by using the grader assessment. Indeed, when the graders score the carcasses, a small difference in marbling has been found across the different muscles and especially between STR045 and CUB045, in comparison with near infrared spectroscopy (NIR) measurements (25). Indeed, recent developments in the marbling assessment tend to use instrument-grading systems, which are likely to be more precise in comparison to human carcass graders. With this new type of technology, Acheson et al. (26) observed that the marbling score decreased from the thirteenth thoracic vertebrae to the fifth lumbar vertebrae. The contradictory results from the human graders may be due to the different marbling grading processes. In study by Acheson et al., a computer vision system, cold camera, and proprietary software were used to assess marbling, which seems to be a more objective and repeatable method in comparison to the assessment provided by the carcass grader. Nonetheless, Schulz and Sundrum (27) observed that the marbling scores at the tenth, eleventh, twelfth, and thirteenth ribs were strongly correlated (r = 0.80-0.89) by using a camera grading technology.

Marbling Is One of the Most Important Traits for Beef Eating Quality

Beef quality has a multifactorial determinism as shown by the various inputs in the MSA model (i.e., ossification and marbling scores, cut, aging time, hanging method, cooking method, and even use of hormone growth promoters). However, as most of these factors are fixed in this study (one aging time, one hanging method, and a limited range in the ossification score), variability in the marbling score becomes an important trait, which significantly contributes to beef eating quality.

In Europe, the main factors of carcass grading are the European conformation score and the fat score, which are compulsory (shown in Table 1) and therefore routinely used by the European beef industry. However, there is very poor and/or no relationship between the European classification scores and marbling, as well as with beef eating quality (28,29). By contrast, marbling is not widely measured in Europe, particularly in France, except in some cases of "Label Rouge" animals (Label Rouge is officially a national label for food, non-food, and unprocessed agriculture products in France). In the slaughterhouse where this work was undertaken, the measurement of marbling was done for the beef premium brand of the Beauvallet Company "Or Rouge" based on the Limousine breed. Furthermore, high-marbled beef does not seem to be welcomed by the French consumers. Indeed, according to a beef consumption survey conducted in France ( 4), the low willingness to purchase beef for a quarter of the respondents is mainly due to their concerns of health risks caused by excess fat content, but marbling does contribute to eating quality. However, the willingness to purchase beef among French consumers decreased from 70% (before tasting) to 55% (after tasting) when beef samples were low-marbled the willingness to purchase beef among the same group of consumers increased from 30% (before tasting) to 80% (after tasting) when beef samples were high-marbled (30). This indicates that, even if a highly visible fatty meat seems unacceptable, the better eating quality of highmarbled beef could meet their eating expectations.

Most of the American and Australian consumers also prefer the visual appearance of low-marbled beef (31,32). However, with efforts to increase the popularization of the relationships between intramuscular fat and eating quality, consumers started to change their mind and embrace fattier meat and even highmarbled beef (33). Consumers from Asian countries, particularly in Japan, are well-known to enjoy high-marbled beef, but Japanese consumers do also enjoy moderately marbled beef (34). Although the preferences of consumers for marbling levels differ across various countries, marbling is undoubtedly one of the multiple traits that highly contribute to beef eating quality (35), but its contribution to eating quality seems to vary according to the muscle as predicted by this study (Table 5).

Intramuscular fat deposition, and therefore marbling, depends on many factors such as nutrition, genetics, and, to a lower extent, animal maturity (36,37). However, no correlation or a weak correlation (only with the MSA marbling score at the fifth rib) was found between the ossification score and the MSA marbling score (Table 2). This may be due to the carcasses graded in this study, which were from old animals (cows) of the Limousine breed and of similar age. This breed is a late maturing one and, more importantly, produces low-marbled beef (38). However, even the late-maturing cows develop more marbling than younger counterparts as they become older and physiologically mature. In fact, the marbling level is influenced by various factors such as expression and the presence of cellular factors (39). The processes determining the development of marbling of mature cows are poorly studied and require further investigation. The factors such as genetics, whole body fatness, energy intake previous to slaughter, and lifetime fat turnover associated with raising calves are potential subjects for future research.

Meat Color Characteristics at the Fifth Rib and the Tenth Rib

From a retail point of purchase, the meat color is one of the most critical traits for consumers to purchase beef (40). Various factors, such as diet, pH, and muscle type, and characteristics affect the meat color (41). The meat color depends on the ultimate pH that gradually increases from lumbar to thoracis, the pH at the fourth rib (LT) being higher than at the eleventh rib (LL) (42). Accordingly, LT should be darker than at the lumborum vertebrae (43). Contrary to this assumption and the previous observations in Australia with other animal types (15), the present study showed that the muscle at the tenth rib (LL) was significantly darker than the muscle at the fifth rib, suggesting the involvement of other factors.

The meat color also partly depends in part on the muscle fiber type (44). Indeed, oxygen diffusion is related to the muscle fiber type and results in more or less oxymyoglobin (45). The proportions of type I and type II A fibers in LT are higher than those in LL, the proportion of type II B fiber in LL is higher than that in LT (46). However, oxidative fibers (I and IIA) are known to have a decreased rate in the extent of postmortem pH decline and lightness and inherently have an increase in redness due to a higher myoglobin concentration, thus resulting in darker meat when compared to glycolytic muscles (type II B) (47). In this way, the LT muscle is expected to be darker than the LL muscle. The current finding indicates that the meat color of LT at the tenth rib was darker than that at the fifth rib. Even though the tenth rib is close to the lumborum vertebrae, the muscle on the fifth rib and the tenth rib is still the LL muscle. The fiber type is therefore unlikely to be a reason for the observed color difference.

Practically, the meat color is not used in the current MSA grading system, but with it being used as a threshold in the old MSA system, the meat color should be three or less than three, and the carcass with a meat color more than four had to be rejected by the system. The proportion of the meat color higher than four at the fifth rib and the tenth rib is calculated in this study, and is 18% and 29%, respectively. In this way, the meat color assessment conducted at the tenth rib would have increased the possibility of being rejected in the older MSA system for some of the carcasses.

Fat Color Characteristics at the Fifth Rib and the Tenth Rib

The fat color is of practical importance for the beef industry since purchase willingness of the consumer is affected by the color, the white fat color being more desirable than the yellow fat color in many countries (48,49). In the European market, too much yellow fat on the carcass is considered unacceptable (50). The fat color depends on age, gender, genotype, and nutrition. The yellowness is mainly explained by carotenes accumulating in fat tissue (48).

Whereas, Meat Livestock Australia indicates that carcasses may be ribbed at any site between the fifth rib and the thirteenth rib for grading (15), in the current study, the fat color score (3.5) at the tenth rib was significantly higher (and therefore yellower) than that at the fifth rib (2.5). Acheson et al. (26) reported that animals deposit intramuscular fat from the anterior to the posterior along the vertebrae. It is, therefore, possible to speculate that the significant difference in fat color between the fifth rib and tenth rib may also be due to a different accumulation rate of carotenoids between the posterior and anterior intermuscular fat. With an increase in maturity, more carotenoids could be concentrated at the tenth rib than that at the fifth rib. The significantly positive correlation observed between the fat color and the ossification score may also support the above hypothesis. This result supports the evidence that adipose tissues become more yellow as animal maturity increases (51). Moreover, Moon et al. (52) indicated that high-marbled beef tended to have a lower yellowness fat color due to the dilution of pigments in more fat, which is in line with a significantly negative correlation between fat color and marbling (Table 3).

MQ4 Scores and the MSA Index at the Fifth Rib and the Tenth Rib

The MSA beef eating quality score (MQ4) is a combination score of tenderness, juiciness, flavor liking, and overall liking for the individual cuts with a defined hanging method, aging time, and the cooking method (14). Marbling, as one of the input parameters, is used to predict the eating quality score (MQ4) of the individual cuts in the MSA cut-based model (20). The MSA index is a global score of the average eating quality and the potential merit of a whole beef carcass, which is calculated from the predictive eating quality scores of 39 MSA cuts. To further confirm the feasibility that the marbling assessment could be conducted at the fifth rib and not influence the eating quality prediction of individual cuts and the whole carcass, correlation analyses were performed with ten cuts to evaluate the relationships between the MQ4 scores at the fifth rib and the tenth rib. The results in the correlation analyses and scatter plots evidenced high and significant correlation coefficients and a similar distribution of the scores, demonstrating that the marbling assessment at either the fifth rib or the tenth rib has an extremely low impact on the prediction of the MQ4 scores for each cut. In addition, similar and strong correlations between the MQ4 scores predicted from the marbling scores recorded at the two ribs were observed for different hanging methods, aging times, and cooking methods. Moreover, the two MQ4 score models regressed by using the marbling score for one specific cut were found highly similar between the two marbling assessment sites (Table 5). Furthermore, we observed the same mean value of the MSA index (52) with no statistical difference (p = 0.92, Table 2) and significantly strong correlations between the MSA indexes predicted from the marbling scores from the fifth rib and the tenth rib (r = 0.97, p < 0.001, data not shown). All of these results indicate that the marbling assessment at the fifth rib or the tenth rib has no or very little impact on the prediction of the MQ4 score and the MSA index for different production and process combinations.

Another interesting finding is that the correlation coefficients between the MQ4 scores of the two grading sites was extremely high for some cuts, such as OUT005, i.e., higher than STA045 and STP045, while the regression model of OUT005 has a very low explanatory power (R 2 -value) when using the marbling score to explain the variability of the MQ4 score. This is in-line with the fact that the marbling score measured on M. LTL might have less impact on the prediction of the MQ4 scores for other cuts due to a low or moderate correlation of the marbling scores between different cuts (25). In summary, without considering the influence of other MSA predictive parameters on the prediction of the MSA index and the MQ4 scores, it is feasible to assess the marbling at the fifth rib to routinely predict the MSA index and the MQ4 scores for French cattle.

CONCLUSION

This study has shown that there is no difference in the marbling scores determined by using the accredited trained graders according to the ABCAS protocol, between the fifth rib and the tenth rib, as well as in the predicted MSA index and the MQ4 scores from these two sets of values. This confirms that the marbling score could be determined at the fifth rib by using the accredited trained graders, i.e., where carcasses are generally quartered in Europe. In contrast, meat color and fat color, not taken into the MSA model to predict eating quality of beef, are significantly different between the two grading sites. Given that Limousine cows are an important source of beef in France and that a little detailed work regarding the marbling distribution has been undertaken using cull cows, the current study is considered to be relevant to provide practical recommendations to the European (and especially the French) beef industry. Thus, this work supports potential implications in favor of the MSA implementation for the late maturing and low-marbled cattle breeds, such as Limousine, and also for potential MSA implementation in French beef plants. However, further work is needed to completely study the implementation of the ABCAS carcass grading system with respect to other critical carcass factors according to the MSA methodology while following the European carcass quartering practices.

Introduction

Beef palatability is characterized by inconsistency and variability (Chriki et al., 2013). As a result, beef producers and retailers fail to guarantee eating quality and deliver consistent palatability to meet consumers' demand as shown in France (Legrand, Hocquette, Polkinghorne, & Pethick, 2013). Meanwhile, consumers have difficulty in selecting beef with a satisfying palatability according to beef appearance [START_REF] Mckinna | Product description and labelling system research summary[END_REF]. This inability is considered as one of the main reasons for the decline in beef consumption (Polkinghorne, Philpott, et al., 2008a;Hocquette et al., 2018). Indeed, the consistency of beef palatability has been considered as a key point for the beef consumption improvement. However, the consistency of eating quality does not always meet consumer expectations for a high price product (Hocquette et al., 2018). Estimation of consumers' overall preferences would be a first step toward the satisfaction of consumers, taking into account the variability in beef quality. Accordingly, in Australia, the Meat Standards Australia (MSA) system has been innovated, in 1996, to address the above issues through a 'cut by cooking method' beef palatability prediction model (Polkinghorne, Thompson, Watson, Gee, & Porter, 2008b;Watson, Gee, Polkinghorne, & Porter, 2008a). Quantifying consumer sensory experience within a broad range of cuts and cooking methods is of importance for any beef palatability evaluation system (Bonny et al., 2017). This is especially true for the European beef industry, since there is still no reliable beef grading scheme for palatability in Europe. The current beef grading system in Europe is the EUROP grid, which is compulsory and focused on production yield rather than eating quality estimation. Therefore, the introduction of a consumer-based beef eating quality guarantee system would be a wise choice for the European beef industry [START_REF] Verbeke | European beef consumers' interest in a beef eating-quality guarantee: Insights from a qualitative study in four EU countries[END_REF] T reliable guarantee scheme including eating quality indicators could be of interest for French consumers, as well as European consumers (Ellies-Oury, Lee, Jacob, & Hocquette, 2019).

In the early meat sensory work undertaken, MSA utilized a series of line scales to measure traits related to consumer beef perception. Finally, a standard consumer score sheet (with the most principle components reduced to four line scales: tenderness, juiciness, flavor liking and overall liking), was developed (Watson et al., 2008b). Meanwhile, concerns regarding variability in sensory quality have received great attention. Many researches have focused on beef palatability and reached consensus that overall beef eating quality could be explained by three sensory traits: tenderness, flavor liking and juiciness (O'Quinn, Legako, Brooks, & Miller, 2018). The contribution of each of these traits to overall liking has been intensively investigated. According to initial findings, tenderness was thought for decades to play the most important role on beef overall palatability in the United-States [START_REF] Miller | Consumer thresholds for establishing the value of beef tenderness[END_REF]Miller, Huffman, Gilbert, Hamman, & Ramsey, 1995). However, in recent years, several studies suggested that flavor liking was as important as tenderness. [START_REF] Neely | Beef customer satisfaction: Role of cut, USDA quality grade, and city on in-home consumer ratings[END_REF] indicated that flavor and tenderness had equal contributions to beef overall liking for American consumers. Furthermore, additional studies proved that flavor liking contributed more to overall liking than tenderness for American consumers (Killinger, Calkins, Umberger, Feuz, & Eskridge, 2004;[START_REF] Behrends | Beef customer satisfaction: Factors affecting consumer evaluations of calcium chloride-injected top sirloin steaks when given instructions for preparation[END_REF]., O'Quinn et al., 2018). Actually, these findings were also confirmed by [START_REF] Huffman | Effect of beef tenderness on consumer satisfaction with steaks consumed in the home and restaurant[END_REF] with US consumers. In this study, consumer testing was conducted in consumers' home and restaurants. The result showed that flavor liking accounted the most to variability (67%) in beef palatability in home evaluation. By contrast, tenderness accounted for the most variation (56%) of the overall liking in restaurant evaluation. Nevertheless, 51% of consumers still regarded tenderness as the most important trait, versus 39% regarding flavor. In addition to that, the rating model of tenderness was the most precise one (79%) for beef palatability determination, flavor liking and juiciness rating models were less precise at 64% and 69% respectively (Platter et al., 2003). It seems that flavor liking is dominant in the formation of overall liking, but tenderness still plays a determinant role in consumer's subconscious perception. Therefore, it is important to investigate the role of each sensory parameter in the construction of overall liking over time.

Over the past 25 years, in view of the highest contribution of tenderness, considerable effort was directed toward improving beef tenderness over years. As a result, great improvements in beef eating quality have been achieved [START_REF] Savell | 2015/2016 National beef tenderness survey-executive summary[END_REF]. O'Quinn et al. (2018) indicated that the improvement of tenderness might be the reason why consumers pay more attention on flavor liking nowadays. Therefore, some interactions between sensory traits may exist. This possibility remains to be explored, since only a few studies have investigated the contribution of interactions to variability in beef palatability. As a matter of fact, beef palatability and consumer satisfaction have been influenced by various factors. Any change in score for each sensory trait may result in significant overall liking difference (O'Quinn et al., 2018). [START_REF] Guzek | Effect of carcass fat and conformation class on consumer perception of various grilled beef muscles[END_REF] illustrated that consumer preference for grilled beef was only affected by muscle cuts instead of carcass composition such as fat and maturity level. Thus, it is necessary to take the major factors relating to beef eating quality and consumer preference into consideration, such as muscle type and cooking method.

For the European beef industry, one of the most important goals should be to pay more attention to beef eating quality and consumer satisfaction. Meeting consumers' eating expectations is an effective strategy to boost beef consumption (Bonny et al., 2018). Thus, in order to gain a better understanding of European consumer's expectation regarding beef eating quality, one major goal is to better know the preference of current European consumers, including the relative contributions of beef sensory traits to their overall satisfaction.

Therefore, the objective of this study was to investigate the contributions of tenderness, juiciness, flavor liking and their interactions to overall liking according to different muscle cuts and cooking methods by European consumers for European beef samples.

Materials and methods

Data collection

The dataset used for this study was previously described by Bonny et al. (2016). It has been set up from a wide range of specific experiments, which represented a large diversity of European cattle types and beef. A total of 743 cattle were from France, Poland, Ireland and Northern Ireland and made up of Angus, Hereford, Murray Grey, Shorthorn, Belted Galloway, Belgian blue, Charolais, Blonde d'Aquitaine, Limousin, Montbeliarde, Romagnola, Simmental, Holstein, Ayrshire and Normande. The cattle were slaughtered based on each country's practice standards. There was a large diversity of cattle source, breed, hang method (achilles hung; tenderstretch hung), aging time (5 days; 7 days; 10 days; 14 days; > 21 days), muscle cut (as shown in the last Tables) and cooking method (grill; roast; yakiniku) to guarantee a large variation in beef palatability and consumer response.

A total of 8216 cuts with consumer testing scores were collected from 18 different muscles. Three cooking methods were conducted in this study (Polkinghorne, Nishimura, Neath, & Watson, 2011;Watson, Polkinghorne, & Thompson, 2008b): the grill cooking method was used in all countries, the roast cooking method was used in Poland, Ireland and Northern Ireland, the yakiniku cooking method was used in Ireland. These studies were conducted independently and the choice of methods to be used was dictated by local priorities. The cooking protocols for each method were tightly defined in terms of temperatures and times and were common to all studies (Polkinghorne et al., 2011;Watson, Polkinghorne, & Thompson, 2008b), with variations in the cooking endpoint for grilled beef in Northern Ireland ("well done"; [START_REF] Farmer | Prediction of eating quality using the meat standards Australia system for Northern Ireland beef and consumers[END_REF] and France ("rare"; Legrand et al., 2013).

Consumer testing studies were performed in four countries to evaluate beef palatability, as described by Bonny et al. (2016). Sample preparation and consumer testing were conducted according to MSA protocol [START_REF] Gee | Protocols for the thawing, preparation and serving of beef for MSA trials for 5 different cooking methods[END_REF]Watson, Gee, Polkinghorne, & Porter, 2008a). All samples were prepared in batches, one consumer tested seven samples by one cooking method: the first "link" sample was from striploin or rump muscle in order to guarantee an average eating quality level. Sensory scores from this portion were not included in the final results. The remaining six samples were from one experimental batch and served to consumer in accordance with a 6 × 6 Latin square, expected to deliver a large diversity of eating quality to each consumer and to balance out potential halo or serving order effects. Consumer evaluation of beef palatability traits were marked on 100-mm line scales (0 = low intensity/liking; 100 = high intensity/liking) for tenderness, juiciness, flavor liking, and overall liking. Consumers were instructed to score the line scale by placing a vertical mark on the line in a position reflecting their perception. Tenderness and juiciness are objective (intensity) measures while flavor liking and overall liking are subjective (hedonic) scores. There are small cross bars at both ends and at 20 mm intervals. The distance from the left end of the scale to the consumer's mark was measured in millimeters (Watson, Polkinghorne, & Thompson, 2008b).

Statistical analysis

All the statistical analyses were performed with the R software (version. 1.1.436). Scatter Plot Matric (SPLOM) was created with the ggplot2 R (R Core Team, 2017) package using the "pairs. Panels" function. Tenderness, juiciness, flavor liking and overall liking were used as variables with variable scatter plots, histograms and the Pearson correlation was calculated between pairs of variables [START_REF] Wickham | ggplot2: Elegant graphics for data analysis[END_REF]. Analysis of variance (ANOVA) was performed using "lsmeans" package and "TukeyHSD()" function to determine the significant differences between sensory scores [START_REF] Lenth | Package "lsmeans[END_REF]. Multivariate regression models and standardized models were developed by using the "lm" (linear model) function and "lm.beta" package.

Stepwise regression was used to discard variables which had no significant effect in the model and ensure remaining variables which had significant impact could enter the model. The relative weight analysis was performed with "relweights" package [START_REF] Kabacoff | R in action: Data analysis and graphics with R[END_REF] and the relative importance analysis was conducted by using R package "relaimpo" [START_REF] Grömping | Relative importance for linear regression in R: The package relaimpo[END_REF] to determine the contributions of tenderness, juiciness, flavor liking to overall liking. The Variance Inflation Factors (VIF) was performed with "vif()" function [START_REF] Daoud | Multicollinearity and regression analysis[END_REF].

Results and discussion

Correlations between sensory traits

As shown in Fig. 1, the positive linear relationships between each sensory trait were observed in the lower left corner from each individual scatterplot; the histogram indicated that the sensory scores were evenly distributed on both sides of the mean; the correlation coefficients ranging from 0.79 to 0.95 (P < .001) suggested the significantly strong correlations between the four studied sensory traits. This range was basically consistent with the conclusions of other research, which indicated a strong association between tenderness, juiciness, flavor liking and overall liking from 0.63 to 0.86 for Polish consumers (Pogorzelski, Woźniak, Polkinghorne, Półtorak, & Wierzbicka, 2020) and from 0.70 to 0.99 for Australian consumers (Polkinghorne et al., 2011;[START_REF] Thompson | Managing meat tenderness[END_REF]. O'Quinn et al. (2012) showed that flavor liking was more highly correlated with overall liking (r = 0.88) than with tenderness (r = 0.76) and juiciness (r = 0.73). Another consistent conclusion by [START_REF] Hunt | Consumer assessment of beef palatability from four beef muscles from USDA choice and select graded carcasses[END_REF] illustrated overall liking was the trait most associated with flavor liking (r = 0.85). In our own study, comparing to previous ones, highest correlation coefficients have been observed with flavor liking, that had the highest correlation with overall liking (r = 0.95) compared to tenderness (r = 0.91) and juiciness (r = 0.84).

Modeling of beef overall liking

The sensory traits tenderness, juiciness and flavor liking, were regressed into palatability models showed in Table 1. Overall liking was used as the independent variable in all models with tenderness, juiciness and flavor liking used as dependent variables. For model 1, 2 and 3, flavor liking, tenderness and juiciness was respectively used as the single variable in the overall liking regression model. The results indicated that regression equations using flavor liking had the highest R 2 , juiciness had the lowest R 2 and tenderness had an intermediate R 2 .

Table 1

Multiple regression models of sensory traits to overall liking. (Pogorzelski et al., 2020). This study reported that tenderness, juiciness and flavor liking accounted for 0.27, 0.06 and 0.67 (for 6 mm thickness steak); 0.31, 0.14 and 0.55 (for 15 mm thickness steak) of eating quality grade, respectively. All the major experimental design factors such as country, muscle/cut, cooking method and their interactions were included in model 6 as fixed effects: the statistical non-significance of all these factors indicated that in such a large dataset, all these experimental factors had no effect on the results (Table 1). As also indicated by [START_REF] Goodson | Beef customer satisfaction: Factors affecting consumer evaluations of clod steaks[END_REF], 75% variation of beef overall liking was due to flavor liking. Whichever calculation methods have been used, flavor liking was the main driver of consumer satisfaction. In such a multilinear regression study, the multicollinearity across variables should be taken into account, that will be discussed below.

It is also well established and accepted that variations in beef sensory perception could be adequately described by four variables: tenderness, juiciness, flavor liking and overall liking. In the early beef sensory work, Watson, Polkinghorne, & Thompson, 2008b deduced sensory traits by sensory profiling, such as using tenderness to represent liking of texture, ease of chew, hardness; describing overall liking as liking of cooked appearance and eating experience. To some extent, overall liking and flavor liking are more flexible and more subjective compared to tenderness and juiciness. During the perceptive procedure, overall liking could be comprehensively attributed to tenderness, juiciness and flavor liking. This was therefore the objective of this study to investigate the contribution of tenderness, juiciness and flavor liking to overall liking.

In MSA methodology, MQ4 is a single palatability score of combining the four sensory traits, by weighting tenderness, juiciness, flavor liking and overall liking scores by about 0.3-0.4, 0.1, 0.2-0.3 and 0.3, respectively, depending on the country (Thompson, 2002;Watson, Polkinghorne, & Thompson, 2008b). A three-variable discriminant function (MQ3) was investigated, using only tenderness, juiciness and flavor liking: it was proved to be less efficient than MQ4 in correctly relating to the four eating quality descriptions of unsatisfactory, good everyday, better than everyday and premium quality used in conjunction with the four line scales (Watson, Polkinghorne, & Thompson, 2008b). In studies utilizing MSA/UNECE sensory protocols, a standard procedure is to compare discriminate analysis weightings from 3 scale and 4 scale (adding overall) models and to consider these weightings and related cut-off values between the 4 nominated descriptions. The most accurate prediction of the four eating quality descriptions has been obtained from an average of the MQ3 and MQ4 weightings (Watson, Polkinghorne, & Thompson, 2008b).

In the meantime, there is still a need to study the impact of the other three traits to overall liking, especially when referring to the strong association between overall liking and the other three sensory traits. In short, there seems to be no discrepancy between contributions of sensory traits to MQ4 and overall liking. Furthermore, investigation of the influence of the other three traits to overall liking, and consequently MQ4, might provide a viable source for MSA-based consumer testing.

Contributions of sensory traits to overall liking

The contributions of each sensory trait to overall liking were evaluated by using relative weight analysis, which is defined as the proportionate contribution of each independent variable to dependent variable, representing both its single contribution and its relative contribution when combined with other predictors [START_REF] Johnson | A heuristic method for estimating the relative weight of predictor variables in multiple regression[END_REF], which means that the sum of the contributions is equal to 100%. In this case, flavor liking contributed the most (40%), followed by tenderness (34%) and juiciness (26%) (Fig. 2).

In order to double check the sensory contribution and driven traits to overall liking, another method to determine relative importance of a predictor called relaimpo was used in this study [START_REF] Grömping | Relative importance for linear regression in R: The package relaimpo[END_REF][START_REF] Johnson | History and use of relative importance indices in organizational research[END_REF]. The relative importance of each trait, which were summed to R 2 (94.3%) instead of 100%, was obtained. In this way, flavor liking still contributed the most (39%), followed by tenderness (31%) and juiciness (24%), each value being determined with a confidence interval (Table 2). In Table 2, 95% lower confidence limit of flavor liking is 39%, which is greater than upper relative importance of tenderness (32%), as well as juiciness (25%). This confirms that flavor liking is a main driven trait of variability in overall liking.

There is, therefore, a strong evidence that, for European consumers, flavor liking is the most dominant contributor. Nevertheless, tenderness and especially juiciness accounted for a considerable amount to overall liking in our study by comparing with the result of O'Quinn et al. ( 2018), which reported that overall liking was explained by flavor liking (49.4%), tenderness (43.4%) and juiciness (7.9%) and using the multiple regression model where overall liking = 0.42 Tenderness +0.07 Juiciness +0.48 Flavor liking. In O'Quinn's study, a consistency between contributions and regression coefficients was observed. In contrast to our study, the regression coefficient of juiciness was 0.1, which is much lower by comparison to its contribution (24%). As wellknown, tenderness, juiciness and flavor liking are not totally independent with each other. Indeed, they have strong relationships among them with high r value ranging from 0.79 to 0.85 as shown in Fig. 1. Thus, the discrepancy between values of contribution and [START_REF] Daoud | Multicollinearity and regression analysis[END_REF]. The VIFs presented in Table 3 indicated there were most likely reliable multicollinearity between variables.

Thus, the values of contributions to overall liking for flavor liking (39%), tenderness (31%) and juiciness (24%) are reliable for European consumers and European beef.

The different contributions of flavor liking, tenderness and juiciness to overall liking between O'Quinn's study (49.4%, 43.4%, 7.4%) and our study (39%, 31%, 24%) could be due to the selection of different cuts. The Longissimus thoracis muscle was mainly used in O'Quinn's study (2012), while, in the current study, we studied eighteen muscle cuts. However, when we regressed a model by using only values of the Longissimus thoracis muscle, contributions of flavor liking, tenderness and juiciness were similar with the result of eighteen muscle cuts, namely 40%, 29%, 24%. Therefore, it is unlikely that differences in muscle cuts are the reason of discrepancy. While there were differences in cattle breeds and consumer country between these two trials, the most likely reason for the differences observed might be the different statistical methods used in our study and in O'Quinn's study (O'Quinn et al., 2018). In fact, in some cases, multiple regression is not capable of partitioning shared variance and regression weights cannot be used as relative contributions.

O 'Quinn et al. (2012) found that Australian steaks were evaluated as less juicy by consumers than all beef sources from the USA, although fat content of Australian samples was not the lowest. In fact, tenderness, flavor liking, overall liking and especially juiciness are known to increase with increasing intramuscular fat amount (Corbin et al., 2015). Moreover, juiciness is the sensory trait the most correlated with fat content (r = 0.37) compared to tenderness (r = 0.31), flavor liking (r = 0.25) and overall liking (r = 0.28) [START_REF] O'quinn | Consumer assessment of beef strip loin steaks of varying fat levels[END_REF]. Generally, an intermediate intramuscular fat percentage should not lead to the worst juiciness. Therefore, it could be speculated that a different perception of tenderness and flavor liking may have an influence on consumer's perception of juiciness. Beef overall palatability may not only be dependent on tenderness, juiciness and flavor liking, but also rely on interactions between these three scores. However, there are relatively few studies on this subject. O'Quinn et al. ( 2018) demonstrated that interaction terms between sensory scores had no effect on overall liking with data obtained from American consumers and mainly the Longissimus thoracis muscle. Thus, to further explore this, interactions among sensory traits were introduced into the overall liking estimation model.

In model 11 (Table 6) when interactions were included, the contributions of flavor liking, tenderness and juiciness were 39%, 32% and 25%, respectively, which were the same as the initial model without any interaction. Flavor liking was still the highest contributor to variability in overall liking. In addition to that, the interactions also accounted for the amount of the variability in overall liking. However, it was worthwhile noticing that in this model, all the interactions were significant (P < .05), but juiciness was not significant anymore. This may be due to the highly multicollinearity between variables. Much higher values of VIFs (ranging from 47 to 240) were obtained during the analysis procedure, thus indicating the coefficients of the model were absolutely unreliable. Thus, taking interactions into consideration in the model to predict overall liking is not relevant.

Importance of sensory traits to overall liking

Rodas-González, Huerta-Leidenz, Jerez-Timaure, and Miller (2009) reported that Venezuelan consumers pay more attention to flavor liking to score their ultimate satisfaction, with higher percentages of acceptability being derived from flavor liking than from tenderness. On the basis of these previous studies and our study, it can be concluded that flavor liking is the most important contributor to overall liking. Flavors are generated from a complex interaction of tastes, tactile senses and aromas taken collectively throughout the tongue, nasal, sinus and oral cavities (Kerth & Miller, 2015). One main reason for this high contribution of flavor liking may be due to the improvement of beef tenderness in recent years (O'Quinn et al., 2018). Indeed, much effort at the commercial level has been conducted to improve beef tenderness since tenderness has been viewed as the most important contributor to consumer overall liking over the earlier past decades [START_REF] Savell | 2015/2016 National beef tenderness survey-executive summary[END_REF].

To gain more insight into this hypothesis, the contributions of the sensory traits to overall liking were investigated for four subclasses of beef, classified according to tenderness, as rated by consumers. Thus, the whole dataset (tenderness: 5-97) was divided into four subsets based on the quartiles tenderness: 5-37; 37-52; 52-66; 66-97 according to the ascending sequence of tenderness score as it is described in Table 4. Four overall palatability models and contribution of each trait based on tenderness ascending sequentially subsets were determined (Table 5). As can be seen from models 7, 8 and 9, with the improvement of tenderness, the contribution of flavor liking increased from 44% to 54% and meanwhile the contribution of tenderness decreased from 22% to 7%. For the upper class of tenderness, the contributions of tenderness, juiciness and flavor liking were all at a high level (model 10). This may indicate that one superior steak should have good perception on each trait, which explains why each trait should contribute a lot. From these results, we can conclude that the improvement of tenderness is one of the reasons of increasing contribution of flavor liking. When consumers make the final decision on overall

Table 4

Multiple regression models of sensory traits to overall liking based on four levels of tenderness evaluation. liking evaluation, more emphasis will be placed on the trait with the lowest perceived score except for premium beef which should be outstanding for all traits. In many studies (Polkinghorne, pers.com), comparison of the MQ3 and MQ4 weightings have shown a transition from tenderness toward flavor liking importance in line with a transition from Unsatisfactory to Premium. This is a characteristic of the MQ4 standard and likely the reason as to why it performs better than 3 scale analysis as the overall component of the MQ4 calculation, typically 30%, can reflect greater or lesser proportions of tenderness, flavor liking and juiciness across the quality range (Watson, Polkinghorne, & Thompson, 2008b).

Database

According to the results above, the contributions of juiciness were similar within different tenderness level, which indicated that juiciness was a trait highly influenced by the multicollinearity between the three studied traits. In order to verify this assumption, a new model was proposed to estimate the contribution of tenderness and flavor liking, and of their interaction to overall liking. Juiciness was therefore excluded from this model. As described in Table 6, the interaction between tenderness and flavor liking was not significant and hence excluded through stepwise from this model (Model 12). This might be explained by the absence of juiciness. Our findings suggest that interactions among sensory traits could influence the relationship between sensory traits and overall liking. Thus, efforts should be developed to enhance each sensory trait instead of the most important one. This is also reported by O'Quinn et al. (2018), who suggested that any improvement in one single palatability trait should guarantee that this will have no negative impact on the other traits.

Influence of cooking methods on overall consumer perception

A major factor taken in consideration by the MSA system is the "cooking method and cut" combination. It is well known that beef eating quality depends on cooking method (Devlin et al., 2017). The influence of cooking method on tenderness, juiciness, flavor liking and overall liking ratings and their contributions were presented in Tables 7 and8. Consumers assigned highest and lowest scores to yakiniku and roast samples, respectively (Table 7). For all cooking methods, flavor liking was still the most important contributor to overall liking. However, for roast, the contribution of flavor liking (40%) is higher than for grilling (38%) and yakiniku (39%) (Table 8), which were characterized by low (53), medium [START_REF] Grunert | Future trends and consumer lifestyles with regard to meat consumption[END_REF] and high ( 59) scores for flavor liking, respectively. Similarly, the contribution of tenderness to overall liking (grill: 33% > roast: 29% > yakinuki: 28%) (Table 8) is in the decreasing order of tenderness score (yakinuki: 59 > roast: 52 > grill: 51) (Table 7). This indicates a good agreement with our previous assumption: when consumers have a low perception for one sensory trait, this trait may contribute more to overall liking. In addition, the contributions of juiciness to overall liking were close (25%, 24%, 23%) whichever the cooking method. This may be explained by the fact that the perception of juiciness could be easily influenced by tenderness and flavor liking. Chriki et al. (2013) observed that muscle type played the greatest role in tenderness determinism. [START_REF] Guzek | Effect of carcass fat and conformation class on consumer perception of various grilled beef muscles[END_REF] illustrated that consumer preference for grilled beef was only affected by muscle cuts. Estimating consumer satisfaction on the basis of each cut is important for the beef industry to establish processing strategy. In fact, the initial MSA studies were focused on grilled striploins with subsequent studies establishing that interactions with muscle, muscle position and cooking method must be included to develop effective prediction of consumer satisfaction. Thus, the system expanded to include various muscle cuts by different cooking methods. In the current stage, each muscle can be independently estimated by using grading input parameters and weighting method (Polkinghorne, Philpott, Gee, Doljanin, & Innes, 2008a). Accordingly, it remains a benefit to further investigate the contributions of sensory traits to overall liking by muscle cuts.

Influence of muscle cuts on overall consumer perception

Overall liking is the synthetic sensory evaluation of a steak during beef consumption, but one steak can be scored highly due to outstanding scores for one or two sensory traits despite one or two traits being evaluated as unacceptable. [START_REF] Huffman | Effect of beef tenderness on consumer satisfaction with steaks consumed in the home and restaurant[END_REF] reported that slightly tough beef would be acceptable by consumers if flavor and juiciness were good enough. Indeed, the overall liking score of KNU099 (45) is not negatively influenced by its low tenderness score (35) (Table 9), which may be compensated by its relatively high flavor liking score (48). This may thus indicate that tough beef might be rated high for eating quality if flavor liking is relatively more acceptable than tenderness.

In Table 9, where the overall liking is significantly different (P < .05) between two cuts, there is at least one significant difference for tenderness, juiciness and flavor liking. For instance, overall liking was significantly different (P < .05) between BLD096 and EYE075, as well as flavor liking and juiciness. In contrast, there was no difference in tenderness. On the other hand, if there is no difference for overall liking between two muscle cuts, it does not mean they have close eating quality experiences as this global score can result from any combination a T = Tenderness.

Table 6

Multiple regression models for contributions of sensory traits and interactions to overall liking with (R2 = 0.94) or without (R 2 = 0.94) juiciness. 2018) suggested that the failure of one single trait (especially flavor) has a large impact on overall palatability. Since flavor liking explains the most part of overall liking variability, it is most likely that muscle cuts with significant differences in flavor liking have also significant differences in overall liking. By contrast, cuts with significant difference in tenderness or juiciness also have significant difference in overall liking, as it is the case for RMP005 and STR045.

To further confirm the previous hypotheses, we calculated the contribution of each trait to overall liking for all the cooking methods according to muscle cuts (OUT005, TOP073, RMP131, STR045, RMP231 and TDR062), which had the highest sample sizes (Table 10). An interesting trend can be seen from OUT005 to TDR062, ranged according to increasing consumer evaluation scores. In this case, the contribution of tenderness to overall liking decreased, the contribution of flavor liking increased. This in line once again with the fact that MQ4 weightings have shown a transition from tenderness toward flavor liking importance in line with a transition from Unsatisfactory to Premium (Watson, Polkinghorne, & Thompson, 2008b). For cut TDR062, this confirms our previous assessment that any premium beef cut should be outstanding for the three sensory traits, and not only for one or two.

Conclusion

According to European consumer testing with European beef samples, flavor liking was the most important contributor (39%) to overall liking, followed by tenderness (31%) and juiciness (24%). Furthermore, contributions of tenderness, juiciness and flavor liking to overall liking variability varied according to cooking method and according to the cut. The improvement in tenderness over years may be one of the reasons why the highest contribution of flavor liking has been observed in recent studies such as this one. Any change in one sensory trait may lead to different perceptions of other traits and overall palatability. Whereas flavor liking was the most driven contributor, low scores for flavor liking have the most important potential to cause acceptability failure of overall liking. On the other hand, juiciness was the least robust trait and could be influenced by other traits during sensory perception. In addition, for medium cuts, one sensory trait with a low score has the potential to be compensated, at least in part, by other traits with higher scores. This is more unlikely for cuts with the highest or the lowest overall liking scores. Meanwhile, when consumers have a bad appreciation for one sensory trait, more emphasis will be placed on the trait with the lowest perceived score. This is best accomodated by the MQ4 statistic that effectively allows an adjustment for flavor liking, tenderness and juiciness through both their direct contribution and their contributions to the overall scale. For outstanding steak, each sensory trait should have excellent scores and their respective contributions to overall liking is high.

Table 7

Consumer palatability scores of tenderness, juiciness, flavor liking and overall liking depending on three cooking methods. Values within a column which do not share a common superscript are significantly different (P < .05).

Discussion

With the development of beef grading schemes (i.e. the MSA and USDA) and the great number of studies about the relationships between beef eating quality and animal/carcass/meat attributes, the French beef industry is more and more convinced by the importance of marbling. Indeed, so far, marbling score has not been widely considered in Europe, and marbling measurement has been conducted mainly for research purpose. By contrast, the compulsory beef grading scheme in Europe is the EUROP grid, which is based on carcass fatness and conformation, not marbling. By using a dataset containing 711 data from beef animals graded according to the MSA standards and the EUROP grid, the relationships between marbling score and European carcass classification scores have been investigated. The underlying hypothesis was that European carcass conformation and fat scores may or not provide data for the estimation of marbling score, although a poor correlation between European fat score and marbling score was previously reported [START_REF] Conroy | The relationship of live animal muscular and skeletal scores, ultrasound measurements and carcass classification scores with carcass composition and value in steers[END_REF]. However, the wide variation in animal breed and type, origin, rearing and feeding management in our study was relevant to study at a large scale the potential relationship between marbling score and European conformation score and fat score.

According to the correlation analysis and multiple regression analysis, weak correlations between marbling score and European conformation score and fat score (r ranging from -0.1 to 0.2; 0.34 to 0.59) were obtained based on different animal type and only a low proportion of variances of marbling score can be explained by European conformation score and fat score (R 2 ranging from 0.03 to 0.24). Based on these results, in general, higher European carcass fat score would be in line with higher marbling score for all animal types and higher European conformation score would be associated with lower marbling score for old females and young bulls but not for young bulls and young females. In the case of young females, higher European conformation score would be in association with higher marbling score. Additionally, other carcass attributes such as weight, age and ossification, rib fat thickness were used in combination with European conformation and fat scores to estimate marbling score. With variable effect of those attributes, the prediction potential of European conformation and fat scores was therefore poor, the greatest R 2 (46%) for young bulls only can barely be taken into consideration. Therefore, the conclusion drawn from this study is that the EUROP grid (European conformation and fat scores) of bovine carcasses cannot be considered as a good indicator of marbling.

Being one of the most critical steps in the MSA beef grading system, carcass grading contributes to the prediction of beef eating quality. Marbling assessment as well as meat and fat color measurements are generally performed at the 10 th -13 th rib. However, in the European context, cutting carcass at the other rib site would reduce the commercial value of the carcass so that the preferred ribbing site is the 5 th rib (quarter site). To adapt the ABCAS grading practices in the European beef sector in the future, marbling assessment was performed at both the 5 th and the 10 th rib sites to examine if difference existed between two rib sits and most importantly, if the prediction of beef eating quality would be affected by different grading sites. As hypothesized, no significant difference of marbling score was found between the 5 th rib and the 10 th rib but the scores of meat color and fat color at the 10 th rib were significantly higher than those of the 5 th rib. In fact, meat color and fat color are mainly used as thresholds to discard non-compliant carcasses in the MSA system rather than as predictive inputs in the MSA prediction model. So, the difference observed for meat and fat color at two grading sites would have no impact on the beef eating quality prediction. Moreover, there was also no difference in the overall eating quality score (MQ4) between the 5 th rib and the 10 th rib and the average difference of MQ4 score between two sites ranged from 0.01 to 0.07, which are very low differences that can be accepted. Hence, marbling assessment can be performed on the M. longissimus thoracis et lumborum at the 5 th rib site.

In addition to practical managements of CCPs including carcass grading in the MSA system, consumer evaluation on beef eating quality is of importance for the development of MSA in any other part of the World. In the MSA system, beef eating quality is scored for tenderness, juiciness, flavor liking and overall liking. In fact, the formation of overall liking depends highly on the other three attributes. According to previous studies that demonstrated flavor liking becomes increasingly important for consumers, we hypothesized that flavor liking is the most important attribute for European consumers as well. Therefore, European consumer preference of the different eating quality attributes was explored through large-scale consumer tests with European beef samples. As expected, we found that tenderness, juiciness, flavor liking and overall liking was strongly correlated with each other (r ranging from 0.79 to 0.95, P<0.001). Due to the exist of multicollinearity caused by strong correlations between eating quality attributes, it was difficult to calculate the individual contribution of tenderness, juiciness and flavor liking to overall liking. Given the utilization of two particular statistical methods that can resolve the multicollinearity issue, flavor liking was found to contribute the most to overall liking (39%), then followed by tenderness (31%) and juiciness (24%). After dividing the whole database into four sub-datasets according to the ascending sequence of tenderness score, we found that, with the increase in tenderness score, the contribution of flavor liking to overall liking increased.

It was therefore concluded that the improvement of tenderness in the recent years may explain the increasing importance of flavor liking for consumers. We also noticed that juiciness can explain a non-negligible part of variability in overall liking but less compared to flavor liking and tenderness.

Chapter 4. Beef quality of grass-fed Angus x Salers young bovines

In order to develop novel livestock farming systems to improve agricultural biodiversity and sustainability and to enhance high quality meat production, a research based on specific crossbreeding, was conducted on the Laqueuille site of the Herbipôle experimental farm of INRAE. This research project is entitled "Pasture-based livestock farming system: Adapting the genetic type and mixing the species to strengthen their sustainability" (Vazeille et al., 2018). There were three independent farming systems developed in this program: a monospecific bovine animal system (ncattle=22), a mono-specific ovine animal system (nsheep=164) and a mixed-species system with sheep and beef cattle (ncattle=13, nsheep=66). The aim of this program was to compare animal performances and meat quality characteristics produced from animals of the same species reared in these three framing systems. Animals feed was provided by the permanent mountainous pastures and haylage. The beef animals used in this experiment were crossed from a hardy breed dam (Salers) and an early-maturing breed sire (Angus) in order to work with cattle well-adapted to the mountainous environment and grass feeding system and potentially able to produce beef of good eating quality. According to the hypothesis of the experimental design, the effect of mixing species in one grazing area will be mainly observed for ovine animals, while the study on bovine animals will be mainly about the effect of specific crossbreeding (Angus x Salers) and grass-feeding on their meat quality. Therefore, the studies presented in the thesis would be focused on bovine animals derived from the mono-specific bovine animal system and the cattle/sheep mixed system. Chapter 4 presents the findings of the research on cattle system focused on two objectives: 1) the characterization of carcass and meat attributes such as beef eating quality of these Angus x Salers animals; 2) the potential application of the MSA grading system with the Angus x Salers crossbred bovines and the French consumers.

Study 1: Carcass characteristics and beef quality of grass-fed Angus x Salers young bovines

This study used thirty-one grass-fed Angus x Salers young animals that were reared in two farming systems (a mono cattle system vs. a mixed system with beef cattle and sheep) to characterize their carcass and their meat in terms of beef nutritional and eating quality.

The hypotheses to be tested in this study were:

The association of beef cattle and sheep within a mixed system will have no adverse impact on carcass and meat attributes.

Grass feeding will result in good fatty acid composition. Hardy and early-matured cross breeding (Angus x Salers) with a grass feeding strategy will produce lean meat but with good eating quality. The hypotheses to be tested in this study were:

French consumers would be able to discern differences in eating quality traits across muscle cuts, muscle slicing methods and ageing times for Angus x Salers animals.

The MSA consumer testing protocol will be relevant for French untrained consumers to accurately score beef samples for eating quality. The assignation of MSA quality grades based on MQ4 score can reach a relevant accuracy.

The current MSA prediction model might be less robust with these young and lean animals.

Introduction

Novel livestock farming systems such as agroecological farming and sustainable intensification systems, have emerged in response to the growing ecological concerns about sustainable agriculture, especially in France [1]. Moreover, the beef industry is undergoing changes due to the increasing concern for animal welfare and environmental impact at the expense of intensive livestock production coupled with the increasing demand for beef of high nutritional and eating quality [2,3], also with the need to develop pathways that are context-specific, with more consideration given to the food origin, its quality, and the region where beef is produced [3].

Animal feeding and farming systems are evaluated as crucial factors of consumers' meat-quality perception, and grass-fed beef is preferred by consumers over concentratefed beef, mainly based on visual (such as color) and credence (such as perceived traits) qualities [4,5]. Grass-fed beef products represent a premium niche market with certain additional value through better animal welfare, a cleaner environmental footprint, and enhanced nutritional quality in terms of a more favorable PUFA/SFA ratio (polyunsaturated fatty acid/saturated fatty acid ratio) and ω-6/ω-3 ratio (n-6 polyunsaturated fatty acid/n-3 polyunsaturated fatty acid ratio) [2,6]. Additionally, grass-fed products have been identified to be more tender, though containing less intramuscular fat [7], but may be associated with lower commercial quality (i.e., lower meat yield).

Furthermore, grass feeding typically results in leaner beef products [8], whereas beef products with higher intramuscular fat content tend to produce beef of superior eating quality. For instance, the marbling level of "abundant" corresponds to the grade of "prime" in the United States Department of Agriculture (USDA) beef-grading system. Specific crossbreeding with early-maturing breeds to enhance fat synthesis and deposition is a way to take advantage of grass feeding systems in order to produce meat of good eating quality [9]. Hardy breeds, such as Salers, are well adapted to the mountainous pasture environment, unlike more specialized beef breeds (i.e., Angus). The early-maturing Angus breed is more adapted to grass feeding and fattening than continental beef breeds [10]. As such, the animals used in this experiment were crossed from an early-maturing sire breed (Angus) and a hardy dam breed (Salers) [11] with the objectives of rearing cattle well adapted to the mountainous environment and grass-feeding system to potentially produce beef of good eating quality. Additionally, carcass characteristics and meat quality have been poorly studied within this specific crossbreed, and were therefore tested within the experiment.

Mixed systems with different species seem to be effective at enhancing animal performance [12] and optimizing agronomic outputs [13]. In mixed-species systems, reducing animal density of one species provides benefits for grass consumption and pasture utilization efficiency [14] and also for parasite control through the dilution of parasites [15]. In comparison with monospecific systems, a mixed sheep/beef cattle system has increased meat production due to higher animal weight gain [16]. However, few studies have investigated the effect of mixed systems with a principal focus on animal performance, such as live weight gain [17]. What is yet less clear is the impact of mixed systems on carcass characteristics and meat eating quality attributes of tenderness, juiciness, and flavor, as well as FA composition.

To better meet these objectives of novel livestock farming systems, two strategies were studied in the current experiment: (1) specific crossbreeding of beef animals was performed for better adaptation to grass feeding and for a better beef eating quality, and (2) a mixed-species system was introduced for integrated management of parasites and higher efficiency of grass utilization. There were two major aims of the present study: (1) to examine the carcass and meat-quality attributes of the Angus x Salers beef cattle;

(2) to investigate the effect of farming system (the association of beef cattle and sheep within a mixed system) on beef carcass characteristics and meat-quality attributes.

Materials and Methods

Experimental Design

The present study was based on an experiment conducted on the Laqueuille site of the Herbipôle experimental unit from INRAE. All cattle used in this study were an Angus x Salers cross. There were two independent farming systems: a monospecific beef cattle system (MONO, n cattle = 22) and a mixed system with beef cattle and sheep (MIX, n cattle = 13, n sheep = 66). In the mixed system, beef cattle co-grazed with sheep from April until the end of July 2019, after which cattle grazed before the sheep from August until mid-October 2019. Calves were weaned in October and fattened indoors with grass haylage. The grass and haylage used to feed all the animals were from natural organic pastures with a variety of grass species (results of botanical analysis of grasses are presented in Supplementary Materials). All males were castrated one week after birth. Allocated areas and livestock units (LU) were the same in both systems, the mixed system being characterized by 40% sheep LU and 60% bovine LU.

Carcass Grading and Muscle-Cut Collection

Thirty-one cattle-20 (10 females and 10 castrated males) from the mono system and 11 animals (6 females and 5 castrated males) from the mixed system-were slaughtered at a young age (around 14 months). All procedures were approved by the C2EA-02 Ethics Committee (APAFIS#1417-2015081011477291 v3 and APAFIS#24191-2015043014541577 v4). Animal carcasses were assessed according to the EUROP grid (conformation and fat scores were converted into a continuous 15-point scale) [18] and by a certified grader for ossification, marbling, and hump height following the MSA (Meat Standards Australia) standards [19]. Critical methods of carcass grading are presented in Supplementary Materials. The descriptive data are presented in Table 1. 9 PUFA/SFA ratio: polyunsaturated fatty acid/saturated fatty acid ratio; 10 ω-6/ω-3 ratio: n-6/n-3 PUFA ratio; 3,5,6,7,8,9,10 data based on 30 animals.

Left and right striploins (m. longissimus dorsi et thoracis), bolar blade (m. triceps brachii caput longum) and two portions of the internal flank plate (m. obliquus internus abdominis) were collected from each carcass at 24 h postmortem (pictures of raw muscles and cuts are presented in Supplementary Materials). These muscle cuts were selected to represent a potential difference in eating quality based on previous work in beef [20]. All muscle cuts were vacuum packed and stored at 4 • C before being frozen and used for further quality testing and measurements. Two aging times were applied to the bolar blade, with 31 muscle cuts aged 5 days and similar 31 muscle cuts aged 14 days. Both striploins and both portions of the internal flank plate were all aged 14 days. As such, in total, 186 samples were collected for subsequent testing sessions, with 31 striploin samples, 62 bolar blade samples, and 62 internal flank plate samples used for untrained consumers, whereas the other 31 striploin samples were used for the trained panelists.

Not all data are reported in the results. Corresponding information is mentioned in the table titles or notes.

Physicochemical Analysis

Measurements of muscle lipid, fatty acid analyses, antioxidant and vitamin contents were conducted on a subsample of the striploin and are described in detail in [START_REF] Hamdi | Effects of Olive-Cake Supplementation on Fatty Acid Composition, Antioxidant Status and Lipid and Meat-Colour Stability of Barbarine Lambs Reared on Improved Rangeland plus Concentrates or Indoors with Oat Hay plus Concentrates[END_REF] [21].

FA Composition

FA composition was determined by chromatographic analysis after transmethylation. FAs underwent methylation by mixing of boron trifluoride (BF3) and methanol 14%. Methyl esters of FAs were then extracted by hexane and recovered after centrifugation at 1000× g for 10 min at room temperature. The FA composition was determined by gas chromatography flame ionization (CPG/FID, Shimadzu, Kyoto, Japan) using an Omegawax 250 capillary column (30 m, 0.25 mm ID). Injector and detector temperatures were 230 • C and 250 • C, respectively. Temperature was increased by 5 • C/min. FAs were identified and quantified, and the results are expressed as content (mg/100 g muscle).

Antioxidant Content

A subsample of 250 mg of striploin muscle was homogenized in 3 mL of phosphate buffer (50 mM, pH 7) with a polytron for 15 s at 17,000× g. The homogenate was centrifuged at 1200× g for 15 min at 4 • C. The supernatant obtained was filtered and stored at 80 • C for determination of the activities of superoxide dismutase (SOD), glutathione peroxidase (GPx), and catalase (CAT).

SOD activity was determined by measuring the ability to inhibit the autoxidation of pyrogallol by 50%. The absorbance of the final solution was measured at 420 nm and 25 • C by a Uvikon 923 double-beam spectrophotometer (Kontron Analysis Division, Zurich, Switzerland).

GPx activity was determined by measuring the rate of oxidation of NADPH. The absorbance of the final solution was measured by the Uvikon 923 double-beam spectrophotometer at 366 nm and 37 • C. GSH Px activity is expressed as mmol NADPH/min.mg protein.

CAT activity was determined by measuring the rate of decomposition of hydrogen peroxide (H 2 O 2 ) to H 2 O and O 2 . The absorbance of the final solution was measured using the Uvikon 923 double-beam spectrophotometer at 240 nm and 20 • C. CAT activity is expressed as mmol H 2 O 2 /min.mg protein.

Vitamin Content

Vitamin A was extracted after saponification and hexane extraction. The hexane phase was removed by evaporation with a stream of gaseous nitrogen. Then, the dry extract obtained was solubilized by 240 mL of tetrahydrofuran and 240 mL of dichloromethane: methanol (65 V/35 V). Vitamin E concentration was determined by UV spectrophotometry at 292 nm and quantified using high-performance liquid chromatography (HPLC; model 430, Kontron) with Kroma System 2000 software (Kontron Analysis Division).

Color and pH

Color was measured on the striploin 2 h after excision from the carcass using a spectrophotometer (Konica Minolta CM-600d, Osaka, Japan) and expressed in CIE L*, a*, and b* units. Five measurements per muscle (randomly distributed over the muscle but avoiding connective tissue) were used to determine the meat color. Meat pH was measured on striploin using a portable pH meter at 24 h postmortem.

Untrained Consumer Testing

A total of 155 muscle cuts were prepared for untrained consumer testing. According to the MSA grilling protocol [22], each muscle cut was sliced into five 25 mm-thick steaks. Each steak was further cut in half after cooking and 10 portions from one muscle cut were served to consumers. Steaks were grilled on a Silex clamshell grill set to 200 • C for 2.5 min up to an internal temperature of 55-57 • C. Representative pictures with three muscle cuts before and after cooking are presented in Supplementary Materials. Each consumer evaluated in total seven steaks-a starter steak (striploin) followed by six testing steaks (from striploin, bolar blade and internal flank plate)-that were served based on a 6 × 6 Latin square allocation for diverse quality levels and balancing order effect across consumers. Each consumer scored tenderness, juiciness, flavor liking, and overall liking on a 100 mm scale, with the left side of the scale representing not tender, not juicy, extremely dislike, and the right side representing very tender, very juicy, and extremely like. Then, consumers were asked to assign each sample to one of four MSA quality grades [2 star (unsatisfactory), 3 star (good everyday), 4 star (better than everyday), and 5 star (premium)] that best described their overall evaluation of the sample. A total of 360 untrained French consumers were recruited to participate in one of the 6 testing sessions with 60 consumers per session.

Meat Preparation, Trained Panelist Eating Quality Evaluation, and WBSF Measurement

Striploin samples were thawed at 4 • C for 24 h and cut into two subsamples for eating quality and WBSF measurement.

A panel of 20 members evaluated each meat sample using the monadic test method. Before the eating quality evaluation, the panelists were trained to be familiar with the meat eating quality evaluation traits. The meat samples were cut into 15 mm-thick steaks, which were grilled on a double-sided grill at 300 • C for 1.15 min to reach an internal temperature of 55 • C. Then, each sample was cut into 5 portions of 15 × 20 × 20 mm and each portion was sliced into 4 pieces to be served to four different panelists. For 5 testing sessions, 25 portions from 5 animals were evaluated using a 5 × 5 Latin square allocation, whereas 30 portions from 6 animals were evaluated using a 6 × 6 Latin square in the last session. In total, 6 sessions with 31 samples from 31 animals were evaluated by 20 panelists. These 20 panelists participated in each session, and each panelist tested 31 samples. Six eating quality traits were used to describe meat quality: initial tenderness, overall tenderness, initial juiciness, overall juiciness, typical flavor, and abnormal flavor. Each eating quality trait was scored on a 10-point non-graduated scale from 0 (tough, dry, slight flavor) to 10 (very tender, very juicy, intense flavor).

WBSF measurement of the raw meat was measured using MTS Synergie 200 [23]. For each sample, 5 cores of 1 cm meat were cut perpendicularly to the fibers. The peak force was recorded for each meat piece and the shear force was finally determined for each sample based on the average of the 5 measurements.

Statistical Analysis

Statistical analyses were performed with R software (4.1.1) and IBM SPSS 25. To assess the effect of animal gender and farming system and their interaction on carcass characteristics and meat quality, a univariate analysis of variance was performed using a general linear model with the post hoc (Tukey HSD) test, including Bonferroni correction for pairwise comparisons. The significance level was determined at a Bonferroni-corrected p < 0.05. This method was used to examine the significant differences between means of raw data. Data from 14-day-aged striploin were used in this analysis.

In the specific case of data from untrained consumers, a linear discriminant analysis was performed [package: MASS, function: lda] to calculate the optimal weightings of the four eating quality attributes (tenderness, juiciness, flavor liking, and overall liking) to predict the final MSA quality grades (2 star, 3 star, 4 star, and 5 star) based on the combined meat eating quality score (MQ4). The methodology is exhaustively described in Watson et al. (2008) [22]. The current MQ4 was calculated according to the following equation: 0.36 × tenderness -0.02 × juiciness + 0.29 × flavor liking + 0.37 × overall liking. The calculation of MQ4 weightings was based on raw data on all samples (10 portions for each muscle cut) tested by consumers.

To provide potential structure of the entire dataset with different dimensions and accurate inference in terms of correlations among all the animal, carcass, and meat quality attributes, principal component analysis (PCA) and Pearson correlation analysis with hier-archical cluster analysis (HCA) were conducted [package: factoextra, corrplot, ClustOfVar, function: fviz_pca, corrplot, hclustvar]. Data from 14-day-aged striploin were used in this analysis.

To comparatively assess the eating quality level of the current samples with those previously reported to define an overall average eating quality level, a European consumertesting dataset from a wide range of experiments conducted containing a large diversity of cattle types and beef cuts [24] was utilized, with chi-squared tests used in this analysis. A total of 1550 samples from 155 muscle cuts, including all the muscle cuts and aging times, were used in this comparative analysis.

Results

Effect of Animal Gender and Farming System on Carcass Characteristics

Animal gender significantly affected carcass weight and ossification score (p < 0.001), with females having lower carcass weight and higher ossification score (Table 2). There was a significant interaction between gender and farming system on fat and muscle percentage based on hot carcass weight (p < 0.01): the MIX females showed higher fat percentage and lower muscle percentage than MONO females, but no difference was observed for males. However, neither animal gender nor farming system showed any significant effect on marbling and fat cover, hump height, carcass conformation, pH, or meat color (p > 0.05). The farming system displayed significant effects on some FA contents (Table 3). On average, MIX animals had higher contents of total FA, SFA, MUFA, and CLA. Animal gender had significant effects on beneficial FA ratio, with females having higher PUFA/SFA ratio and lower ω-6/ω-3 ratio than males (p < 0.05). On average, no effect of farming system was found on antioxidant content or eating quality evaluated by untrained consumers (Table 4), and animal gender had significant effects on three eating quality traits evaluated by untrained consumers, with higher flavor liking, overall liking, and MQ4 scores for females (p < 0.05) (Table 4).

Relationships between Animal and Carcass Characteristics and Meat Eating Quality Attributes

Animal age, marbling score, and conformation score were more related to eating quality perceived by panelists than consumers (Figure 1A). Carcass weight and hump height were more correlated with eating quality perceived by consumers. The two ellipses in Figure 1A represent the core area of the two farming systems, which are obviously not discriminated. Moreover, according to hierarchical cluster analysis (Figure 1B), carcass characteristics were clustered more closely to consumers' evaluated eating quality than panelists' evaluated eating quality.

The correlations between all the traits were confirmed by Pearson correlation coefficients. Some are indicated as being significant in Figure 1B, and others are partially presented below. Marbling was positively correlated with age (r = 0.38, p < 0.05). The ω-6/ω-3 ratio was negatively correlated with ossification score (r = -0.45, p < 0.05). CIE L* was negatively correlated with vitamin E content (r = -0.49, p < 0.05). Hump height had a negative correlation with all consumers evaluated eating quality traits, MQ4 score and overall tenderness evaluated by panelists (r = -0.46 to -0.50, p < 0.01). Similarly, carcass weight was negatively correlated with flavor liking (r = -0.43, p < 0.05). EU fat score was positively correlated with consumer-assessed MSA quality grades (r = 0.38, p < 0.05). One result of the current data is that consumer eating quality scores had no significant correlation with WBSF, and was only correlated with the panelists' scores for juiciness (r = 0.4, p < 0.05). Tenderness and juiciness evaluated by panelists were negatively correlated with WBSF (r = -0.65, -0.36, p < 0.01). In addition, contents in SOD, some ω-3 (EPA, DPA), and some ω-6 (ARA) PUFA were positively correlated with consumers scored tenderness, flavor liking, and overall liking (r = 0.36 to 0.51, p < 0.05). Panelist-evaluated tenderness was positively correlated with the content of total lipids, SFA, MUFA, ω-6 PUFA, and certain ω-3 PUFA (EPA) (r = 0.37 to 0.46, p < 0.05) and negatively correlated with ω-6/ω-3 ratio (r = -0.43, p < 0.05). Panelist-evaluated juiciness was correlated with catalase content and ω-6/ω-3 ratio (r = 0.49, -0.45, p < 0.05). Flavor evaluated by panelists had no correlation with FA content, but abnormal flavor was positively correlated with the content of total lipids, SFA, C16:1n-7, C18:1n-9, C18:1 9trans, total MUFA, MUFA cis, total CLA, and CLA 9cis11trans (r = 0.37 to 0.44, p < 0.05) and negatively correlated with DHA content (r = -0.36, p < 0.05). Correlation analyses with FA contents were performed on data from 30 animals, since the abnormal FA content of one sample was not considered due to a sampling problem.

The Eating Quality of Angus x Salers Beef Evaluated by Consumers

To assess the eating quality level of the Angus x Salers beef samples (Table 5, line A), a European consumer-testing dataset (Table 5, line B) from a wide range of experiments conducted with a large diversity of European cattle types and beef cuts [24] was utilized in the comparative analysis. The data comprised 743 animals and 86,624 beef tests. This dataset was used as a means to reflect an average and diverse eating quality level (based on the four MSA quality grades), and if the eating quality level of the Angus x Salers beef is higher than that of the European beef samples, a "better than average" quality might be reached. For Angus x Salers beef samples, the highest proportion was assigned to 5 star (27% versus 11% for European dataset), followed by 4 star (30% versus 24% for European dataset) and 3 star (26% versus 39% for European dataset), while only 17% of Angus x Salers beef samples were assigned 2 stars (versus 26% for European dataset), these differences being significant (p < 0.001) (Table 5, lines A andB). Thus, for the Angus x Salers beef samples, fewer samples had low-quality grades (2 star and 3 star), and more samples

The Eating Quality of Angus x Salers Beef Evaluated by Consumers

To assess the eating quality level of the Angus x Salers beef samples (Table 5, line A), a European consumer-testing dataset (Table 5, line B) from a wide range of experiments conducted with a large diversity of European cattle types and beef cuts [24] was utilized in the comparative analysis. The data comprised 743 animals and 86,624 beef tests. This dataset was used as a means to reflect an average and diverse eating quality level (based on the four MSA quality grades), and if the eating quality level of the Angus x Salers beef is higher than that of the European beef samples, a "better than average" quality might be reached. For Angus x Salers beef samples, the highest proportion was assigned to 5 star (27% versus 11% for European dataset), followed by 4 star (30% versus 24% for European dataset) and 3 star (26% versus 39% for European dataset), while only 17% of Angus x Salers beef samples were assigned 2 stars (versus 26% for European dataset), these differences being significant (p < 0.001) (Table 5, lines A and B). Thus, for the Angus x Salers beef samples, fewer samples had low-quality grades (2 star and 3 star), and more samples have high-quality grades (4 star and 5 star). Then, the consumer was asked to assign each sample to one of the four MSA quality grades [2 star (unsatisfactory), 3 star (good everyday), 4 star (better than everyday), and 5 star (premium)] best describing their overall evaluation of the sample.

For a more relevant comparison of eating quality scores (tenderness, juiciness, flavor liking, overall liking, and MQ4) of Angus x Salers beef samples with that of the European dataset, we used striploin and bolar blade samples only, since data on internal flank plates are not available in the European dataset. Indeed, the Angus x Salers beef samples had higher scores for all eating quality traits (Table 6).

Discussion

Carcass Characteristics

By comparison with young Angus or Salers bovines slaughtered over decades in the same processing plant and assessed with the same measurements [25], the Angus x Salers animals of this study were lean based on fat percentage (19.52% for Angus and 16.07% for Salers versus 15.35% for Angus x Salers). Furthermore, the 2.92% of total lipids of the current animals is generally considered lean [26]. Grass feeding and the young age of our animals are likely the main reasons for this leanness [27].

Differences in fat percentage based on hot carcass weight between the two systems were observed for females only. In general, animals reared and fattened on intensive feeding systems show a tendency to have higher intramuscular fat (IMF) content, with females likely depositing more IMF than males [28]. However, this is not always the case, e.g., likely due to the lower intake energy levels, no difference in IMF content between sexes of feral cattle was observed under feral conditions in Doñana National Park, Spain [29]. This may fit with our observations that fat cover and marbling remained unaffected by gender and farming system due to the low energy of the grass-based diet in the current study.

In addition, no differences were found in meat color between females and males or between the two farming systems, which confirms previous findings that different rearing managements had little or no impact on beef color [30]. It is somewhat notable that the current beef samples were likely to be dark based on the low values of lightness and redness (CIE L* = 31.3, a* = 14.0), in particular when comparing to dark-cutting beef [31]. This is in accordance with the fact that grass-fed/finished cattle tend to produce darker meat [32], with high pH caused by a lower muscle glycogen content at the time of slaughter. However, the possible interference of pH can be ruled out, since the current pH was generally compliant (<5.7) and was not correlated with L* value in the present analysis. Moreover, CIE L* and a* values of the current samples (from around 200-day grass-fed animals) were numerically lower than in a study conducted with longissimus dorsi et thoracis muscle from 199-day grass-fed Charolais and Limousin bulls that had CIE L* and a* values of 46.5 and 18.0 [33]. This suggests the current beef may indeed be dark. It was demonstrated that beef color is considered acceptable for consumers when a* values are ≥ 14.5 [34], the current beef being close to this threshold, which may indicate that the current meat color is not too dark to be unacceptable for the consumer. In addition, grass diets can significantly improve beef antioxidant contents, preventing the oxidation of oxymyoglobin to metmyoglobin, which leads to negative changes in meat color [35]. However, given the low number of correlations between meat color and antioxidant contents in the present study, the low value of CIE L* may be explained by the specific crossbreeding and/or insufficient blooming time of the beef samples, which is recommended to be longer [36], hence sufficient oxidation of pigment had not fully occurred.

Fatty Acid Profile

FA content was unaffected by the interaction of animal gender and farming system. However, the females had a beneficially higher PUFA/SFA ratio and a lower ω-6/ω-3 ratio than males, which is likely due to the association between animal maturity and FA deposition. Females were more physiologically mature than males based on ossification score, with the ω-6/ω-3 ratio being negatively correlated with ossification score (p < 0.05), indicating that more mature females tended to deposit more PUFA (especially ω-3 PUFA) than males. The farming system mostly affected the SFA and MUFA contents, with only MIX animals having higher levels of those FAs than MONO animals. This may also be attributed to different animal maturity. It is well established that grass feeding can significantly improve beneficial FA composition with higher ω-3 PUFA content and lower ω-6/ω-3 ratio [2]. Indeed, the current grass-based diet and crossbreeding strategy resulted in a low ω-6/ω-3 ratio (1.6 relative to the recommended target of <2-3) [2]. The contents of SFA and MUFA increase faster than the content of PUFA with increasing fatness, and consequently, the relative proportion of PUFA and the PUFA/SFA ratio decrease. Hence lean and late maturing breeds would have a higher PUFA/SFA ratio than early-maturing breeds when slaughtered at the same carcass weight [2]. This fact can support our finding that the PUFA/SFA ratio of current animals was 0.23, which can be considered moderate, since beef PUFA/SFA ratio is generally low, around 0.1, except for very lean beef, where the PUFA/SFA ratio can be easily reach the recommended values for human nutrition (>0.4) [26]. By comparison of the current FA composition with that of grass-fed beef [37], the current animals presented quite good ω-3 PUFA composition with high content of ALA (51.3 mg/100 g muscle relative to a range of 28.1-52.8 mg/100 g muscle), EPA (11.0 mg/100 g muscle relative to a range of 5.8-24.5 mg/100 g muscle) and DHA (4.3 mg/100 g muscle relative to a range of 1.5-4.2 mg/100 g muscle), despite the fact that these comparisons may depend on genetic makeup, breed, gender, age, and geographic location, which all affect FA compositions [27]. Consumption of ω-3 PUFA is linked to a variety of health benefits, notably in the prevention of cardiovascular diseases. However, the Western diet is largely deficient in ω-3 PUFA, with the actual daily intake achieving only up to half the recommended amount (estimated actual consumption in the Western diet: 0.9 g/d ALA, 137 mg/d EPA and 101 mg/d DHA; recommendations: 1.8 g/day ALA, 250 mg/d EPA, 250 mg/d DHA) [38]. Consumption of 70 g of the current Angus x Salers beef can provide around 7.2 mg EPA. High ω-3 PUFA content can benefit human health, but the oxidation of ω-3 PUFA would produce health-impairing factors. However, in comparison with the literature on grass-fed beef [27,39], the antioxidant contents of our Angus x Salers animals seem outside an optimal range. This might produce health-risk components from ω-3 PUFA oxidation.

Eating Quality

No effect of farming system was found on eating quality, while significant effects of gender were detected by consumers, with higher scores of flavor liking, overall liking, and MQ4 for females. As expected, panelist assessments of eating quality of the beef samples from Angus x Salers animals seem better than that of purebred Salers (i.e., the panel scores of overall tenderness, juiciness, and flavor were 7.5, 7.3, and 6.8 for striploin of the present Angus x Salers animals, 5.5, 5.6, and 5.6 for striploin of young Salers bulls, and 5.1, 5.7, and 5.9 for striploin of Salers cull cows [40]). Although data from different experiments are not comparable, a mean value greater than approximately 7 for all eating quality traits may indicate an average or greater eating quality for Angus x Salers animals, despite the abnormal flavor, which was evaluated as extremely high (close to 10). This fits with earlier observations, which showed that consumers prefer eating quality of grain-fed beef because grass-fed beef contains "abnormal" pastoral flavor characterized as "grassy", "wild" and "barny" and lacks normal beef flavor (reviewed by Pogorzelski et al., 2022 [41]). In addition, aroma volatiles are related to FA composition [42] with correlations found between abnormal flavor scores from panelists and total lipids, SFA, MUFA, CLA, and some ω-3 PUFA (DHA) contents. The oxidation of ω-3 PUFA might be associated with the high abnormal flavor score [2]. In this study, most correlations between sensory traits from panelists and FA contents were positive. The only negative correlation was between abnormal flavor and DHA content. No significant correlation was found between sensory traits from panelists and PUFA content. These observations differ from the findings by Ellies- Oury et al. (2021), which indicated that with longissimus thoracis muscles from young Charolais bulls, sensory traits (juiciness, overall liking and flavor) evaluated by panelists were negatively correlated with PUFA proportions [43]. This may be due to different animal types and production systems, and also different FA units (content or proportion) used in the different studies. Moreover, we found that consumer-scored tenderness, flavor liking, and overall liking were positively correlated with some ω-3 (EPA, DPA) and some ω-6 (ARA) PUFA contents. It can be concluded from those findings that the correlations and/or relationships between sensory traits and FAs can be variable according to animal types and farming systems.

Most importantly, on average, the current beef samples had higher consumer eating quality scores and a higher proportion of samples were assigned to the higher MSA quality grades of 4 star and 5 star, with a lower proportion assigned to being unsatisfactory than the beef samples from the European dataset. In general, IMF highly contributes to beef eating quality with strong correlations (r = 0.74 to 0.88, p < 0.05) between IMF content and tenderness, juiciness, flavor liking, and overall liking being reported [44]. The contribution of marbling score to the variability in MQ4 score is up to 51% for m. longissimus thoracis et lumborum [45]. However, in the present study, no significant correlation was found between marbling score and eating quality scores assessed by both consumers and panelists. This absence of significant correlation may be somewhat explained by the low range of variability of marbling score, and a greater number of animals with a larger phenotypic range may be warranted. Furthermore, this may also be explained by the fact that other factors aside from marbling affect beef eating quality [46] and the marbling effect may have been overshadowed by these other factors. For instance, consumer scores are related to FA composition, mainly ω-3 and ω-6 PUFA (i.e., EPA, DPA, ARA) contents, whereas tenderness evaluated by panelists was more related to total lipids, SFA, and MUFA contents. Consistent with the literature [47], ω-6/ω-3 ratio had a negative correlation with tenderness and juiciness. In fact, given that the current animals were young and lean, specific relationships might exist between animal and muscle characteristics and eating quality traits. Indeed, beef eating quality is multifactorial determined, for instance, with various effects of marbling depending on muscle cut [45] or with a low effect of breed on eating quality [48].

The WBSF measurement was used additionally in this study to provide more repeatable measurements of tenderness. It provides also indications of the strengths of the connective tissue and myofibrillar structures [49]. In the study of Dransfield et al., (2003) [40] with three muscles aged 14 days from four breeds, three quality classes (lowest, intermediate, and highest) were derived based on panelists' eating quality assessment, with WBSF values of raw meat from the highest to the lowest eating quality classes being 79.4 to 94.1 N/cm 2 . Our result of WBSF on raw meat (77.1 N/cm 2 ) suggests that the tenderness of Angus x Salers beef might be in the tender category (the highest-quality class set by Dransfield et al. (2003) [40]). Consumer assessment of tenderness and instrumental measures of tenderness have been extensively studied and their correlations were generally weak [r = -0.19 [50], -0.26 [51]] in most studies, but sometimes can be higher (r = -0.72) [52]. A higher correlation between panelist-scored tenderness and WBSF (r = -0.82) has been reported [53]. Our results confirm a weak correlation (p > 0.05) between consumer-assessed tenderness and WBSF and a moderate but significant correlation between panelist-assessed tenderness and WBSF. Although WBSF measurement is not a good indicator of consumer perception of meat eating quality, since the former is not only related to mechanical force but also associated with the sensations generated by moisture and fat within meat, whereas panelist assessment of quality can be reconciled to a certain extent with WBSF. This is consistent with [START_REF] Perry | Relationship between Objective Measurements and Taste Panel Assessment of Beef Quality[END_REF] [54], who found that consumers had the ability to detect any improvement in eating quality, but the improvement cannot be measured by WBSF. Conversely, panelists could better discriminate tender and tough meat in accordance with WBSF. This can further support the finding of no correlation between consumers' and panelists' assessments of beef eating quality. Trained panelists and untrained consumers may have different emphases when tasting beef; for example, as panelists are more qualified and guided by more granular eating quality traits, they may intentionally focus on certain aspects that determine eating quality, such as abnormal flavor, which may be not noticed by untrained consumers. Although both methods have been found to be effective in assessing the eating quality of meat, this uncorrelated relationship is understandable given the different processing protocols (i.e., sample size and cooking temperature) and scales of the two methods. Moreover, the low number of animals and beef samples potentially with a low range of variability of eating quality can also explain these uncorrelated results.

Conclusions

This study demonstrated that young grass-fed crossbred Angus x Salers animals can produce beef of "better than average" eating quality with low intramuscular fat content, high ω-3 PUFA content, and a low ω-6/ω-3 ratio. This is of importance due to the paucity of research on grass-fed Angus x Salers animals. FA composition had correlations with beef eating quality in varying extents: consumer-scored tenderness, flavor liking, and overall liking were mainly related to ω-3 and ω-6 PUFA contents, and panelist-evaluated tenderness was more correlated with the contents of total lipids, SFA, and MUFA. The ω-6/ω-3 ratio was positively correlated with WBSF and negatively correlated with panelistscored tenderness and juiciness.

To summarize, crossbreeding and especially the grass-feeding strategy could contribute together to good animal performances in terms of FA profile and meat eating quality. Although the association of beef cattle and sheep within a mixed system had very little or no impact on carcass or meat attributes, the findings of this study have important implications for future practices: (1) beef cattle and sheep mixed farming systems, which have agroecological benefits, can be implemented without undue penalty to meat quality;

(2) Angus x Salers crossbreeding with a grass-feeding strategy is relevant due to the higher beneficial FA composition and above-average eating quality.

Supplementary Materials:

The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/foods11162493/s1, Supplementary Materials File S1: I-Animal feeding, management and performances; II-Critical description of carcass grading, consumer test and sample preparation and Quality Assurance. References [19,22]

French consumer evaluation of eating quality of Angus x Salers beef: effects of muscle cut, muscle slicing and ageing

This section is the version that was submitted for publication in Meat Science.

Jingjing Liu, Liselotte Pannier, Marie-Pierre Ellies-Oury, Faustine Noel, Sophie Prache, Isabelle Legrand, David Pethick & Jean-François Hocquette. French consumer evaluation of eating quality of Angus x Salers beef: effects of muscle cut, muscle slicing and ageing. Meat Science.

Introduction

Beef quality is a convergence between the quality characteristics of the product on one hand and consumers' demands and expectations on the other hand (Grunert et al., 2004;[START_REF] Hocquette | The challenge of quality[END_REF]. With increasing consumer awareness of food production including the adding value of meat quality, such as health and safety value, and consumers' inquisitiveness and mindfulness around sustainability of animal production, these factors (healthiness, safety, sustainability, etc.) are more and more incorporated within consumers' expectations of beef quality (Clinquart et al., 2022). So far, to improve meat production and economic efficiency, more productive commercial breeds with better quality traits have been used and local breeds are at a risk of being replaced by these breeds. To better meet consumers' expectations, there is a growing interest to promote innovative farming systems based, for instance on animal crossbreeding, local breeds and feeding for a more flexible management and a more productive, diverse and sustainable supply chain (Aydin, 2021). Indeed, crossbreeding provides animals the opportunity to express their genetic potential by exploiting heterosis effects and the complementarity between traits [START_REF] Leroy | Review: Sustainability of crossbreeding in developing countries; definitely not like crossing a meadow[END_REF]. Proper implementation of crossbreeding can induce substantial increases in animal performance and production, promote more efficient farming systems and favor integration within a reliable market chain [START_REF] Leroy | Review: Sustainability of crossbreeding in developing countries; definitely not like crossing a meadow[END_REF]. As such the sustainability of local livestock production can be enhanced. For instance, in France, local breeds such as Salers are more adapted to local harsh production environments with a rustic profile that might be favored by local consumers (Casabianca & Matassino, 2006). On the other hand, early-maturing breeds such as Angus, are well adapted to grass feeding and can produce meat of high quality with more marbling at a younger age, in comparison with late-maturing breeds. Therefore, the strategy of crossbreeding Angus and Salers could be an option to ensure both meat eating quality and sustainability of local meat production. The eating quality properties of beef depend mainly on the composition and characteristics of the original muscle (i.e. fiber type and intermuscular fat content, which vary based on genetics, production systems, pre-slaughter management and animal slaughter stress handling), as well as on the postmortem processes which are involved in the conversion of muscle to meat, and on the final processing of meat (ageing and cooking) (reviewed by Hocquette et al., 2012). This multitude of factors and their interactions make it difficult for beef producers to guarantee a consistent quality product for the consumers. Nevertheless, consumer demand for high-quality beef has been taken into account by some innovative livestock and grading systems that has provided the impetus for their development to control, evaluate and improve meat eating quality for consumers [START_REF] Gagaoua | Current research and emerging tools to improve fresh red meat quality[END_REF]. For instance, the Meat Standards Australia (MSA) system is an innovative meat eating quality grading scheme that is based on the prediction for eating quality of individual muscle cuts (Watson et al., 2008). The MSA meat quality prediction model is underpinned by the eating quality evaluation and MSA quality grades assessment of beef by untrained consumers and is determined by Critical Control Points (CCPs) from breeding, production, pre-slaughter, slaughtering and post-slaughter processing, and valueadding aspects (Watson et al., 2008;Bonny et al., 2018). These inputs are used to predict a single meat quality score (MQ4), which is calculated by the combination of weighted tenderness, juiciness, flavor liking and overall liking and is ultimately related to the MSA quality grades selected by consumers (Watson et al., 2008). A large amount of data has been collected utilizing the same MSA standard protocols to conduct large scale consumer testing and carcass grading from different animal breeds, production systems, muscle cuts and consumer groups of different origins. This allowed the design of a relatively robust model to predict beef eating quality (Hocquette et al., 2020;Watson, Polkinghorne, et al., 2008). However, in the MSA prediction model, some factors should reach a minimum value to be included: for example, very lean carcasses with less than 3 mm rib fat thickness would be excluded for an accurate prediction (Polkinghorne et al., 2019). This might be the case in France since beef animals are generally leaner than Australian ones. Nevertheless, some breeds beyond the threshold can still produce beef of good quality, and it is also necessary to examine if the prediction model works well with these animals. Furthermore, the contribution of the different animal/carcass characteristics to beef eating quality varies between animal types and breeds (Chriki et al., 2013), although differences in eating quality between breeds are often small (Conanec et al., 2021). In the MSA grading system, breed adjustment is limited to Bos indicus vs. Bos taurus through the indication of the percentage of Bos indicus (Watson et al., 2008;Bonny et al., 2018). However, there can be various combinations of breeds and livestock systems, which generate distinct associations between animal/carcass characteristics and beef eating quality. This is the reason why many studies have been conducted in different countries utilizing the same MSA protocols, and testing its applicability for predicting beef consumer outcomes in European and other countries (Hocquette et al., 2020). Herein, it is relevant to analyze more variable data provided by particular livestock systems and specific animals for a more robust and generic prediction of beef eating quality with less bias. Consequently, this study was designed to examine whether the MSA beef grading system could be used to predict beef eating quality from grass-fed Angus x Salers crossbred animals with the current Australian prediction model with carcass grading data collected at the time of slaughter. To be consistent with the MSA prediction outcomes of "muscle x ageing x cooking" combinations, different muscle cuts, ageing times and muscle slicing methods were used. Each beef sample was scored for tenderness, juiciness, flavor liking and overall liking with also the assessment of MSA quality grades as an overall satisfaction (Watson et al., 2008). This study may offer important insights into 1) the current MSA prediction model since Angus x Salers animals have been rarely studied before; 2) the French beef sector since it is not common to produce and consume beef from such young crossbred animals in France.

Methods and materials Carcass grading and trait measurements

Thirty-one grass-fed Angus x Salers crossbred animals (16 females and 15 castrated males) were reared in the INRAE experimental farm and slaughtered at a young age (around 14 mouths). In France, beef animals are generally not slaughtered at such a young age. For instance, for Label Rouge beef, beef animals are slaughtered at the age of around 30 months (Raulet et al., 2021). Animals were transported the day before and slaughtered early morning at the experimental slaughterhouse of the INRAE Research Centre in compliance with ethical guidelines for animal care. Transport time from the farm was about 1 h to 1.5 h and animals had free access to water until slaughter. Animal were percussion stunned followed by immediate exsanguination. Electrical immobilization and electrical stimulation to accelerate rigor. Carcasses were hung by the Achilles tendon. Carcasses were graded 24 h post-slaughter minimum by a certified grader under the auspices of AUS-MEAT and the International Meat Research 3G Foundation for ossification, marbling, rib fat thickness and hump height following the MSA chiller assessment standard (AUS-MEAT, 2018). Beef ultimate pH was measured on m. longissimus dorsi et thoracis at quarter site using a portable pH meter at 24 h postmortem minimum. Carcass characteristics and age of the animals used are presented in Table 1. Based on the marbling score and rib fat thickness, the current Angus x Salers animals were considered lean. et al., 2019) were collected from each carcass at 24 h postmortem minimum. These muscle cuts were selected as valued muscles from a commercial perspective for France in order to complete previous work (Legrand et al., 2013) with the MSA approach. There were 5 sample types prepared from these three muscle cuts to represent different ageing times and muscle slicing methods: striploin aged for 14 days; bolar blade aged for 5 days; bolar blade aged for 14 days; two sets of internal flank plate aged for 14 days and sliced in two ways with different muscle orientations. According to the MSA sensory testing protocols (Watson, Gee, et al., 2008a), striploin and bolar blade were prepared according to the normal grilling standard as long blocks. Starting at the anterior, or proximal end of the block, five 75 x 60 x 25 mm steaks were sliced across the grain, using a cutting guide. Internal flank plate was sliced in two ways (Figure 1). Internal flank plate is generally cooked as stir fry or thin slice of slow cook instead of grilling in the MSA sensory test. According to the French cooking habit, internal flank plate is generally prepared in large and flat slices (the thickness of this muscle cut is often less than 25 mm) and grilled on the flat part (French slice Ⅰ, Figure 1). To be consistent with the thickness standard of MSA grilling, the so-called French slice of internal flank plate was prepared, as close as possible, to a thickness of 25 mm. Since all the samples must be cooked at one time, 25 mm thickness was applied on all the samples. Considering the standard of cooking internal flank plate as stir fry or thin slice (stir fry is prepared as 75 x 10 x10 mm long piece of beef cut across the grain in the MSA sensory testing), the other batch of internal flank plate was prepared as 75 x 25 x 25 mm slice (French slice Ⅱ, Figure 1), which was used as a control (based on MSA stir fry) to compare with the French way of cooking internal flank plate.

French slice Ⅰ muscles were cut along the fibers (cutting perpendicular to the muscle fibers is rarely used, as it can lead to meat exudation). Therefore, these two muscle slices are mainly in difference of muscle orientation and sample size. The aim of these two preparations is to collect MSA data from French consumers with this important muscle cut, which is regularly consumed in France. Five days are the minimum ageing time in the MSA system and 14 days are a relevant period often used in experimental work [START_REF] Jurie | Muscle and meat quality characteristics of Holstein and Salers cull cows[END_REF] and appropriate to compare with 5 days ageing. These different ageing times and muscle slicing ways were applied on different cuts also due to the limited number of samples and due to the small size of some cuts in such young and lean animals. In total, 155 samples were collected for subsequent consumer testing sessions with 31 striploin cuts, 62 bolar blade cuts and 62 internal flank plate cuts. Before consumer testing, samples were stored in vacuum pack bags at 4°C according to their own ageing time then after transferred to -20°C being frozen.

Consumer testing

According to the MSA sensory testing protocols (Watson, Gee, et al., 2008a), each sample was sliced into five 25 mm thick steaks. Each steak was cut in half after cooking and 10 portions of steaks from one muscle cut were served to different consumers.

Steaks were cooked on a Silex clamshell grill set to 200°C for 2.5 minutes up to an internal temperature of 55-57°C and a rare degree of doneness. Each consumer evaluated in total seven steaks including the first sample as a starter steak (striploin) then followed by six steaks, which were served in order based on a 6 × 6 Latin square design allowing for diverse quality levels and balancing order effect across consumers. Consumers evaluated each steak based on tenderness, juiciness, flavor liking and overall liking by scoring them on a 0 to 100 mm scale, with the left side of the scale representing not tender, not juicy, dislike extremely and the right side representing very tender, very juicy and like extremely. Consumers also selected one of four MSA quality grades indicated as 2star (Unsatisfactory), 3star (Good Everyday), 4star (Better Than Everyday), and 5star (Premium) for each sample, to best described their overall quality perception of the sample. In this study, a total of 360 untrained French consumers were recruited to participate in one of the 6 testing sessions with 60 consumers per session.

Statistical analysis

The calculation of the combined meat quality score (MQ4) was in accordance with the procedures reported by Watson et al. (2008). To create one composite score, a weighted combination was established based on tenderness, juiciness, flavor liking and overall liking and determined by using a discriminant analysis function. The linear discriminate function was used to develop the MQ4 grade boundaries, which reflect in the best possible way the MSA quality grades [2star (Unsatisfactory), 3star (Good Everyday), 4star (Better Than Everyday), and 5star (Premium)] given by the consumers. The linear discriminate function establishes the optimum boundaries that separate the adjacent quality grades (2/3 star, 3/4 star and 4/5 star). Based on this principle, a four-scale approach for the four quality traits (tenderness-tn, juiciness-ju, flavor liking-fl and overall liking-ov) was firstly used to calculate the weightings of these four traits. Secondly, a three-scale approach including tn, ju and fl was used to calculate the weightings of these three traits only without ol. Finally, the optimal MQ4 weighting was calculated as a mid-point between the weightings from the three-and four-scale approaches (Watson et al., 2008). The actual observed MQ4 scores were calculated according to the above process.

In the MSA prediction model, by using a multiple regression approach, input variables from the production, processing and value adding sectors were used to predict the MQ4 score of individual muscles. The data collected from the current animals/carcasses were used in the current Australian MSA prediction model to obtain the predicted MQ4 scores using the SP2009 version. Pearson correlation analysis was thereafter performed to determine correlation coefficients between predicted MQ4 scores (calculated from the MSA prediction model) and observed MQ4 scores (calculated based on consumer scores on tn, ju, fl and ol). Notably, the current MSA prediction model only predicts internal flank plate as stir fry or thin slice instead of grilling, the predicted MQ4 score of internal flank plate is for stir fry. The prediction of MQ4 for striploin and bolar blade is in accordance with corresponding cooking method (grill).

Variance analysis was performed to examine the difference in eating quality scores between muscle cuts, ageing times and cooking methods, by using the general linear model with the post-hoc (Tukey HSD) test including the Bonferroni correction for pairwise comparisons, the significance being determined at a Bonferroni-corrected P<0.05.

To estimate the actual observed MQ4 score (calculated based on tn, ju, fl and ol) from the main carcass attributes (carcass weight, hump height, ossification, age, marbling and pH), linear regression models were established by using "lm" (linear model) function. All statistical analyses were performed with the R software (4.1.1).

Results and discussion MQ4 as a definition of beef overall eating quality for French consumers

As shown in Table 2, for each type of beef sample, tn, ju, fl and ov scores and their ranges are of the same order of magnitude. This indicates that these eating quality traits are consistent across consumers. Meat eating quality can be adequately described by tn, ju, fl and ol (Watson, Gee, et al., 2008b). The idea of combining these four traits into one single score to best specify overall eating quality as ultimately indicated by the MSA quality grades was proposed in the MSA grading scheme (Watson, Gee, et al., 2008b). The optimal MQ4 weightings were calculated as a compromise between the three-(tu, jn, fl) and four-scale (tu, jn, fl, ol) weightings, the latter with ol as a component to improve the discriminatory efficiency and stability by smoothing the varying importance of the other three scales across the grades (Watson et al., 2008). The weightings of the three sensory traits (tn, ju and fl) obtained in this dataset were 0.46, 0.01 and 0.53. The weightings of the four sensory traits (tn, ju, fl and ov) were 0.26, -0.05, 0.06 and 0.73. Finally, the optimal weightings of the four sensory traits (tn, ju, fl and ov) to predict the MSA quality grades (2star, 3star, 4star and 5star) from the MQ4 score were 0.36, -0.02, 0.29 and 0.37 as calculated through a compromise between both approaches (Table 2). Australia allowed updating with more data each year to optimize MQ4 weightings of eating quality traits from 0.4 to 0.3 for tn (Bonny et al., 2018;Pethick et al., 2021). The highest contribution of fl has been also observed recently with the European consumer responses (Liu et al., 2020). The optimal weights also vary between countries for tn (0.18-0.31), ju (0.01-0.23), fl (0.24-0.43) and ol (0.24-0.39) (Bonny et al., 2016;Legrand et al., 2013;[START_REF] Polkinghorne | A comparison of Japanese and Australian consumers' sensory perceptions of beef[END_REF][START_REF] Thompson | Beef palatability in the Republic of South Africa: implications for niche-marketing strategies[END_REF]. Comparison of the weightings with those of other studies confirms that ju contributes less to the MQ4 score, and the current data seems provide the smallest value (-0.02) ever reported. However, a very low weighting for ju was also observed in a previous French study with different degrees of doneness (0.01 for rare doneness, 0.02 for medium doneness) (Legrand et al., 2013). A possible explanation for this might be that, for French consumers, ju did not play an important role in overall satisfaction of eating quality, whereas tn, fl and ol were more valued. However, as it was demonstrated by Liu et al. (2020), the perception of ju can be compensated by other traits, especially tn and ol, since they are strongly correlated with each other (in this experiment, the current correlation coefficients between tn, ju, fl, ov and MQ4 score are 0.97, 0.79, 0.97 and 0.99 respectively, P<0.001). The lower weighting of ju does not mean it is not important, although from a statistical perspective, it might be less significant. But from a practical point of view, any trait cannot be ruled out and they are all contributing to the overall perception of beef eating quality.

French consumer assessment on the MSA quality grades of Angus x Salers beef

The distribution of the French consumer MQ4 scores of the Angus x Salers beef samples in different MSA quality grades are presented in Figure 2. The distribution of the MQ4 scores is displayed within each of the MSA quality grades to which each sample was assessed by the individual consumer. The optimal boundaries that discriminate the four quality grades according to the MQ4 scores are 40.2, 61.1 and 79.1 for the 2/3 star, 3/4 star and 4/5 star boundary, respectively. For example, if the MQ4 score of a sample is above 79.1 then it would be assigned into 5star (Premium) and if it is below 40.2, then it would be rated as 2star (Unsatisfactory). The boundaries obtained in our study (Figure 2) are all within the ranges that have previously been published [2/3 star (35.9-43.7), 3/4 star (57.5-69), 4/5 star (70-83.9)] (Bonny et al., 2016;Legrand et al., 2013;Polkinghorne et al., 2011;Smith et al., 2008;Thompson et al., 2010) but slightly in the upper range of grade boundaries. The results also align very well with the boundaries observed in an earlier study (38,[START_REF] Bryant | Strategies for overcoming aversion to unnaturalness: The case of clean meat[END_REF] and 80 for 2/3 star, 3/4 star and 4/5 star) in which French consumers assessed beef samples from 18

French beef and dairy animals (young dairy cows and bulls, cull dairy and beef cows), which were selected to reflect common commercial production provided to French consumers (Legrand et al., 2013). This indicates that, on one hand, the French consumer assessment on beef quality according to the MSA standard remains unaffected by the specificity of the Angus x Salers crossbred animals, on the other hand, the beef eating quality of these grass-fed animals would be within the common range of acceptance by French consumers. Additionally, in total more than half of the samples were assigned as 4star (Better Than Everyday) and 5star (Premium) grades, suggesting that most of the current Angus x Salers beef samples was at least above average eating quality for French consumers.

Accuracy of MQ4-assigned quality grade

The current accuracy of MQ4-assigned quality grade relative to the actual consumerselected grade was on average 64.5% (Table 3). In other words, the function of MQ4 score allowed to assign around 65% of the samples to their correct grades selected by French consumers, which aligns well to the average accuracy achieved by Australian, Polish, Japanese and Korean consumers (61%-68%) (Polkinghorne et al., 2011;Pogorzelski et al., 2021). As reviewed by Pethick et al. (2021), one sustainable legacy of the MSA model is that consumers receive the beef eating quality they are prepared to pay for, and they would even be willing to pay more for a premium quality. Behind this, is the requirement for a quality grading system with a high degree of accuracy. In this case, the consistent accuracy obtained in our study indicates that the MSA consumer sensory protocol is relevant for French consumers, even for these beef samples from grass-fed Angus x Salers animals, which were never studied before using the MSA approach.

One hundred percent accuracy is impossible to achieve, and an average 64.5% of accuracy can be considered as relevant due to several reasons. First of all, the accuracy was higher for 2star and 5star quality grades assignments (75.6% and 77.7%, respectively), this is largely due to the fact that for those 2 grades, there is only one adjacent quality grade instead of two. It is indeed critically important to accurately predict "Unsatisfactory" and "Premium" quality grades, and easier for consumers to distinguish eating quality between "dislike extremely" and "like extremely". Secondly, as it was clearly demonstrated by Watson et al. (2008) and Polkinghorne et al. (2011), if the possibility that consumers' expectations to be met or even exceeded can be taken into account by the methodology of MQ4 quality grade assignment, the accuracy of quality grade identification would be higher than it actually could be. The overlapping of MQ4 scores between two adjacent quality grades but for the lower grade (Figure 2) indicates that, in some cases, consumer-selected grades were higher than MQ4assigned grades. For instance, they receive an MSA determined 2star beef product, but it could be above 2star quality according to their own eating experience. In this case, consumer' satisfaction of beef quality would be exceeded, or in other words, the quality grade of this beef product is underestimated by the quality grade. This is confirmed by our study that as shown in Table 3: 88.3%, 77.9% and 63.6% of 3star, 4star and 5star beef samples were well and even better estimated (34.7%, 21.2% and 15.3% were overestimated for 2star, 3star and 4star) by consumers compared with MQ4 assignment. Overall, on the basis of these last results, the MSA grading scheme is a powerful tool to meet satisfaction of the majority of consumers. 2star), this reflects the extent to which consumer expectations would be met by the MSA grading scheme; 2 Well estimated and overestimated: Percentage of real consumer-selected grade compared to the same grade or a lower grade after MQ4assigned quality grade (i.e. consumer-selected 2star, 3star and 4star vs. MQ4predicted 2star), this reflects the extent to which consumer expectations would be met or will be exceeded by the MSA grading scheme; 3 Accuracy: Accuracy of MQ4predicted grade compared to the real consumer-selected grade, this is the proportion of correct MQ4-predicted quality grade by the MSA grading scheme in proportion of real consumer-grade.

Prediction of beef eating quality using animal and carcass characteristics

A crucial part of the MSA grading scheme is the prediction of MQ4 scores aligned with the MSA quality grades using a range of production, processing, carcass grading and value adding factors, as routinely done in Australia for more than half of the commercial slaughtered cattle (53% in 2020-2021) (MLA, 2020). The predicted MQ4 score of each muscle in this study was obtained from the Australian prediction model. Based on the data collected from the current consumer evaluation for tn, ju, fl and ov, the observed MQ4 score for each muscle was calculated from MQ4 = 0.36 * tn -0.02 * ju+ 0.29 * fl + 0.37 * ol. To examine if the MSA prediction model works well with the current animals, beef samples and consumers used in this study, the correlations between observed MQ4 scores and predicted MQ4 scores was analyzed. These correlations between the actual observations and predicted MQ4 scores are not significant for the individual cuts (Table 4) nor for all the samples gathered (r=0. 15, P>0.05). Despite this, the mean differences between observed and predicted MQ4 scores were low for the STP045 and TFL064 (especially given the MSA prediction is for a stir fry) but usually relatively larger for the BLD096: they ranged from 1 to 13 on a 100-point scale (Table 4). The predicted MQ4 scores were lower than observed MQ4 scores by up to 13 points and hence generally, the quality score of the current beef samples was underestimated by the MSA prediction model, especially for BLD096. The current samples were cooked to rare for better meeting French consumers' preference of rare degree of doneness, whereas the MSA model is mainly based on cooking to medium. One reason for BLD096 to be largely underestimated by the MSA model might be that for French consumers, BLD096 is more suitable for rare cooking to deliver better eating quality.

In addition, the high value of RMSE reflects that in this study, the ability of the MSA model to predict the actual observed MQ4 scores is less accurate.

Although extensive research has been carried out based on the MSA protocol, the prediction has been mostly restricted to the most common breeds and crossbreeding between them in Australia as well as in other countries. Despite the majority of differences between breeds could be accounted for carcass traits, as it was demonstrated by Bonny et al. (2018), in some cases, the MSA prediction model still underestimates eating quality such as for dairy breeds (Bonny et al., 2016). With the development of MSA in Europe, a big dataset has been established containing around 20 beef and dairy breeds as well as crossbreds with varying genotypes, with a wide range of age (from 266 to 6133 days), ossification (from 100 to 590), marbling score (from 100 to 820) and rib fat thickness (from 0 to 25 cm) (Bonny et al., 2016;Liu et al., 2020). The MSA prediction model works well with those animals (Bonny et al., 2016).

Although the purpose of the crossbreeding with Angus is to produce meat with more marbling in comparison with Salers purebred, the current animals were young for France (mean chronological age -422 days; physiological maturity -130 ossification score) and lean (240 marbling score; 0.3 mm rib fat thickness). The underestimation of eating quality of the beef samples from the current animal by the MSA prediction model might be explained by the fact that our animals do not meet all MSA requirements. In fact, under the MSA carcass grading classification, carcasses with less than 3 mm rib fat thickness (100% of carcasses in the current study) would be excluded from the MSA grading system, since low or no fat covering for major muscles could cause an unacceptable gradient of temperature and pH condition (Polkinghorne et al., 2019). Also, if the ultimate pH is above 5.7, the carcass would be ineligible for MSA grading. There were 10 carcasses over 31 that had the ultimate pH over 5.7 in the current study, this could be one factor that influence the accuracy of the MSA prediction. In the MSA prediction model, by using a multiple regression approach, input variables including carcass attributes were used to predict the MQ4 score of individual muscles.

Here, as presented in Table 5, multiple regression models were run with the main carcass attributes (carcass weight, hump height, ossification, age, marbling and pH) to explore if MQ4 score can be predicted by the carcass attributes of the current animals. As such, indeed, the actual observed MQ4 scores cannot be explained by carcass weight, ossification, marbling and pH (Table 5, Model 1,3 and 5). However, in general, multiple regression used on low numbers is statistically weak. Despite this, as expected, we found that around 90% of the variability of the predicted MQ4 scores can be explained by those factors (Table 5, Model 2,4 and 6). This discrepancy is considered logical due to the reason explained above and to the fact that this specific animal type (Angus x Salers) has never been studied elsewhere, and certainly not considered in the current MSA prediction model. Due to the low values and low variability of carcass attributes such as rib fat thickness, marbling score and ossification score, which are used as critical MSA predictive inputs, the prediction of consumer perception of eating quality for these grass-fed animals (likely to be genetically specific) might be out of the range of standard MSA use. As demonstrated by Pethick et al. (2021), it is necessary that any grading system improves and evolves in response to new predictive inputs, and MSA indeed lays strong emphases on this.

To increase the scope of the MSA system and improve the precision of the quality prediction, adjustments with new types/breeds of cattle and the function of genetic information would be looked into. The three muscle cuts (n=31 for each cut) were 14 days aged and MSA grilling cooked to rare degree of doneness. 6 Proportion of variability of either observed MQ4 or predicted MQ4 explained by carcass attributes. As expected, this proportion is higher for predicted MQ4 (even if not equal to 100%) than for observed MQ4. *P < .05, **P < .01, ***P < .001.

Can consumers discern differences in eating quality traits under different conditions?

Two specificities of the MSA grading scheme compared to other grading systems are: i) the eating quality of individual muscle cuts is independently predicted instead of assigning a single grade to the entire carcass; ii) the ultimate eating quality is underpinned by untrained consumer sensory evaluation. Untrained consumer evaluation has evolved as a pivotal tool in meat quality research due to its capacity to reflect the actual consumer response with a great accuracy when testing samples with a wide range of quality variance (Hwang et al., 2008;Miller, 2017). Most of the variability in beef eating quality is attributed to different muscle types [START_REF] Jurie | Muscle and meat quality characteristics of Holstein and Salers cull cows[END_REF]. However, the effects of ageing and cooking on eating quality also varies with muscles, and differences can be detected by consumers based on the MQ4 scores (Bonny et al., 2018).

As shown in Figure 3, consumer scores of eating quality traits were significantly different between muscle cuts. BLD096 and TFL064 had higher consumer scores for all the sensory traits, while STR045 had the lowest scores (P<0.001), when aged 14 days. This finding contrasts with previous studies which found that STR045 had higher scores for tenderness, juiciness, flavor liking and overall liking than BLD096 (Liu et al., 2020). Bonny et al. (2016) found that for beef breeds, BLD096 had higher MQ4 score than STR045, but for cross breeds, STR045 had higher MQ4 score than BLD096. This indicates that consumers' evaluation of eating quality traits varies according to breeds and muscle cuts, and most importantly, the quality variances in different sensorial dimensions can be distinguished by consumers. This aligns very well with other published data indicating that consumers can distinguish the differences in eating quality traits with different cooking methods and beef thicknesses (Legrand et al., 2013;Pogorzelski et al., 2020). The advantage of the MSA grading scheme is that it relies on untrained consumers from different regions, who would be able to discern those differences. And those differences are indeed included in the MSA system for further updating. Moreover, Figure 3 shows the relationships between the three cuts within each animal/carcass with the STR045 having lower sensory score compared to BLD096 and TFL052 for all the sensory traits. Some inconsistent layouts of quality scores of different cuts can be observed within the same animal (three points connected inconsistently up-and-down by one grey line). This indicates that a difference can be discerned by consumers between muscle cuts although consumer preferences for beef quality are remarkably diverse. In addition, in Figure 3, quality traits of STR045 look more variable than for BLD096 except for juiciness, which contributes less to overall quality than other sensory traits. This further confirms that BLD096 had better eating quality, which should be consistent with less variability. This also in line with the previous assumption that BLD096 responds better to rare degree of doneness for French consumers (in Table 3, BLD096 was relatively largely underestimated by the MSA model than other two muscles). A lot of variations in sensory scores for each muscle across the different carcasses can be seen in Figure 3, the variations seem to be much more than suggested by the SE (standard error) estimated in Table 2. This suggests that there might be other sources of variability for these light and lean carcasses, for instance, related to slaughter practices and chilling conditions. Especially as the carcasses did not receive electrical stimulation. 6 presents the variance analyses of consumer sensory scores of BLD096 at the different ageing times (5 and 14 days). On average, no significant differences of consumer sensory scores were observed between 5 days and 14 days of ageing (P>0.05). However, with increasing ageing time, sensory scores at 14 days are about 2 points higher than those at 5 days, but this is non-significant on a statistical point of view. Moreover, the MSA model predicts no effect of aging between 5 and 14 days for the BLD096 and TFL064 (stir fry); 2-3 points for the STR045 depending on whether anterior of posterior. Whilst no difference was observed, the current finding is still consistent with other studies showing that ageing muscles from 5 to 21 days increases the sensory score of the striploin by up to 4 palatability units (Bonny et al., 2018). The BLD096 muscle cuts used in this analysis were cooked by grilling to a rare degree of doneness using the MSA protocol.

Table 7 presents the variance analyses of consumer sensory scores of TFL052 for different slice of muscle cuts with different muscle orientation. Interestingly, consumer score of juiciness of French slice was significantly higher than that of the MSA controlled slice (P<0.05). Apart from juiciness, no significant differences in other sensory traits were observed between two different ways of slicing meat samples (P>0.05). In fact, the two slice methods used in our study are technically similar with only differences in muscle orientation and sample size when grilled. The higher juiciness score of the French slice samples might be due to the fact that French sliced beef was bigger than MSA controlled sliced beef (Figure 1). Larger pieces of steak helped to keep more internal juice from spilling out during cooking. By contrast, with smaller piece of steak, meat juice spilled out more easily. Therefore, muscle orientation and/or sample size are important for consumers to perceive juiciness. Apart from juiciness, it seems that consumers would not be able to discern such subtle differences with the other three traits, this is understandable since tenderness and flavor liking are more associated with the original meat characteristics (i.e. muscle fiber, connective tissue and fat composition), which remain unaffected by muscle slicing or sample size. Nevertheless, it is still somewhat surprising that consumers could detect the difference of juiciness between two muscle slices in difference of muscle orientation and size. The TFL052 muscle cuts used in this analysis were 14 days aged. 1 French slice Ⅰ: 75 × 60 × 25 mm steaks were sliced along muscle fibers and grilled on the 75 x 60 mm side; 2 French slice Ⅱ grilling: 75 x 25 x 25 mm steaks were sliced across the grain and grilled on 75 x 25 mm thin side

Conclusion

For accurate meat eating quality guarantee and prediction, around more than 100,000 consumers having scored around 700,000 beef samples over the World using the MSA methodology. Through handling all factors that affecting beef eating quality including different beef sources in terms of animal breeds, production systems and muscle cuts, a robust modelling approach was established and is still being updated using standard protocols to grade carcasses and evaluate beef eating quality. To better meet consumers' demand in beef eating quality in addition with enhanced sustainability of local livestock production, a crossbreeding and grass feeding strategy was implemented in Auvergne-Rhône-Alpes of France for a better adaptation to the local mountainous pasture environment. Moreover, this experiment aimed to study if the MSA consumer sensory evaluation protocol and prediction model are suitable for grass-fed Angus x Salers crossbred animals in the French context. Furthermore, French consumers were able to discern differences in eating quality traits from different muscle cuts and ageing times for these crossbred animals. The most obvious finding from this study is that the MSA consumer testing protocol, the MQ4 calculation based on consumers' responses and its association with the MSA quality grads are relevant to develop a consumer-based grading system for beef eating quality prediction in France. However, due to the specificities of these French young and lean animals in association with some non-MSA-compliant carcass conditions, carcass grading might be less accurate to predict eating quality scores according to the current MSA prediction model. There is a need to develop further research more focused on specific crossbreds including new genetic types and local livestock production systems for a more accurate and robust modelling system to predict beef eating quality.

Discussion

By comparison with young purebred Angus or Salers bovines slaughtered over decades in the same abattoir and assessed with the same measurements [START_REF] Hocquette | Yield of Bovine Carcasses[END_REF], the current Angus x Salers animals of this study were lean based on fat percentage (19.52% for Angus or 16.07% for Salers versus 15.35% for Angus x Salers animals). Also in comparison with previous studies based on a wide range of animal data in Europe with marbling score (from 100 to 820) and rib fat thickness (from 0 to 25 cm) (Bonny et al., 2016;Liu et al., 2020), the Angus x Salers animals were indeed lean with 2.9% of total lipids, 240 of marbling score and 0.3 mm rib fat thickness. Grass-feeding and being slaughtered at a young age (422 days) are considered as the main reasons of this leanness [START_REF] Daley | A Review of Fatty Acid Profiles and Antioxidant Content in Grass-Fed and Grain-Fed Beef[END_REF]. In addition, it is well established that grass feeding can significantly improve the FA composition of beef with FA beneficial for the human health such as higher ω-3 PUFA content and lower ω-6/ω-3 ratio (Scollan et al., 2014). Indeed, the current grass-fed animals presented quite a good ω-3 PUFA content (DHA 4.3 mg/100g muscle; EPA 11.0 mg/100g muscle; ALA 51.3 mg/100g muscle), with a low ω-6/ω-3 ratio (1.6 relative to the recommended target of <2-3) (Scollan et al., 2014) and a moderate PUFA/SFA ratio (0.23 relative to the recommended target of >0.4) (Lukic et al., 2021). To evaluate the eating quality of the Angus x Salers animals, untrained consumer tests, trained panel evaluation and mechanical measurement (WBSF) were used. Overall, by comparing with some relevant results from previous studies such as with purebred Angus and Salers animals, we found that the Angus x Salers beef had a good score of tenderness and of specific flavor, but a high score of abnormal flavor which might be associated with the oxidation of ω-3 PUFA. Most importantly, in comparison with the large dataset used to represent an average eating quality of beef in Europe, more than half of the Angus x Salers beef were assessed by MSA quality grade as 4star and 5star ("Better Than Everyday" quality and "Premium" quality). Therefore, we ascertained that these young grass-fed Angus x Salers animals produced lean beef of "better-than-average" eating quality with a high ω-3 PUFA content and a low ω-6/ω-3 ratio. Along with the investigation of the eating quality of the current Angus x Salers beef, the MSA consumer testing and carcass grading protocols were used to examine if the MSA standards can support a consumer quality-based beef grading system in France. The proposal to create a single score to describe the overall meat eating quality (MQ4) based on the combination of tenderness, juiciness, flavor liking and overall liking was successful. In addition, the assignation of MSA quality grades based on the MQ4 score by French consumers is also relevant using these specific crossbred animals. French consumers categorized beef into the four MSA quality grades with an accuracy of 64.5%, which is within the normal range of results (61%-68%) previously obtained in other studies. In addition, the MQ4 weightings (0.36, -0.02, 0.29 and 0.37 for tenderness, juiciness, flavor liking and overall liking) and the optimal boundaries (40.2, 61.1 and 79.1) that discriminate the four MSA quality grades are all within the range obtained from other previous studies. In addition, we noticed that French consumers were able to discern differences in eating quality traits from different muscle cuts and ageing times for these crossbred animals. This all confirms that the MSA methodology can be practically used in the French context. Another crucial part of the MSA grading system is the prediction of the MQ4 score aligned with MSA quality grades using a range of production, processing, carcass grading and value adding factors, which is the core of the current Australian MSA prediction model. One of the objectives of this study was to examine how the MSA prediction model could predict the eating quality of Angus x Salers beef. There are some constrains in the MSA grading system such as carcasses with less than 3 mm rib fat thickness would be excluded from the prediction model. This is our case since the current animals are lean with less than 3 mm rib fat thickness. Hence, it may be not allowed for commercial purposes to use those MSA predictive inputs (i.e. ossification, age, marbling, rib fat thickness, etc.) to accurately predict eating quality of the current animals through the MSA prediction model. Despite that, the prediction of MQ4 score based on the current carcass grading data was still performed for research purpose only by using the MSA prediction model. As more or less expected, we found that there was no correlation between the predicted MQ4 score provided by the MSA prediction model and the actual MQ4 score calculated by combining consumer scores (MQ4 = 0.36 * tenderness -0.02 * juiciness + 0.29 * flavor liking + 0.37 * overall liking). Nevertheless, the most important finding from this study is that the MSA consumer testing protocol and the MQ4 indication are relevant to develop a consumer-based grading system for beef eating quality prediction in France. However, due to the fact these crossbred animals were young and lean, carcass grading might be less accurate to predict eating quality scores according to the current MSA prediction model.

Chapter 5. Perception, willingness and acceptance of cultured "meat" for consumers from different countries

The third part analyses the results of cultured "meat" surveys that were collected from different countries and focus demographic group discussions undertaken based on consumer perceptions, attitudes, willingness and potential acceptance of cultured "meat".

Study 1: Chinese consumers' attitudes and potential acceptance toward artificial meat

This study investigated Chinese consumers' attitudes, perspective, potential acceptance, and willingness to engage with artificial meat, and aims to provide a Chinese reference to the widest artificial meat market worldwide. Some of the major potential predictors of willingness to try, eat regularly, and pay for artificial meat, as well as potential reasons for willingness or non-willingness to try and perception about the feasibility of this novel product have been also explored.

The hypotheses to be tested in this study were:

Chinese consumer might be familiar with the concept of artificial meat. Based on the long history of vegetarian diets and the broad market of plant-based meat substitutes in China, Chinese consumers' attitudes toward artificial meat might be more open and positive than consumers from other countries. Chinese consumers would not be eager for artificial meat but would be willing to try.

Study 2: Does the future of meat in France depend on cultured muscle cells? Answers from different consumer segments This study explored French consumers' perception of cultured "meat" with rigorous discussion of willingness to try, to eat and to pay for cultured "meat" with different consumer segments.

The hypotheses to be tested in this study were:

Cultured "meat" would be likely to have a future in the coming years worldwide but French consumers might be the most reluctant to accept this novel product. Factors affecting acceptance of cultured "meat" may be specific in the French context. Different consumer segments would have different perceptions of cultured "meat".

Introduction

"Conventional meat production systems" (which produce meat from farm animals that have been raised on farms or ranches), are facing important challenges such as environmental and animal welfare issues, food safety, public health, and the need to face the increasing worldwide population and associated protein demand [1,2]. It is claimed that the ever-increasing demand for meat is unlikely to be met through conventional meat production, due to the limited arable land and water resources [3]. As such, despite improvements of conventional breeding and production systems, researchers and private companies have devoted themselves to the development of better alternatives to meat, other than highly marketed vegetarian meat alternatives [4]. There is another type of meat alternative on the horizon-cell-based meat, or so-called artificial meat, also known as cultured meat, cultivated meat, in vitro meat, or lab-grown meat [5,6], which can be grown from live animal stem cells rather than from farm animals [7]. Based on the hypothesis that artificial meat may be able to alleviate the aforementioned ethical, environmental, health, and hunger problems resulting from conventional meat production [8], artificial meat is currently a hot topic within the industrial, political, societal, and scientific areas in both the scientific field and the press media [9,10]. In addition to the hypothesis that artificial meat is presented by the press media to be able to address the aforementioned problems in Western countries [10], it could also mitigate the same major issues for China. As early as 2007, global food demand is expected to be 77% higher by 2050, China is driving this demand by accounting for 43% of the global agri-food increase [11]. Meat demand in China is anticipated to grow a lot from 2019 to 2050, given the sustainable growth of China's middle class, the meat demand would be higher than nowadays [12]. In particular, beef consumption in China from 2015 to 2050 is expected to increase by 119% [13]. However, meat production in China is increasing at a lower rate than the demand for meat [14]. Meat shortage may be a major sustainable development challenge that China must face in the future, which means they need to continue exploring and adopting effective alternatives to fill this vacancy, such as artificial meat produced from cell culture.

Up to the present time, there have been various surveys regarding consumer acceptance of artificial meat, with most research focusing on Western countries [15,16], such as the United States and European countries. These studies found that more than half of the respondents who participated in these surveys would be willing to try artificial meat but fewer participants would be willing to eat regularly or pay for this novel product [17]. In fact, due to the cross-cultural differences in consumer acceptance of artificial meat, these results cannot be extrapolated from Western countries to Chinese consumers [8]. Many consumer-facing companies now realize that they will need to penetrate the Chinese market to boost their development [18]. Scientists also speculated that this novel food product, advertised as clean, (i.e., wholesome, sustainable and less polluting, since potentially using fewer hormones, less resources, and being less harmful for the environment), could be the silver bullet for China [19]. However, limited research has explored consumer acceptance of artificial meat in China [20]. Nevertheless, Chinese consumers' opinions and the potential Chinese market have been already studied by the Cellular Agriculture Society [19]. A high potential acceptance of artificial meat has been observed but another study indicated that approximately 50% of Chinese consumers have no opinion [21]. Other authors who conducted a survey in three countries (the USA, India, and China) confessed the possibility that some Chinese participants did not fully understand the concept of artificial meat which might be confounded with plant-based meat [20]. Therefore, more reliable data and new investigations are needed to investigate artificial meat acceptance and potential demand of Chinese consumers for artificial meat with more assurance that the concept will be fully understood. In addition, strong evidence is still lacking about the perception by Chinese consumers how artificial meat may contribute to solving the above-mentioned problems related to food security, animal welfare, and environmental protection. In light of its distinctive catering culture, view of food, and traditional philosophy, Chinese attitudes toward artificial meat need multi-aspect and specific exploration.

Therefore, the current study seeks to work with a large group of Chinese consumers who are likely to better understand the concept (compared to previous studies) and with appropriate questions to deeper investigate their attitudes, perspective, potential acceptance, and willingness to engage with artificial meat, and to provide a Chinese reference to the widest artificial meat market worldwide. Some of the major potential predictors of willingness to try, eat regularly, and pay for artificial meat, as well as potential reasons for willingness or non-willingness to try and perception about the feasibility of this novel product have been also explored.

Materials and Methods

Data Collection

An online survey entitled "Survey on novel food-Artificial Meat" was conducted in China between 15 June and 30 August 2020. A total of 4929 survey responses were collected by different means including the dissemination in universities and commercial providers of sampling services. The process adhered entirely to the ESOMAR (European Society for Opinion and Market Research) guidelines on ethical online research [22]. This includes assurances that respondents gave informed explicit consent to take part in the survey and had their personal data protected. Indeed, after being informed of the objectives of the survey and how the given information will be used, all respondents gave their informed consent for inclusion of their answers before and after they participated in the study. Respondent details (sex, age, education level, activity area, monthly net income, meat consumption, and familiarity of respondents with artificial meat) have been collected in an anonymous way with an option "no wish to answer" and with no personally identifiable information. Otherwise, this work is part of an international survey (available in different languages) conducted following local guidelines based on the laws and regulations of the countries in which the research has been performed (in this case, based on practices of China agricultural University [21]) and this includes ethical approval by ethics committees when required (such as in Brazil: number CAAE: 37924620.5.0000.5404).

Responses when total survey duration was less than half of the median duration were excluded to avoid irrelevant answers to some extent. Indeed, these answers might be less accurate and therefore could cause biased results. The final sample consisted of 4666 participants, which means a confidence interval of 1.43% maximum based on the size of the Chinese population and depending on the different proportions of answers to the different questions (https://www.surveysystem.com/sscalc.htm (accessed on 6 February 2021)).

Questionnaire Design

The survey consists of four sections. Before starting to answer the questionnaire, the introduction of artificial meat in the form of text and graph was briefly provided to respondents (Figure 1). Unlike previous studies conducted in China, a picture describing the principles of artificial meat production has been provided in addition to a small text to avoid as much as possible any misunderstanding or any confusion with other meat substitutes as suspected in previous surveys [20,21]. Indeed, a pre-test of the survey showed that the respondents were indeed sometimes confused between plant-based meat and artificial meat produced from cultured cells. The survey was thus modified after discussion with these first respondents in order to consider the psychology of Chinese respondents. Indeed, this combination of quantitative and qualitative data as recommended in psychometrics [23] was crucial to minimize any confusion. As a result, the explanatory figure was added and invalid responses were removed. Artificial meat, also known as cultured meat, in vitro meat, cultivated meat, lab meat, clean meat and synthetic meat, is a novel food produced in laboratories using animal muscle stem cells, but does not come directly from a living animal and which proliferate in culture. The production of artificial meat is the subject of media enthusiasm to feed the growing human population. In order to address the increasing concerns about environment (global warming) and ethic (animal welfare) but also the weakness of the conventional meat production (limited farming resources and low efficiency to feed the ever increasing population), scientific research is devoted to introduce and develop on a large scale artificial meat as a novel food in the future.

Statistical Analysis

Data were analyzed by using R software (version.3.5.2) [24] and IBM SPSS 25 [25] using a variety of statistical techniques as previously done [26]. As the first step, the distribution of answers was summarized based on number of responses, percentage of responses, mean value, and standard deviation (SD) by R function "describe."

In order to visually present the responses of multiple-choice questions, the word After being informed, the respondents could answer the survey according to various criteria. Sociodemographic information was collected in the first section, including sex, age, education level, activity area, monthly net income, meat consumption, and familiarity of respondents with artificial meat. Then, one question was asked as preamble regarding respondents' food purchase criteria. The second section provided information about respondents' attitudes toward societal challenges faced by conventional meat and artificial meat, regarding ethical, environmental issues, the traditional meat industry, and rural life issues. Afterwards, the personal perceptions about artificial meat compared to conventional meat or plant-based meat were asked within the third section. After these sections, the respondents were asked about their willingness to engage with artificial meat with respect to willingness to try (WTT), how much they would be willing to pay (WTP) and under which context they would be willing to eat or not artificial meat regularly (WTE). Finally, future development strategies were asked concerning the respondents' opinions towards whether artificial meat is realistic, as well as the feasibility of the development of artificial meat by private and public research models. The survey is detailed in Supplementary Materials.

Artificial meat, also known as cultured meat, in vitro meat, cultivated meat, lab meat, clean meat and synthetic meat, is a novel food produced in laboratories using animal muscle stem cells, but does not come directly from a living animal and which proliferate in culture. The production of artificial meat is the subject of media enthusiasm to feed the growing human population. In order to address the increasing concerns about environment (global warming) and ethic (animal welfare) but also the weakness of the conventional meat production (limited farming resources and low efficiency to feed the ever increasing population), scientific research is devoted to introduce and develop on a large scale artificial meat as a novel food in the future.

Statistical Analysis

Data were analyzed by using R software (version.3.5.2) [24] and IBM SPSS 25 [25] using a variety of statistical techniques as previously done [26]. As the first step, the distribution of answers was summarized based on number of responses, percentage of responses, mean value, and standard deviation (SD) by R function "describe."

In order to visually present the responses of multiple-choice questions, the word cloud was used to provide the overview by distilling the size of text based on words that appear with frequency [27] by "wordcloud" package and "comparison.cloud" function. The word cloud graph was generated from three multiple-choice questions, which were: (1) What could be the reasons for you to have willingness to try artificial meat? (2) What could be the reasons for you to have no willingness to try artificial meat? (3) What would you expect from artificial meat?

As in previous studies [20], some, but not all of the assumptions of ANOVA were sometimes violated in this survey case, such as normality of distributions and homogeneity of variances. Therefore, we ran a Welch's ANOVA, which does not require the homogeneity of variance assumption, and we obtained extremely similar results compared to ANOVA which is considered as being robust [28]. Based on these observations, we proceeded with ANOVA since the Welch's ANOVA does not accept interactions. Variance analysis was performed in SPSS to determine whether the demographic variables affect respondents' willingness to try, to eat regularly, and finally to pay. The main effect of each of the demographic factors on the willingness to try, to eat, and to pay was first studied to select the significant factors by Welch's ANOVA, which were further used to study the interaction effects by ANOVA based on the robustness of the ANOVA under the application of both normally and less-normally distributed data [28]. Then the post-hoc test was performed by using a Tukey HSD test with the Bonferroni correction for the pairwise comparisons between significant groups. Differences were considered significant at a Bonferroni-corrected p < 0.05. The significant differences were confirmed by other statistical approaches such as the Student t-test adapted for heterogenous variances and also nonparametric tests. In addition, the responses regarding willingness to pay and "age × familiarity" was performed in mosaic plot by R package "vcd" and "mosaic".

With a large amount of qualitative data, it is relevant to use some exploratory techniques to explore the underlying relationship structures among multiple, diverse and categorical variables. Thus, multiple correspondence analysis (MCA) was chosen and applied in this study to detect and reveal the exploratory insights among multiple aspects of consumer behavior, intention, and willingness. Particularly, MCA can be used to represent and model datasets as clouds of points in a multidimensional space, showing if relationships exist between variables and offering statistical results that can be seen both analytically and visually [29]. The MCA was performed by R package "FactoMineR" and "MCA" functions.

In addition, cluster analysis defines and classifies homogeneous variables into groups based on the MCA dimensions assuming that they have substantive coherence [29]. The principle of clustering is by default based on the Euclidean distance, which is the square root of the sum of the square differences. In this study, data for willingness to try and willingness to eat regularly [Question1: would you be willing to try artificial meat?, divided into two groups: No Willing to try (score of 1 and 2, when participants answer 1-definitely no willing to try or 2-probably no willing to try, which were both combined as no willing to try in MCA analysis) and Willing to try (score of 4 and 5: 4-probably willing to try or 5-definitely willing to try); Question2: would you be willing to eat artificial meat regularly?, divided into two groups: Willing to eat regularly; No Willing to eat regularly], were then analyzed to select the categorical variables with similar characteristics, regarding societal challenges and respondents perception of artificial meat. According to the results of MCA, the first two dimensions (for both WTT and WTE) were chosen to calculate the Euclidean distance from each variable to the two categorical groups, and the variable with the smallest distance to each center group was allocated to the corresponding cluster.

Results

Distribution of Answers

Demographic Information of the Respondents

According to the sociodemographic information detailed in Table 1, the current sample of respondents was characterized by quite equally females (50.6%) and males (46.2%) who were mainly young and middle-aged people (77.6%), no scientist and outside the meat sector (74.3%), medium education level [(college, 55.6%; master, 20.4%)], medium income (3000-8000 yuan, i.e., around 460-1240 USD or 380-1020 Euros, 38.1%) and meat-eaten people (regularly and daily meat eater, 73%). Among them, 55.9% of respondents have heard about artificial meat before.

Monthly net income

No wish to answer = 0 1026 21.9 As shown in Table 2A, nearly half of the respondents agreed with the potential ethical and environmental problems that may be caused by conventional meat production (47.4 and 50.0% respectively for the sum of scores 4 and 5), and 51% of respondents believed reducing meat consumption could be a good solution to solve the potential ethical and environmental problems caused by conventional meat production. Around half of the respondents thought that artificial meat is more ethical and eco-friendly than conventional meat. However, more than 50% of the respondents admitted that artificial meat would have negative impacts on the traditional meat industry, territories, and rural life. Nonetheless, for all these questions, many respondents were unsure (i.e., from 23.3 to 33.6% of score 3, Table 2A).

Perception

More than half of the respondents (sum of scores 1 and 2) believed that artificial meat will be less healthy, less safe, less tasty, and with a lower nutritional value than conventional meat (Table 2B). A total of 49% of respondents thought this novel food is fun and/or intriguing, 36% thought artificial meat is promising and/or acceptable, and 15% of the respondents indicated that artificial meat is absurd and/or disgusting (Figure 2). Relative to 16.1% of the respondents who had high emotional resistance to try artificial meat (sum of scores 4 and 5), 52.3% had less emotional resistance to try (scores 1 and 2) whereas 31.5% of them were unsure (Table 2B).

Foods 2021, 10, x FOR PEER REVIEW 7 of 27 3.1.3. Perception More than half of the respondents (sum of scores 1 and 2) believed that artificial meat will be less healthy, less safe, less tasty, and with a lower nutritional value than conventional meat (Table 2B). A total of 49% of respondents thought this novel food is fun and/or intriguing, 36% thought artificial meat is promising and/or acceptable, and 15% of the respondents indicated that artificial meat is absurd and/or disgusting (Figure 2). Relative to 16.1% of the respondents who had high emotional resistance to try artificial meat (sum of scores 4 and 5), 52.3% had less emotional resistance to try (scores 1 and 2) whereas 31.5% of them were unsure (Table 2B).

Willingness to Engage

A total of 49.7% of respondents would be willing to try artificial meat (29.8% probably yes, 19.9% definitely yes), 20.9% respondents expressed their unwillingness to try artificial meat (11.3% probably no, 9.6% definitely no), and 29.3% were unsure about their willingness to try it (Table 3A).

Willingness to Engage

A total of 49.7% of respondents would be willing to try artificial meat (29.8% probably yes, 19.9% definitely yes), 20.9% respondents expressed their unwillingness to try artificial meat (11.3% probably no, 9.6% definitely no), and 29.3% were unsure about their willingness to try it (Table 3A).

A great part of the respondents (70.1%) already eat meat substitutes. Around 52.9% of the respondents (irrespectively of the fact they already eat meat substitutes or not) will accept artificial meat as a good alternative to conventional meat (Table 3A). However, it is noteworthy that 34.1% of the respondents who already eat meat substitutes (in the form of plant-products) will refuse to accept artificial meat as an alternative to conventional meat. Finally, 12.9% do not accept artificial meat nor plant-based meat substitutes (Table 3A).

Of the total respondents, 47.2% do not want to eat artificial meat regularly at all. By contrast, 39.7%, 35.5%, and 35.6% of them are willing to eat artificial meat regularly at restaurants, home, and/or in ready-to-eat meals respectively. About 46% of respondents are willing to pay for artificial meat at a much cheaper price than conventional meat (even nothing at all), 40% are willing to pay at a lower price than conventional meat, 10% are willing to pay at the same price as conventional meat. Only 3% of respondents are willing to pay more (data shown in Table 3A).

Reasons for Willingness or No Willingness to Try and for Expectations about Artificial Meat

Globally, 74.2% of respondents agreed to try at least once artificial meat but 25.8% answered they do not want to (data not shown). Food crisis (data shown in the note of Figure 3), avoiding zoonosis, and curiosity are presented as the first three important reasons associated with willingness to try, but ethical and eco-friendly issues are not the main reasons compared to the concerns of food shortage and security. There were a number of people (22.9%) who had indicated that they had no willingness to try artificial meat.

1.

Reasons for willingness to try: food crisis: 31.3%; Less risk of zoonosis (disease that can be transmitted from animals to people): Unnaturalness and unsafety were frequently chosen as the main reasons for no willingness to try whereas environmental footprints and negative impact on territories were the last two reasons for their no willingness to try (Figure 3).

In terms of expectations about artificial meat, safety, taste, and adequate nutrition were the most often selected (47.5; 42.9, and 42.4% respectively) compared to other options such as reducing or no farming (19.9 and 15.6% respectively). Many respondents expected also artificial meat could solve the problems of carbon footprints and animal suffering (42.9 and 34.4% respectively). Nearly 9% of respondents answered having no expectations from artificial meat (Figure 3). Reasons for willingness to try: food crisis: 31.3%; Less risk of zoonosis (disease that can be transmitted from animals to people): Unnaturalness and unsafety were frequently chosen as the main reasons for no willingness to try whereas environmental footprints and negative impact on territories were the last two reasons for their no willingness to try (Figure 3).

In terms of expectations about artificial meat, safety, taste, and adequate nutrition were the most often selected (47.5; 42.9, and 42.4% respectively) compared to other options such as reducing or no farming (19.9 and 15.6% respectively). Many respondents expected also artificial meat could solve the problems of carbon footprints and animal suffering (42.9 and 34.4% respectively). Nearly 9% of respondents answered having no expectations from artificial meat (Figure 3).

Future Development Strategies

Respondents were asked about their opinion on development of artificial meat. For 45.1% of respondents, the development of artificial meat will become realistic in the medium term (i.e., from 6 to 15 years) but for 10.3% of respondents, artificial meat will never be realistic (Table 3B).

About 46% of respondents answered that the public research should invest much more or more time and funding to develop artificial meat, 14.5% of respondents thought that public research should invest much less or less in this biotechnology development. In contrast, 51.8% of respondents considered that private research models are much less or less relevant and only 14.6% thought start-ups are relevant for artificial meat research development (Table 3B). In both cases, between 33.5% and 39.5% are unsure about these aspects.

Effects of Demographic Factors on Willingness to Try, to Eat Regularly or to Pay for Artificial Meat

In order to perform further analysis, representativeness of the current sample regarding sex and age was analyzed. There were 50.6% females and 46.2% males in this study,

Future Development Strategies

Respondents were asked about their opinion on development of artificial meat. For 45.1% of respondents, the development of artificial meat will become realistic in the medium term (i.e., from 6 to 15 years) but for 10.3% of respondents, artificial meat will never be realistic (Table 3B).

About 46% of respondents answered that the public research should invest much more or more time and funding to develop artificial meat, 14.5% of respondents thought that public research should invest much less or less in this biotechnology development. In contrast, 51.8% of respondents considered that private research models are much less or less relevant and only 14.6% thought start-ups are relevant for artificial meat research development (Table 3B). In both cases, between 33.5% and 39.5% are unsure about these aspects.

Effects of Demographic Factors on Willingness to Try, to Eat Regularly or to Pay for Artificial Meat

In order to perform further analysis, representativeness of the current sample regarding sex and age was analyzed. There were 50.6% females and 46.2% males in this study, the gender ratio for China population is 48.69% females and 51.09% males. Whereas in the current study, respondents were on average young (with 46.1%, 31.5%, and 22.4% of respondents between 18 and 30 years of age, 31 and 50 years, and more than 51 years, respectively), the corresponding age structure of China population is 23.4%, 38%, and 38.6% for the same age groups [30]. Despite the fact our sample of Chinese consumers seems to be non-representative particularly based on age, the current demographics were studied as important potential factors to explain willingness to try (Tables 4 and5) or to regularly eat cultured meat and to pay for artificial meat (Table 6). The mean difference is significant at the 0.05 level. Only significant interactions are indicated.

Relationships between Demographic Factors and Willingness to Try

Willingness to try artificial meat is regulated by all the studied demographic factors (sex, age, education level, activity area, income, level of meat consumption, and familiarity with artificial meat), which act together in interactions (Table 4).

As it is shown in Table 5A, for the current participants, men over 31 or 51 years of age were more willing to try artificial meat than women of the same age. Women are characterized by an increasing willingness to try with increasing age, whereas for men, willingness to try increases between 18-30 and 31-50 years of age only.

People who had a master's degree (especially men) have more willingness to try artificial meat than other participants with different levels of education (Table 5B).

Participants who perform an activity within the meat sector have the highest willingness to try artificial meat, especially female meat workers. The group with the lowest willingness to try is females who are not scientists and who work outside the meat sector. On average, scientists have greater willingness to try than other female participants outside the meat sector (Table 5C).

Males with medium income have the highest willingness to try and females with the lowest income have the lowest willingness to try (Table 5D).

Males who are high meat-eaters would be more willing to try artificial meat followed by females of the same level of meat consumption. On the opposite, males, and to a lower extent of females, who never eat meat, have the lowest willingness to try (Table 5E).

Whereas willingness to try generally increases with increasing age, old participants with the highest education level have more willingness to try than younger and low educated participants. Moreover, young but high educated respondents are less willing to try artificial meat to a large extent (Table 5F).

The group of old people has not always the highest willingness to try artificial meat since old participants who never eat meat have the lowest willingness to try even lower than young and mid-aged vegans. By contrast, old meat daily eaters would be the best volunteers to try artificial meat compared to younger participants or less regularly meateaters (Table 5G).

Relationships between Effective Demographics and Willingness to Eat Regularly and Willingness to Pay

On average, willingness to eat regularly and willingness to pay do not depend too much on demographic factors compared to willingness to try (only significant interactions are shown in Table 6). In line with previous literature underlying the importance of familiarity for consumers' acceptance of this novel product, the "age × familiarity" interaction for willingness to pay is significant (Table 6). The relationships among willingness to pay, familiarity, and age are therefore investigated, and results presented in a mosaic plot (Figure 4). Positive and great values of Pearson standardized residuals are observed in cells colored in blue. Negative and great values of Pearson standardized residuals are observed in cells colored in red. Standardized residuals are standardized differences between observed and theoretical effectives. There are associations between respondents' age, familiarity, and their WTP for artificial meat that can be observed. These results were confirmed by significant differences between mean values of WTP following variance analysis: 18 c ) where 1 corresponds to the answer "WTP less (or much less)", 2 to the answer "WTP the same as conventional meat", 3 to the answer "WTP more (or much more)" than for conventional meat.

The Driven Factors of Willingness to Engage with Artificial Meat

It can be seen from the MCA plot of societal challenges that respondents can be distinguished according to their score of willingness to try and to eat artificial meat regularly (Figure 5a,b). As shown in Figure 4, young respondents who already heard about artificial meat would be willing to pay less than for conventional meat. Furthermore, people who are older than 31 years of age and never heard about artificial meat before are likely to pay less for artificial meat or the same price compared to conventional meat. A very small proportion of middle-aged people who are unfamiliar with artificial meat are inclined to pay more than for conventional meat.

Positive and great values of Pearson standardized residuals are observed in cells colored in blue. Negative and great values of Pearson standardized residuals are observed in cells colored in red. Standardized residuals are standardized differences between observed and theoretical effectives. There are associations between respondents' age, familiarity, and their WTP for artificial meat that can be observed. These results were confirmed by significant differences between mean values of WTP following variance analysis: 18 c ) where 1 corresponds to the answer "WTP less (or much less)", 2 to the answer "WTP the same as conventional meat", 3 to the answer "WTP more (or much more)" than for conventional meat.

The Driven Factors of Willingness to Engage with Artificial Meat

It can be seen from the MCA plot of societal challenges that respondents can be distinguished according to their score of willingness to try and to eat artificial meat regularly (Figure 5a,b).

for conventional meat.

The Driven Factors of Willingness to Engage with Artificial Meat

It can be seen from the MCA plot of societal challenges that respondents can be distinguished according to their score of willingness to try and to eat artificial meat regularly (Figure 5a,b). Respondents with willingness to try (Figure 5a) and to eat regularly (Figure 5b) answered to a larger extent that "conventional meat cause ethical and environmental problems", "artificial meat is ethical" and "reduce meat consumption could be a good solution". They also answered that "artificial meat has a negative impact on the traditional meat industry, territories, and rural life." On the other hand, disagreements with the same statements (i.e., "conventional meat cause ethical and environmental problems", "artificial meat is ethical", and "reduce meat consumption could be a good solution") are associated with no willingness to try and to eat regularly.

Respondents who believe artificial meat can be very safe, healthy, nutritional and tasty compared to conventional meat would be willing to try (Figure 6a) and to eat this novel food regularly (Figure 6b). On the contrary, respondents have definitely no willingness to eat regularly when they believe artificial meat products will be much less safe, healthy, nutritional and tasty compared to conventional meat. However, when participants are concerned by safety, healthiness, as well as by nutritional and sensory traits of artificial meat, they still would be willing to try. Responses such as "no emotional re- Respondents with willingness to try (Figure 5a) and to eat regularly (Figure 5b) answered to a larger extent that "conventional meat cause ethical and environmental problems", "artificial meat is ethical" and "reduce meat consumption could be a good solution". They also answered that "artificial meat has a negative impact on the traditional meat industry, territories, and rural life." On the other hand, disagreements with the same statements (i.e., "conventional meat cause ethical and environmental problems", "artificial meat is ethical", and "reduce meat consumption could be a good solution") are associated with no willingness to try and to eat regularly.

Respondents who believe artificial meat can be very safe, healthy, nutritional and tasty compared to conventional meat would be willing to try (Figure 6a) and to eat this novel food regularly (Figure 6b). On the contrary, respondents have definitely no willingness to eat regularly when they believe artificial meat products will be much less safe, healthy, nutritional and tasty compared to conventional meat. However, when participants are concerned by safety, healthiness, as well as by nutritional and sensory traits of artificial meat, they still would be willing to try. Responses such as "no emotional resistance to artificial meat" are related to willingness to try and to eat regularly. It is obvious that respondents who have extremely emotional resistance to artificial meat tend to perceive artificial meat as "absurd and/or disgusting", which would lead to no willingness to try and to eat regularly (Figure 6a,b). The response of "artificial meat is "promising and/or acceptable" is related to the willingness to try and to eat regularly clusters, whereas "artificial meat is fun and/or intriguing" is located both at "willing to eat regularly" and "no willing to eat regularly" clusters (Figure 6b). Respondents who already eat meat substitutes and will accept artificial meat as alternatives would be highly willing to try and eat regularly. However, respondents who do not eat meat substitutes but somehow accept artificial meat as alternatives would be willing to try and to eat regularly. People who do not accept artificial meat as alternatives would be unwilling to eat artificial meat regularly (Figure 6b).

It is presented in Figure 6a that "ever heard" and "never heard" about artificial meat It is obvious that respondents who have extremely emotional resistance to artificial meat tend to perceive artificial meat as "absurd and/or disgusting", which would lead to no willingness to try and to eat regularly (Figure 6a,b). The response of "artificial meat is "promising and/or acceptable" is related to the willingness to try and to eat regularly clusters, whereas "artificial meat is fun and/or intriguing" is located both at "willing to eat regularly" and "no willing to eat regularly" clusters (Figure 6b). Respondents who already eat meat substitutes and will accept artificial meat as alternatives would be highly willing to try and eat regularly. However, respondents who do not eat meat substitutes but somehow accept artificial meat as alternatives would be willing to try and to eat regularly. People who do not accept artificial meat as alternatives would be unwilling to eat artificial meat regularly (Figure 6b).

It is presented in Figure 6a that "ever heard" and "never heard" about artificial meat are both associated with willingness to try. By contrast, "ever heard" about artificial meat is related to both willingness or no willingness to eat regularly, "never heard" about this novel food is related only with no willingness to eat regularly (Figure 6b).

In addition, in order to further confirm the relationships between respondents' perspective and willingness to engage with artificial meat, the three variables of interest (willingness to try, to eat regularly, and to pay) are all presented in Figure 6b. Willingness to eat regularly is close to "artificial meat is promising and/or acceptable" and "I accept artificial meat as an alternative and I eat meat substitutes." Willingness to try is close to "I do not have emotional resistance to artificial meat." Willingness to pay the same as conventional meat or less" is close to "artificial meat is fun and/or intriguing." No willingness to try and to eat regularly are both close to "artificial meat is absurd and/or disgusting." No willingness to try is closer to "I have emotional resistance to artificial meat". The answer "I do not accept artificial meat as an alternative and I do not eat meat substitutes" is closely related to "artificial meat is absurd and/or disgusting." Respondents were asked to indicate their most important criteria during food purchase from ten options: safety, sensorial quality, and price are ranked as the top three food purchase criteria. Environmental impact and ethics may not be the priority considerations for most of the current respondents. The cross-analysis between food purchase criteria and willingness to try artificial meat is presented in Figure 7. People who want the least to try artificial meat are those with the highest concern for nutritional value, safety, and traceability. By contrast, people who would be relatively more willing to try are respondents who pay the highest attention to labels, energy intake, and ethics. In addition, in order to further confirm the relationships between respondents' perspective and willingness to engage with artificial meat, the three variables of interest (willingness to try, to eat regularly, and to pay) are all presented in Figure 6b. Willingness to eat regularly is close to "artificial meat is promising and/or acceptable" and "I accept artificial meat as an alternative and I eat meat substitutes." Willingness to try is close to "I do not have emotional resistance to artificial meat." Willingness to pay the same as conventional meat or less" is close to "artificial meat is fun and/or intriguing." No willingness to try and to eat regularly are both close to "artificial meat is absurd and/or disgusting." No willingness to try is closer to "I have emotional resistance to artificial meat". The answer "I do not accept artificial meat as an alternative and I do not eat meat substitutes" is closely related to "artificial meat is absurd and/or disgusting."

Relationships among Respondents' Attitudes, Perspective, Acceptance and Willingness to Try and to Eat Regularly

Respondents were asked to indicate their most important criteria during food purchase from ten options: safety, sensorial quality, and price are ranked as the top three food purchase criteria. Environmental impact and ethics may not be the priority considerations for most of the current respondents. The cross-analysis between food purchase criteria and willingness to try artificial meat is presented in Figure 7. People who want the least to try artificial meat are those with the highest concern for nutritional value, safety, and traceability. By contrast, people who would be relatively more willing to try are respondents who pay the highest attention to labels, energy intake, and ethics. Figure 8 indicates a positive association between willingness to try and willingness to eat regularly artificial meat. However, this cross-analysis also indicates that some respondents with no willingness to eat artificial meat regularly may agree or probably agree to try once. However, 11% of respondents who would be willing to eat artificial meat regularly at a restaurant, home, or in ready-to eat meals, have no willingness to try artificial meat before regular consumption, which may suggest they already trust the food-tech industry. Figure 8 indicates a positive association between willingness to try and willingness to eat regularly artificial meat. However, this cross-analysis also indicates that some respondents with no willingness to eat artificial meat regularly may agree or probably agree to try once. However, 11% of respondents who would be willing to eat artificial meat regularly at a restaurant, home, or in ready-to eat meals, have no willingness to try artificial meat before regular consumption, which may suggest they already trust the food-tech industry. As shown in Figure 9, respondents who will accept artificial meat as an alternative to conventional meat generally have a high willingness to try (63%) and a high willingness to eat regularly (62%), which is logical. Among them, a great part of them already eat meat substitutes (52% and 48% for willingness to try or eat regularly respectively). In other words, for respondents who would be willing to try artificial meat and would accept it as a viable alternative to conventional meat compared to meat substitutes, 52% of them already eat meat substitutes and 11% have not eaten meat substitutes but would accept artificial meat as an alternative. For people who had no willingness to try artificial meat, 70% of them would not accept artificial meat as an alternative. Nearly 62% of respondents who would be willing to eat artificial meat regularly would accept it as a viable alternative to conventional meat, including 14% of them who do not eat meat substitutes.

Around 38% of respondents who would be willing to eat artificial meat regularly would not accept it as an alternative to conventional meat. On the opposite, 41% of respondents, who had no willingness to eat artificial meat, would accept artificial meat as a viable alternative to conventional meat.

The observations between respondents' perspective of artificial meat and their willingness to eat regularly is presented in Figure 10. Nearly one-quarter of people who have no willingness to eat artificial meat regularly find artificial meat is "absurd and/or disgusting" whereas, surprisingly, about one quarter find it "promising and/or accepting." For half of the respondents who have no willingness to eat regularly, artificial meat is "fun As shown in Figure 9, respondents who will accept artificial meat as an alternative to conventional meat generally have a high willingness to try (63%) and a high willingness to eat regularly (62%), which is logical. Among them, a great part of them already eat meat substitutes (52% and 48% for willingness to try or eat regularly respectively). In other words, for respondents who would be willing to try artificial meat and would accept it as a viable alternative to conventional meat compared to meat substitutes, 52% of them already eat meat substitutes and 11% have not eaten meat substitutes but would accept artificial meat as an alternative. As shown in Figure 9, respondents who will accept artificial meat as an alternative to conventional meat generally have a high willingness to try (63%) and a high willingness to eat regularly (62%), which is logical. Among them, a great part of them already eat meat substitutes (52% and 48% for willingness to try or eat regularly respectively). In other words, for respondents who would be willing to try artificial meat and would accept it as a viable alternative to conventional meat compared to meat substitutes, 52% of them already eat meat substitutes and 11% have not eaten meat substitutes but would accept artificial meat as an alternative. For people who had no willingness to try artificial meat, 70% of them would not accept artificial meat as an alternative. Nearly 62% of respondents who would be willing to eat artificial meat regularly would accept it as a viable alternative to conventional meat, including 14% of them who do not eat meat substitutes.

Around 38% of respondents who would be willing to eat artificial meat regularly would not accept it as an alternative to conventional meat. On the opposite, 41% of respondents, who had no willingness to eat artificial meat, would accept artificial meat as a viable alternative to conventional meat.

The observations between respondents' perspective of artificial meat and their willingness to eat regularly is presented in Figure 10. Nearly one-quarter of people who have no willingness to eat artificial meat regularly find artificial meat is "absurd and/or disgusting" whereas, surprisingly, about one quarter find it "promising and/or accepting." For half of the respondents who have no willingness to eat regularly, artificial meat is "fun For people who had no willingness to try artificial meat, 70% of them would not accept artificial meat as an alternative. Nearly 62% of respondents who would be willing to eat artificial meat regularly would accept it as a viable alternative to conventional meat, including 14% of them who do not eat meat substitutes.

Around 38% of respondents who would be willing to eat artificial meat regularly would not accept it as an alternative to conventional meat. On the opposite, 41% of respondents, who had no willingness to eat artificial meat, would accept artificial meat as a viable alternative to conventional meat.

The observations between respondents' perspective of artificial meat and their willingness to eat regularly is presented in Figure 10. Nearly one-quarter of people who have no willingness to eat artificial meat regularly find artificial meat is "absurd and/or disgusting" whereas, surprisingly, about one quarter find it "promising and/or accepting." For half of the respondents who have no willingness to eat regularly, artificial meat is "fun and/or intriguing." By contrast, for people who would be willing to eat regularly at a restaurant, home or in ready-to-eat meals, a large majority of them thinks this novel product is "promising and/or accepting" or "fun and/or intriguing." However, there is still a small portion of people who find artificial meat is absurd and/or disgusting but still would be willing to eat it regularly.

Foods 2021, 10, x FOR PEER REVIEW 17 of 27 and/or intriguing." By contrast, for people who would be willing to eat regularly at a restaurant, home or in ready-to-eat meals, a large majority of them thinks this novel product is "promising and/or accepting" or "fun and/or intriguing." However, there is still a small portion of people who find artificial meat is absurd and/or disgusting but still would be willing to eat it regularly. Bryant et al. [20] observed that Chinese consumers' attitudes toward artificial meat were more positive than consumers in the USA. However, Siegrist and Christina [31] found that American consumers did not differ from Chinese consumers in accepting artificial meat. In addition, Zhang et al. [21] found that nearly 22% of Chinese participants were opposed to artificial meat, and approximately 50% remained neutral. Consequently, only 28% expressed their positive attitude in favor of artificial meat, which reflects that the Chinese consumers' attitudes toward artificial meat are quite conservative. It was also demonstrated that Chinese consumers are among the least likely to eat artificial meat (26%) compared to some other Asian countries (Vietnam, Thailand, Indonesia) [32]. In brief, the current literature about potential acceptance of artificial meat in China did not reach so far consensus on this subject. In the current survey with 4666 Chinese consumers, we found that only a fifth of respondents were definitely willing to try, nearly half of them were not willing to eat regularly, and almost 90% were willing to pay for artificial meat at a lower price than conventional meat. Obviously, these findings cannot be interpreted as Chinese consumers are eager for artificial meat.

Anderson and Tyler [33] found that Chinese respondents were more likely to provide responses in the middle of the scales than respondents in the United States. This is consistent with our study indicating that 30% of respondents stated that they are "unsure" or "neutral" and about 40% of respondents may or may not agree, perhaps with a yes or no, to nearly every question. On one hand, people are likely to be truly unaware of the related issues such as the societal concerns facing traditional meat that are likely to be resolved by artificial meat. On the other hand, this may be due to the influence of "golden mean", which has always been the basic concept of Chinese philosophy. People in different countries answer survey questions with different habits. However, a too extreme response does not always reflect their sense of worth. Especially for the Chinese consumers, who prefer any implicit expression. In other words, the uncertain answers such as "unsure" or "probably" are more likely to be related to the rejection.

In light of the large numbers of consumers and relatively permissive regulatory frameworks, it has been hypothesized that Asia is currently the first consumer market for artificial meat [34]. However, since the Chinese market of artificial meat has not been Bryant et al. [20] observed that Chinese consumers' attitudes toward artificial meat were more positive than consumers in the USA. However, Siegrist and Christina [31] found that American consumers did not differ from Chinese consumers in accepting artificial meat. In addition, Zhang et al. [21] found that nearly 22% of Chinese participants were opposed to artificial meat, and approximately 50% remained neutral. Consequently, only 28% expressed their positive attitude in favor of artificial meat, which reflects that the Chinese consumers' attitudes toward artificial meat are quite conservative. It was also demonstrated that Chinese consumers are among the least likely to eat artificial meat (26%) compared to some other Asian countries (Vietnam, Thailand, Indonesia) [32]. In brief, the current literature about potential acceptance of artificial meat in China did not reach so far consensus on this subject. In the current survey with 4666 Chinese consumers, we found that only a fifth of respondents were definitely willing to try, nearly half of them were not willing to eat regularly, and almost 90% were willing to pay for artificial meat at a lower price than conventional meat. Obviously, these findings cannot be interpreted as Chinese consumers are eager for artificial meat.

Anderson and Tyler [33] found that Chinese respondents were more likely to provide responses in the middle of the scales than respondents in the United States. This is consistent with our study indicating that 30% of respondents stated that they are "unsure" or "neutral" and about 40% of respondents may or may not agree, perhaps with a yes or no, to nearly every question. On one hand, people are likely to be truly unaware of the related issues such as the societal concerns facing traditional meat that are likely to be resolved by artificial meat. On the other hand, this may be due to the influence of "golden mean", which has always been the basic concept of Chinese philosophy. People in different countries answer survey questions with different habits. However, a too extreme response does not always reflect their sense of worth. Especially for the Chinese consumers, who prefer any implicit expression. In other words, the uncertain answers such as "unsure" or "probably" are more likely to be related to the rejection.

In light of the large numbers of consumers and relatively permissive regulatory frameworks, it has been hypothesized that Asia is currently the first consumer market for artificial meat [34]. However, since the Chinese market of artificial meat has not been deeply explored despite its growing interest in Western countries, Chinese philosophy (e.g., the way of thinking as well as expression) should be taken into account.

What Kind of Consumer

Would Be Willing to Engage with Artificial Meat in China? 4.2.1. Artificial Meat Is More Attractive to the Elderly and to Men in China Some previous studies have observed, in different countries, that artificial meat is more attractive for young people and men than for older people and women [16,35]. This is understandable because new innovative things are more likely to be accessible and acceptable by young people compared to old ones. However, in contrast, the current study found that middle-aged adults and old people (over 31 years of age) were more willing to try artificial meat than young people. In line with previous conclusions, Chinese men were indeed more willing to try artificial meat than Chinese women, and young Chinese had a more conservative attitude towards artificial meat. Regardless of the fact that the current samples of respondents were not sufficiently representative of the Chinese population based on age, the comparisons between age groups were still relevant in this study. These differences between age groups may be due to different consumption concepts based on different economic strengths. With the accumulation of economic and social resources by middle-aged and elderly people, the latter became more curious and open-minded to accept novel things, to compensate for the lack of impulse purchases in their youth due to a very low economic activity in China several decades ago. By contrast, Chinese young people, who are more beginners in being economically independent, being still students, with economic and parental pressure, may tend to be more conservative face to new things, especially if the price of the new product is too high. Nevertheless, the artificial meat market could be developed not only for middle-aged adults but also for young people due to the increasing tendency of modern eating habits based on convenient snacking. This modern habit can be easily explained by the busy work schedules of young and especially middle-aged people in China [36].

Artificial Meat Is More Attractive for Daily and Regularly Meat Eaters

Meat eaters are found more willing to engage with artificial meat than vegetarians [37]. Our study confirmed that daily meat eaters had a higher willingness to try artificial meat than regularly and rarely meat-eaters. Despite the consideration that artificial meat can be regarded as a vegetarian product, which could be attractive to some vegetarians and vegans [38], it was well confirmed by several studies that artificial meat is more appealing to meat-eaters than vegetarians [39,40]. Our study confirms that vegans and/or vegetarians were more unwilling to try artificial meat expectedly. For the current respondents, there are only 3.4% vegetarians, which indicates that the majority of meat-eaters are likely to be considered as the potential target for the acceptance of artificial meat. In addition, during the COVID-19 pandemic, due to the increased weaknesses of the global food supply chain, some people are now expecting the transition to artificial meat to happen even faster [41] and this may have influenced answers to this survey towards a great acceptance of artificial meat.

Are Education and Activity Area Relevant for Willingness to Try Artificial Meat?

It was demonstrated that highly educated people are more inclined to hold analytic, deliberative standpoint rather than based on heuristics and biases which may be more likely to lead to higher acceptance of artificial meat [8]. Nevertheless, Hocquette et al. [42] found that educated people were not convinced by the fact that artificial meat is the solution to the problems of the conventional meat industry. For the current participants, willingness to try is poorly associated with educational level. To sum up, based on the current sample, people with master's degree might be more willing to try, this might be due to their generally scientific background and their level of knowledge may stimulate their acceptance of new tech and concept. Education background might contribute to willingness to engage with artificial meat since people with doctorate degree are more likely to be rigorous to strictly refuse or accept artificial meat. People who were less educated may also be very open or stubborn toward this novel product. However, it is still relevant to emphasize the slightly non-representativeness of the current data for the entire Chinese population although comparisons between social groups are still valid. Indeed, as stated by Heidemann et al. [43], respondents from different regions could provide new multiple perceptions and enrich the existing knowledge even if they are non-representative of the whole country. Furthermore, we observed that highly educated respondents were poorly aware of this novel product [40]. Further research is therefore needed based on the education perspective.

It was found that cultured meat acceptance is significantly higher amongst agricultural and meat workers, indicating that those who are closest to existing meat production methods are most likely to prefer alternatives [26]. The present study also observed that participants who work in an area related to the meat industry had a higher willingness to try artificial meat, this may be explained by the fact that people working related to meat production are more curious about the novel product itself and willing to have a first try at least. However, it does not mean they would be willing to eat regularly or to pay at an expensive price. Since no significant effects were observed in variance analysis between activity area and willingness to eat and pay, the conclusion of Bryant et al. [26] was not confirmed in our study. 4.2.4. Unfamiliarity with Artificial Meat May Lead to a First Try but Not Likely to Eat Regularly and to Pay More People familiar with the conception of artificial meat are more likely to have higher acceptance according to previous studies [8]. Indeed, Rolland et al. [44] concluded that having positive information improves acceptance and willingness to taste artificial meat. For the current Chinese respondents, with quite neutral information provided, over half were familiar with artificial meat. In contrast to another survey conducted in China in which the term "cultured meat" was used, most people declared that they had never heard of cultured meat or in vitro meat [21]. This discrepancy may be explained by familiarity with the different terms or names used instead of familiarity with the novel product by itself [8]. Consequently, previous results might be biased due to some confounding effects of different naming. For instance, the Chinese translation of artificial meat is "man-made meat", which might be confounded with plant-based meat; cultured meat is more likely to be known or perceived as "farmed/nurtured meat" and cultivated meat can be translated into "cultivated and reproduced meat", which are more unfamiliar terms to Chinese people compared with artificial meat.

We observed that people who have never heard about artificial meat would be willing to try and pay less or pay the same price as conventional meat likely due to the curiosity for this novel technology. However, for those who never heard about artificial meat, they had no willingness to eat regularly. The finding that people not yet aware of this novel product would be willing to try and even pay for it but unwilling to eat regularly indicates that regular engagement is unacceptable for them at the present stage.

On the other hand, when consumers fully understand the concept of the new product, some of them refuse or are unsure to try at the very early beginning maybe because of some reasons such as food neophobia and emotional resistance. However, others would be willing to pay for artificial meat but with a lower price (as for young participants) probably because, from their perspective, artificial meat will be never better than real meat. This might reflect a special pattern of Chinese consumers who have a lack of knowledge about this novel product. Whereas it has been suggested that the limited knowledge curbs the development of artificial meat [45], by contrast, the core value of food products lies not only in physical aspects but also in long-term added value [46]. For instance, willingness to pay about animal welfare in relation to a social consensus has clearly a moral value [47]. When informing consumers about environmental or animal welfare benefits, their willingness to consume increases [16,48]. Herein, propagating the knowledge about positive impacts on animals provided by artificial meat can be regarded as an effective way to improve consumer acceptance in China.

Potential Changes of Drivers of Chinese Consumers' Willingness to Try or to Eat Artificial Meat

Several factors such as unnaturalness, food neophobia, food disgust, price, and taste were identified as major factors influencing consumer acceptance of artificial meat in Western countries [48][49][50]. A substantial cultural difference between China and other countries may cause different inducement of accepting or rejecting artificial meat [20]. Regarding global hot issues of social concern such as environment protection, animal welfare, and world hunger which are anticipated to be alleviated by artificial meat [8], the views of most Chinese respondents might be consistent to some extent. The questions related to these issues were therefore asked to address societal challenges, though the comparison of artificial meat and conventional meat.

Ethical and Environmental Issues Can Be Drivers of Artificial Meat

The societal challenges considered in this study were expressed in terms of ethical and environmental problems associated with meat production, in favor of meat consumption reduction (even veganism) or in relation to traditional issues. It was found that, for participants who believed that conventional meat causes ethical and environmental problems, artificial meat appears more ethical and eco-friendly compared to conventional meat. For these respondents, reducing meat consumption is a good solution to resolve the above issues, and they would be willing to become vegetarian and/or to try artificial meat with a high probability. This indicates that people with a more favorable perception of artificial meat might be more knowledgeable on this subject related to ethics and environmental issues with a more unfavorable perception of the conventional production system.

Despite the fact that these respondents agree with the idea that artificial meat will have negatively impacts on traditional meat industry, territory and rural life, they still expressed their willingness to try. Consequently, in line with our previous discussion, it might be hypothesized that an effective way to improve consumer acceptance of artificial meat might be to focus on the problems of conventional meat production, to emphasize the potential benefits of artificial meat, and explain how artificial meat can be substituted to conventional meat production. It is important to note that when respondents were asked about their expectations on artificial meat, nearly 30% expected reducing farming and 20% even hope for "no farming." It seems that despite the long history of agriculture, some Chinese people do not place considerable importance on the traditional agricultural industry.

Most Consumers Are Unsure and Have Less Strong Opinions about Artificial Meat

It was previously reported that Chinese consumers only focus on their personal reasons to eat or not artificial meat [51]. The current participants indeed expected positive internal characteristics (e.g., sensorial quality) of artificial meat. However, willingness to try or not to try might not be associated with any negative or positive characteristics of artificial meat products, since the responses about the characteristics "artificial meat is less tasty" and "artificial meat is less safe, healthy and nutritional" are all related to willingness to try. The first engagement with artificial meat might be less strict since only a first try would probably not lead to negative consequences. However, this is not the same case for willingness to eat regularly. When respondents were asked about their willingness to eat, their concerns about taste, safe, healthiness, and nutrition became more important. For those who think artificial meat has fewer advantages in the aforementioned characteristics, they would refuse to eat it regularly.

Extremely strict boundaries may not always exist between acceptance and rejection. For some swing consumers, the internalities of artificial meat such as taste and appearance might not so important. Consumer behaviors are complex and multifactorial. The conclusion that can be drawn from this study might be that the large number of swing consumers are the real market to look for. Moreover, the producers may seize the chance of consumers' willingness to have a first try, to convince their potential consumers by resolving the concerns about safety and the others. Besides, extremely emotional resistance to artificial meat in line with the perspective of "artificial meat is absurd and/or disgusting" will lead to the rejection of artificial meat directly. However, nearly half of respondents who think "artificial meat is fun and/or intriguing" would probably willing to try and pay with a curiosity attitude, but also with an attitude "I do not accept artificial meat as an alternative to conventional meat, I eat meat substitutes though". Extending these results to the huge Chinese population of China, we can speculate that most people may have a wait-and-see attitude towards artificial meat based on the uncertainty of this novel product, which is an obstacle needed to be resolved by the proponents of artificial meat.

Reasons for Willingness to Try

The most important reasons to try artificial meat mainly focus on food production and safety issues to satisfy the nutritional needs of the Chinese population. Indeed, in China, saving food has always been a topic of great concern to society especially after the great Chinese famine (1958)(1959)(1960)(1961)(1962) and the conception of saving food is deeply rooted in the hearts of the Chinese people. Therefore, it is highly inadvisable to waste food under the premise that there are 815 million people in a state of food shortage today [52]. Artificial meat is expected to produce meat in a highly effective way to solve the estimated increase in meat demand [53]. Chinese people would be willing to make a contribution to the world hunger issues by eating artificial meat.

A quarter of respondents had a willingness to try by curiosity. Since with the constant update of novel concepts and the rapidly increase of middle-class in China, people are looking for luxury food with a high curiosity and acceptance of new products [6]. Although it was also observed that the consciousness about animal welfare is growing as Chinese incomes and the industrialization of animal farming increases [54], less than a quarter of people aimed to improve animal welfare and reduce suffering through artificial meat. Among all the options, environmental-and ethical-based considerations ranked quite behind, indicating that Chinese consumers would be willing to try artificial meat not mainly due to the concerns of ethics and environment. In addition, one possibility which cannot be ruled out is that people still have not understood the potential advantages of artificial meat for the environment and ethics compared to conventional meat. However, we may conclude here that, despite the fact that the environmental issues have attached worldwide attention, Chinese people still seem not to pay much attention to these concerns compared to other issues such as food hunger in particular with the artificial meat topic.

Reasons for No Willingness to Try

Previous observations indicated that in developing countries, concerns about environmental animal welfare and food security issues is less than in developed countries [40,[START_REF] Grunert | Future trends and consumer lifestyles with regard to meat consumption[END_REF]. This may be true concerning environment and animal welfare issues which might be not as great as in Western countries. However, given the particular case in China, people worry a lot about food safety, which is indeed a major global public health concern and even particularly serious in China. Major sources of food safety issues in China are illegal additives and chemical contamination [START_REF] Lam | Food supply and food safety issues in China[END_REF]. In China, the "green food" label-an eco-certification scheme for food-is used to certifies both the production process and the product characteristics [START_REF] Lin | Green food industry in China: Development, problems and policies[END_REF]. With this organic label, natural, safety, high-quality, and nutritious properties of food will be guaranteed and trusted by Chinese consumers subsequently with a higher willingness to pay [START_REF] Yu | Willingness to pay for the "Green Food" in China[END_REF]. Artificiality, which has a negative connotation of unnatural, chemical, and lab-produced, contrasts with the principles of green food. This will negatively influence Chinese consumers' attitudes toward artificial meat to a large extent. Although the term "artificial meat" is likely to highlight the unnaturalness of the product, the use of other terms such as "cultured meat" still cannot get rid of the fact that this novel product is not produced naturally. In fact, the safety issue is also a concern of some scientists, who claimed that the process of artificial meat production is never perfectly controlled, and some unexpected biological mechanisms could occur with potential negative impacts on human health or for the environment [START_REF] Hocquette | Is in vitro meat the solution for the future?[END_REF].

Consistent with the previous literature, unnaturalness was cited as one of the biggest barriers to consumer acceptance that could induce emotional resistance and lead to rejection of the novel product [48,[START_REF] Laestadius | Is the future of meat palatable? Perceptions of in vitro meat as evidenced by online news comments[END_REF][START_REF] Bryant | Strategies for overcoming aversion to unnaturalness: The case of clean meat[END_REF]. On the opposite side, Anderson and Bryant [START_REF] Anderson | Messages to Overcome Naturalness Concerns in Clean Meat Acceptance: Primary Findings[END_REF] underlined that naturalness does not always mean goodness, whereas unnaturalness does not always mean badness. But according to Deckers [START_REF] Deckers | Are Scientists Right and Non-Scientists Wrong? Reflections on Discussions of GM[END_REF], naturalness is a deeply rooted worldview, the current development of artificial meat is challenging this worldview that is unlikely to be an effective advantage for persuading consumers [START_REF] Bryant | Strategies for overcoming aversion to unnaturalness: The case of clean meat[END_REF]. According to our observations, concerns regarding safety and unnaturalness might be the most barriers for Chinese people's willingness to engage with artificial meat. In addition, although a greater preference for artificial meat is significantly consistent with a lower price [49], the high price is not the main reason for Chinese people to reject the product at this stage, probably because respondents are not aware of the real price in practice since this novel product is not currently available commercially [8].

Expectations for Artificial Meat

In line with the previous findings, safety is the most critical expectation for artificial meat. Interestingly, what really matters for Chinese people, in addition to safety, are the instinctive expectations for food mainly taste and nutritional value. The expectations about environment protection, price, and animal welfare were less important than safety, taste, and nutritional value in the Chinese context. As a consequence, to improve the acceptance of this novel food by the Chinese consumer, research and development should be driven by improvements in safety, taste, and nutrition value. 4.3.6. The Potential Possibility to Engage with Artificial Meat Dempsey and Bryant [19] interviewed stakeholders in China who acknowledged that the barriers for artificial meat in the Chinese market will differ from those in West countries. Indeed, Chinese respondents who mainly focus on safety, nutritional value, origin, and traceability during their food purchase were more unlikely to try artificial meat compared to respondents who mainly focus on ethics, energy intake as well as marks and labels who were more willing to try artificial meat. Safety, nutritional value, and traceability of food are shown to be the main drivers for greater profitability [START_REF] Bai | Food Economics and Policies: Challenges and Approaches toward Better Nutrition and Safer Food in China[END_REF]. Thus, guaranteeing and improving factors that matter for Chinese consumers can improve the adoption of this novel food.

Previous studies indicated that most consumers are willing to try artificial meat, but a relatively small proportion would choose it over conventional meat or other meat alternatives [8]. In our study, nearly half of the respondents would be willing to try (29.8% probably, 19.9% definitely), close to half of the respondents refuse to eat artificial meat regularly, the rest of the others would be willing to eat regularly depending on conditionals (around one third prefer at a restaurant, at home, and ready-to-eat meals, respectively), nearly both half of them would be willing to accept artificial meat as an alternative to conventional meat. In addition, 10.7% of the respondents who would try artificial meat and 14.4% of the respondents who would eat artificial meat regularly would accept artificial meat instead of meat substitutes. By contrast, a third of the respondents would try artificial meat and eat meat substitutes, but they do not accept artificial meat as an alternative in the future. China has a long history of producing and eating meat substitutes made with soybean proteins. In the light of the present results, Chinese consumers would still prefer to consume plant-based meat substitutes rather than accepting the idea that artificial meat might become a viable alternative in the future.

Food neophobia and food disgust have significant effects on the willingness to consume artificial meat [50]. We observed that once people have emotional resistance and negative feeling such as absurdity and/or disgusting, they would be likely unwilling to try and to eat regularly artificial meat. On the other hand, for the participants who had no willingness to eat regularly, half of them thought artificial meat is "fun and/or intriguing". However, among people having a positive perspective such as "promising and/or acceptable", one-third of them had no willingness to eat it regularly yet, which indicates that being very positive toward artificial meat does not induce any great willingness to eat it regularly.

Wilks and Phillips [START_REF] Wilks | Attitudes to in vitro meat: A survey of potential consumers in the United States[END_REF] reported that 65% would be willing to try artificial meat, but among them, 33% would be willing to eat it regularly. Weinrich et al. [16] found that 57% of German consumers are willing to try artificial meat and only half of those would be willing to eat regularly. More positively, in the current study, 20% of respondents would be definitely willing to try and among them, 64% would be willing to eat regularly. In the same way, 30% would be probably willing to try and, among them, 63% would be willing to eat regularly. In brief, being willing to try artificial meat does not mean people would be willing to eat regularly, which is understandable. What is unanticipated is that a few proportions of respondents indicated that they were willing to eat artificial meat at a restaurant, home-made, or in ready-eat-meals although, they had even no willingness to try artificial meat. Indeed, we could have speculated that, if people refuse to try at the first step, they would be reluctant to engage further with artificial meat. Alternatively, it can be interpreted as a strong and direct wish of people to eat regularly artificial meat, with no need of a first try, or without any need of previous or specific information about the type of meat in dishes. Another possibility can be that when some specific options of food (burger, lasagna, or eating at a restaurant) were offered to consumers, it will stimulate their subconscious willingness through thoughts such as "it's OK to eat" or "why not eat?" This observation reveals that it is difficult and tricky to predict consumers' willingness in practice. Respondents' answers might be subject to bias because indicating our own attitudes and willingness to engage with a product which is not yet available is difficult [8]. Sharma et al. [66] demonstrated that consumer acceptance could be the biggest barrier that artificial meat faces, exploring the solution to address the above difficulties is a long way off.

Future Development

The current respondents were asked about the potential future development and availability of artificial meat. Some producers are expected that artificial meat will be available in markets within five years [START_REF]Hampton Creek Says It's Making Lab-Grown Meat that Will Be in Supermarkets by[END_REF]. Moreover, recent forecasting reports have estimated by the year 2040, 35% of the global meat production may be cultivated [START_REF] Gerhardt | How Will Cultured Meat and Meat Alternatives Disrupt the Agricultural and Food Industry? ATKearney[END_REF]. The most optimistic artificial meat companies currently estimate that artificial meat will be commercially available by 2021 [START_REF] Foote | Cultured Meat Could Be on the EU Market "as Early as 2022[END_REF]. Furthermore, Mark Post, who created the world's first artificial meat hamburger, also claimed lately that cultured meat will become a food commodity in the near future due to the technological advances and investment in cultured meat [START_REF] Post | Scientific, sustainability and regulatory challenges of cultured meat[END_REF].

For the current Chinese respondents, most people estimated that artificial meat will become realistic in their daily life, a small proportion (10.3%) of people expressed their strong conviction that artificial meat will never come to the realistic world. Taken into account the centralized policy and regulatory environment in China, the development of the artificial meat industry in China would be heavily dependent on governmental decisions, the latter being partially determined by levels of acceptance amongst the general public [19]. With the fact that artificial meat became authorized for sale in late 2020 in Singapore (the first nuggets with artificial meat are available in a restaurant) [START_REF] Ives | Singapore Approves a Lab-Grown Meat Product, a Global First[END_REF], it is questioned if China will follow its Asian neighbor.

In answer to our questions concerning the public and private research models, less than half of respondents trust the public research or private research for developing research on artificial meat, which implies that more than half have no trust either on public research or on the private model. The aforementioned fact that the majority of respondents expressed their attitudes by "unsure" and "probably" indicates that a large part of respondents was actually swinging between acceptance and rejection. However, for Chinese consumers, public research centers or government-related organizations are more reliable than private companies such as startups. The Chinese may have less willingness to purchase products from startups, even if that private companies are likely to receive public funding. By contrast, if the product is developed by governmental organizations, Chinese people may have more willingness to try, to eat, and to pay for it. After all, there is a gap between expectations and reality, since there is no yet commercialized artificial meat to more precisely evaluate consumers' behavior.

Conclusions

This study investigated consumers' attitudes, perspective, willingness, and potential acceptance of artificial meat in China. According to the current responses, nearly half of the respondents would be willing to try artificial meat, close to half of the respondents refuse to eat artificial meat on a regular basis, the rest would be willing to eat regularly depending on conditions. However, new information provided by this study is that nearly 90% of the respondents would be willing to pay less for artificial meat compared to conventional meat. This observation is important following the fact that this novel product has been recently approved for sale in Singapore. Most people agree with the weaknesses of current conventional meat production and therefore may consider the potential advantages of artificial meat with respect to environmental, ethical, and health issues. These views are confirmed by the fact that a significant part of respondents would be naturally willing to reduce their meat consumption and to try and eat artificial meat as a meat substitute. For Chinese consumers, safety is the most important issue. In this context, perceived unnaturalness of artificial meat can induce a feeling of insecurity or emotional resistance and, thereby further lead to the rejection of this novel product with no willingness to eat regularly, which would be a difficult obstacle to overcome. Unlike in Western countries, ethics and environmental issues are not the main drivers of acceptance of artificial meat for Chinese consumers at this stage. In addition, this novel food is expected to be safe, tasty, and nutritious at least as much as the conventional meat for the current consumers. According to the present results, it would make sense to focus on important issues for Chinese consumers to promote any type of food products on the basis of Chinese catering culture, perspective on food, and traditional philosophy. Otherwise, anticipation of the success of artificial meat in China should be less optimistic.

have been collected in an anonymous way with no personally identifiable information and with an option not to answer.

Introduction

Despite growing concerns about environmental and ethical issues related to animal production [START_REF] Scollan | Future research priorities for animal production in a changing world[END_REF], world demand for protein and more particularly meat is expected to continue to increase and to reach 470 million tonnes in 2050, of which 72% will be consumed in developing countries (compared to 58% today) according to FAO forecasts.

In this particular context, meat produced in vitro, called "artificial meat" or "cultured meat" is presented, according to its supporters and the start-ups developing it, as a new biotechnology capable of addressing some of the current problems, such as overcoming world hunger, limiting the environmental impacts of animal husbandry while respecting current ethical values (reduction of animal slaughter and animal suffering) [START_REF] Choudhury | The business of cultured meat[END_REF] although these points are controversial, particularly with regard to environmental impact (Chriki, Ellies-Oury, & Hocquette, 2020;Chriki & Hocquette, 2020). Furthermore, so called "artificial meat" does not currently qualify as meat according to the definition of the American Meat Science Association [START_REF] Boler | What is meat? A perspective from the American Meat Science Association[END_REF]. Despite this, we will refer to it as "cultured meat" in this article since this is the most widely used wording in the scientific literature (Chriki, Ellies-Oury, Fournier, Liu, & Hocquette, 2020). "Artificial meat" was also used indifferently to minimize any naming effect.

"Cultured meat" comes from cells initially taken by biopsy from a living animal (embryonic stem cells or embryonic myoblasts). These cells are cultured and multiply a very large number of times to fuse and form mainly clusters of muscle fibres with sometimes other cells, such as preadipocytes to be used for adipogenesis [START_REF] Mehta | Adipogenesis from bovine precursors[END_REF], ready to be eaten in a burger for example. The cells are therefore grown in a culture medium optimized for them to multiply. The culture medium used must contain nutrients, hormones, growth factors, etc. (Chriki & Hocquette, 2020;[START_REF] Post | Cultured beef: Medical technology to produce food[END_REF].

Since it first appeared and gained media attention in 2013, this biotechnology has generated strong scientific and media interest (Chriki, Ellies-Oury, Fournier, et al., 2020), and has thus prompted various reactions ranging from disgust for some to fascination for others, as well as simple curiosity that could encourage some consumers to try it. Opinions are therefore divided, sometimes difficult to understand, and often complex to interpret. Given the current craze for "cultured meat" in the press media (Chriki, Ellies-Oury, Fournier, et al., 2020), it is therefore important to study these various opinions in order to better understand and target consumer expectations on this subject [START_REF] Bryant | What's in a name? Consumer perceptions of in vitro meat under different names[END_REF]Bryant, Szejda, Parekh, Desphande and Tse, 2019;[START_REF] Hocquette | Cultured meat in western media: The disproportionate coverage of vegetarian reactions, demographic realities, and implications for cultured meat marketing[END_REF]. This is why several surveys have been conducted with potential consumer groups in order to explore their attitudes and assess their perception and consent to buy and consume such a product. A first survey was carried out in 2015 with around 2000 people worldwide (Hocquette et al., 2015). Many other surveys have been carried out: the first was conducted in 2016 in the USA, with 673 responses (Wilks & Phillips, 2017), a Dutch study gathered only 165 responses [START_REF] De Graag | Exploring Consumers' acceptance towards different framing of invitro meat[END_REF], a German study 713 responses (Weinrich, Strack, & Neugebauer, 2020) and an Italian study 525 responses (Mancini & Antonioli, 2020) as examples. These surveys led to the same conclusion: despite a strong willingness to taste "cultured meat" once, the majority of consumers are not yet ready to consume it regularly, although a large proportion of them really do not know and may be influenced by more information.

However, it should be acknowledged that many of these surveys were conducted at the initiative of the proponents of "cultured meat". Furthermore, providing positive information to the consumer to describe the potential benefits of "cultured meat" is likely to influence them in favor of "cultured meat" (Bryant & Barnett, 2020;Rolland, Markus, & Post, 2020). One recent survey suggested that French consumers are less accepting "cultured meat" (Bryant, van Nek and Rolland, 2020), possibly due to increasing awareness or knowledge of this novel product. In order to confirm this recent tendency, this new study was conducted to gain a deeper understanding of consumers' perception of "cultured meat" currently in France. To do this, the questionnaire included increasingly precise questions which were inspired by the weaknesses and limitations of previous surveys, thereby making it possible to go further in the analysis. It was observed that, despite the textual description of "cultured meat" production, respondents were still confused between "cultured meat" and plant-based meat alternatives. This was likely to affect the accuracy of the responses to the survey and this bias might exist in previous studies. Therefore, a graphic explanation was used in the current survey to help respondents to immediately understand the concept of "cultured meat" avoiding some bias. In addition, in the previous surveys, the distinction was not always made between regular consumption and occasional consumption when testing "cultured meat". This point was specifically addressed in this survey by questioning French consumers about their willingness to try, eat regularly and pay for "cultured meat". Besides, although not fully representative of the French population, given the large number of respondents as in the present survey (N > 5000), the results will be statistically more reliable when analyzing the relationships between answers. Consequently, this study will be of unprecedented quality in identifying the motives and barriers to "cultured meat" acceptance as it is based at least on 3 to 4 times more responses than other studies that may have been published so far.

The objective of this study was therefore to study the perception of French consumers with regard to "cultured meat", to determine with greater accuracy whether or not, in France, in 2020, "cultured meat" could have a future in the coming years on our plates.

Materials and methods

This study was carried out in three stages: 1) drafting the questions and formatting the survey, 2) distribution via different social network platforms by different people with various activities and relational networks, and finally 3) statistical analysis and interpretation of the results.

Questionnaire design

Thirty questions were drafted, in order to carry out a neutral and objective survey, but also clear and understandable to all. Neutrality is an essential criterion because respondents should not be influenced by the wording of the questions. In addition, this survey was inspired in certain principles by a first survey carried out in 2015 (Hocquette et al., 2015) in order to assess the possible evolution of consumer perception five years later. Indeed, many articles on this subject have flourished, particularly in the media (Chriki, Ellies-Oury, & Hocquette, 2020), and consumers are therefore now more aware of the subject.

The questions were then classified into 7 categories to clarify the reading path of the survey. Thus, after a brief illustrated introductory text to present the context of the survey, the questionnaire was organized around:

- This 30-question quiz was improved by an expert in social sciences and formatted using the Google Forms app. This article presents the results of one part of the questionnaire only. This research project started in March 2020 with the development of the action plan and protocol, and the dissemination of the online survey began in June and finished at the end of 2020 with the possibility of spontaneous sharing by others.

The process adhered entirely to the ESOMAR (European Society for Opinion and Market Research) guidelines on ethical online research ESOMAR, 2011). This includes ensuring that respondents gave their informed explicit consent to take part in the survey and that their personal data was protected. Indeed, after being informed of the objectives of the survey and how the information provided would be used, all respondents gave their informed consent for the inclusion of their answers before and after they participated in the study. Respondent details were collected in an anonymous way with a "do not wish to answer" option and with no personally identifiable information. Furthermore, this work is part of an international survey available in different languages including Chinese as described by [START_REF] Liu | Chinese consumers' attitudes and potential acceptance toward artificial meat[END_REF]. This research was conducted following local guidelines based on the laws and regulations of the countries in which the research was performed and this includes ethical approval by ethics committees when required (such as in Brazil: CAAE number: 37924620.5.0000.5404, [START_REF] Chriki | Brazilian consumers 'attitude towards what is called « cellbased meat[END_REF].

Data collection

Subsequently, the survey was distributed as had been done for the American, Italian and German surveys, for example (Mancini & Antonioli, 2020;Weinrich et al., 2020;Wilks & Phillips, 2017). The first É. Hocquette et al. dissemination was carried out on a small scale, through those close to the designers of the survey: this made it possible to have a preliminary external opinion so that minor adjustments could be made. Finally, the survey was widely disseminated on social networks: various Facebook accounts with over 2000 people potentially approached simultaneously, LinkedIn to over 10,000 people, 2000 on ResearchGate, Twitter to around 1000 people and Instagram to around 10,000 people. We noticed that the survey was then spontaneously shared by others interested in the topic including supporters of "cultured meat".

After collecting more than 5418 responses from different channels, statistical analyses were carried out. Descriptive statistics were used to explore the data, using Microsoft Excel software (see Tables 1 to 5 in supplementary material).

Statistical analysis

The data were analyzed using R software (version.3.5.2) (R Core Team, 2014) and IBM SPSS 25 (2017) using a variety of statistical techniques as previously done (Bryant et al., 2020;Liu et al., 2021).

As in the previous study by Bryant et al. (2020), some, but not all of the assumptions of ANOVA were, in a few cases, violated, such as normality of distributions and homogeneity of variances. However, ANOVA is considered as being robust (Schmider, Ziegler, Danay, Beyer, & Bühner, 2010). Variance analysis was thus performed in SPSS as previously described (Liu et al., 2021) to determine whether demographic variables affect respondents' willingness to try, eat regularly and finally pay. Then the post-hoc test was performed using Tukey's HSD test with the Bonferroni correction for pairwise comparisons between significant groups. Differences were considered significant at a Bonferroni-corrected p < 0.05.

In addition, a Principal Component Analysis (PCA) was performed with quantitative data to represent and model multidimensional point cloud datasets, showing whether relationships exist between variables [START_REF] Chriki | Brazilian consumers 'attitude towards what is called « cellbased meat[END_REF][START_REF] Destefanis | The use of principal component analysis (PCA) to characterize beef[END_REF]. Agglomerative clustering was also performed with the Euclidean metric, average linkage and with 4 clusters. This number of clusters was chosen after analysis of the silhouette score. The average silhouette score over all data points for this resulting clustering is 0.24. We then have discarded two clusters with an insufficient number of data points (7 and 9 data points respectively). The differences between the two major clusters were analyzed using the Student's t-test for quantitative data and the chi square test for qualitative data.

Results

Demographic information of respondents

A total of 5418 responses were collected via the online survey for the French-speaking population (Table 1). The first part concerned personal questions in order to collect demographic information. A good gender balance was observed with 52.6% women and 47.4% men, which is very close to the current gender distribution in France: 51.6% women and 48.4% men (World data atlas, 2020). In terms of the age of the participants in the survey, there was a majority of young people between 18 and 30 years of age (40.5%), slightly less between 31 and 50 years of age (36.2%) but far fewer seniors over 51 years of age (only 23.3%). With regard to the level of education, there was strong heterogeneity with 1.2% of respondents having primary or high school education, 14.4% having a baccalaureate diploma, 25.5% having a Licence (Bachelor's degree) or having studied for 2 years after the Baccalaureate, 45.9% having a Master's degree and finally 11.0% having a PhD. With regard to occupation, 7.9% of the respondents were scientists specializing in meat, 36.8% were other scientists not familiar with the meat sector, 32.8% worked in the meat sector but were not scientists, and finally 22.4% had another occupation unrelated to science or the meat sector. With regard to net monthly income, there was a good balance of salaries: 28.2% of the respondents earned less than €1500, 17.5% between €1500 and €2000, 13.8% between €2000 and €2500, 9.6% between €2500 and €3000, 9.19% between €3000 and €4000 and finally 8.6% earned more than €4000 per month. As for meat consumption, only a few (6.2%) had a vegetarian or vegan diet, 13.4% consumed it rarely (weekly or less), the majority of respondents (54.5%) consumed meat regularly (i.e. several times a week), and 13.4% consumed it daily or at every meal. Finally, the survey asked the French respondents whether or not they were familiar with the subject of "cultured meat", and unsurprisingly a very large majority (86.3%) of the respondents had already heard of this new biotechnology.

To conclude, a total of 5418 responses from different consumer segments was collected to provide multiple perceptions of "cultured meat". However, the results show a majority of young respondents (between 18 and 30 years of age), with a Master's (MS) degree, mostly eating meat regularly (with a few vegetarians), and above all almost all the respondents had prior knowledge of "cultured meat".

Effects of demographic factors on perception, willingness to try, eat

regularly or pay for "cultured meat"

Global perception

A key question of the survey was to ask respondents about their global perception of "cultured meat" with the following wording: "What do you think of 'artificial meat'?" They were given the choice of only three possibilities based on the major perceptions previously reported in the literature 1: It is absurd and/or disgusting; 2: It is fun and/or interesting. 3: It is promising and/or feasible.

As shown in Fig. 1, the majority of the respondents (59.2%) think that this product is "absurd and/or disgusting". A smaller part of the respondents find it "fun and/or interesting" (23.9%) or "promising and/ or feasible" (16.9%).

Willingness to try

A total of 50.6% of respondents would like to try "cultured meat" (18.7% answered "Definitely yes" and 31.9% "Probably yes", Table 1 in supplementary material). However, willingness to try (WTT) depends on many factors. It differs the most according to education level, occupation and meat consumption habits interacting with each other and to a lesser extent interacting with age or gender (Table 6 in supplementary material).

Male respondents with a Master's degree or PhD had a higher (P < 0.05) WTT (Score on a 1-5 scale >3.2) than those with less education (< 2.6). The same trends were observed for female respondents, except that respondents with primary or high school or a Baccalaureate education had an intermediate WTT so that the WTT differed (P < 0.05) between males (1.7 to 2.3) and females (2.6 to 2.8) for these two groups. In other words, less educated males had a lower WTT than less educated females but both more educated males and females had a higher WTT than the less educated ones.

According to the results of this survey (N = 5418 responses), females who eat meat daily have a lower (P < 0.05) WTT (< 2.8) than females who eat less meat from never to regularly (> 3.1). Similarly, males who eat meat daily or regularly have a lower (P < 0.05) WTT (< 2.9) than males who eat less meat from never to rarely (> 3.5). Consequently, the WTT differs between males or females for each level of consumption (P < 0.05).

Young respondents (18 to 30 years of age) familiar with the meat sector have a lower (P < 0.05) WTT (< 2.75) than other groups of the same age (> 3.35) namely respondents who do not know the meat sector or who are scientists. This trend is also observed for the other age groups. More generally, for a given age group, scientists have a higher WTT than other groups (Table 2A).

Respondents over 31 years of age who eat meat daily have a lower (P < 0.05) WTT (< 2.42) than respondents of the same age who eat meat rarely or never (> 3.0). Respondents under 31 years of age who eat meat daily have a lower (P < 0.05) WTT (< 3.0) than respondents of the same age who eat meat rarely (> 3.8). So, WTT is negatively affected by the level of meat consumption but to a slightly different extent according to age.

When respondents are both less educated (primary school up to two years after the Baccalaureate) and familiar with the meat sector (scientists or not), they have a lower (P < 0.05) WTT (< 2.4) compared to more educated respondents (Baccalaureate up to PhD) either scientists (knowing the meat sector or not) or not familiar with the meat sector, who have a higher WTT (> 3.1).

When respondents are both less educated (primary school up to two years after the Baccalaureate) and eat meat regularly or daily, they have a lower (P < 0.05) WTT (< 2.5) compared to more educated respondents (Baccalaureate up to PhD) who never or rarely eat meat, who have a higher WTT (> 3.35).

Non-scientist respondents who eat meat regularly or daily familiar with the meat sector or not have a lower (P < 0.05) WTT (< 2.9) than scientists who do not know the meat sector, (WTT > 3.2) or scientists who do know the meat sector who never eat meat (WTT = 3.8) (Table 2B).

Willingness to eat

A total of 20.3% of respondents would like to eat "cultured meat" regularly, either at home, in restaurants and/or in ready-made meals, which means that 79.7% of respondents answered that they did not want to eat "cultured meat" regularly ( occupation, level of meat consumption and income interacting with one another and to a less extent interacting with age or gender (Table 7 in supplementary material). WTE significantly differs according to gender interacting with occupation and meat consumption. Indeed, WTE tends to differ according to occupation with respondents familiar with the meat sector whether non-scientists or scientists having the lowest (P < 0.05) WTE (1.08 to 1.15) compared to respondents not familiar with the meat sector, whether non-scientist (1.25) or scientist (1.28-1.30).

Respondents who eat meat daily have the lowest WTE (on average 1.10) and respondents who never eat meat have the highest WTE, followed by people who rarely eat meat with a difference between males and females in both cases (P < 0.05) (Table 3A). WTE is also the highest among young people not familiar with the meat sector (P < 0.05). Being older and/or being familiar with the meat sector are both factors that decrease WTE (Table 3B). WTE is also the lowest for respondents over 31 years of age who eat meat daily (Table 3C). WTE is the lowest (1.04) among respondents familiar with the meat sector, who are not scientists and eat meat daily. In contrast, WTE is the highest for respondents who never eat meat (from 1.31 to 1.50). For the other groups, eating meat more frequently or being familiar with the meat sector are both factors that decrease WTE (Table 3D).

WTE is the lowest for respondents who eat meat daily (1.08 to 1.11) and the highest for respondents who never eat meat, especially for those who have heard of "cultured meat" (1.37). However, the difference is only significant in terms of familiarity for respondents who eat meat rarely or regularly, with a higher WTE for respondents who have never heard of "cultured meat" prior to the survey (Table 3E).

WTE also depends on income but with a different pattern according to age. Indeed, for young people, WTE is the highest (1.32) for incomes below €1500 per month. In contrast, WTE is the highest (1.20) for the highest incomes above 31 years of age.

Willingness to pay

About 68.5% of the respondents would like to pay less or much less (including nothing) for "cultured meat" compared to "conventional meat". In addition, 22.7% of them declared they would be willing to pay the same price (Table 1 in supplementary material). This means that only 8.7% of the respondents were willing to pay more or much more for "cultured meat" than for "conventional meat". However, this willingness to pay depends on meat consumption and education level which also act interact (Table 8 in supplementary material).

Like WTE, WTP is the lowest among the oldest respondents eating meat daily. It is the highest among the youngest respondents who never eat meat (Table 4).

WTP is the lowest for young people (18 to 30 years of age) in primary or high schools (1.00) or for older people (over 31 years of age) with less than a master's degree level of education (<1.75). On the other hand, WTP is the highest (> 2.8) for young people (18 to 30 years of age) after high school and especially with a master's degree or PhD (>2.3). Respondents with a PhD have the highest WTP (>1.97) but in ascending order: people over 51 years of age (1.97), between 31 and 50 years of age (2.07) and between 18 and 31 of age (2.49). There is the same tendency for the master's degree level but from 1.77 for the oldest respondents to 2.30 for the youngest.

Drivers, motives and barriers to willingness to try, eat or pay for

"cultured meat"

Societal challenges

Another part of the survey concerned opinions on societal challenges related to/concerning the meat industry. The respondents seem to be rather unsure as to the ethical (such as animal suffering and slaughter) and environmental issues (such as high water consumption or greenhouse gas emissions) related to livestock and the meat industry. To the question "Do you think the on-farm breeding and meat industry cause major ethical problems (e.g. animal suffering, slaughter, etc.)?" people answered in similar proportions (between 17.8 and 23.4% from score 1 [I strongly disagree] to score 5 [I strongly agree]). A similar range (between 17.5% and 23.1%) was observed for the answers related to the question: "Do you think that the on-farm breeding and meat industry cause major environmental problems (e.g. huge water consumption,

Table 3

Pairwise comparisons for willingness to regularly eat "artificial meat" between demographic groups for different interactions between selected factors (level of meat consumption, gender, age groups, occupation and familiarity with "artificial meat", N = 5418 responses). greenhouse gas emissions, etc.)?". This is also a high degree of homogeneity observed concerning the possibility of reducing meat consumption to address these potential problems, with 27.3% for a score of 1 and 28.8% for a score of 5. However, almost half (49.2% of score 1) the respondents definitively think that "cultured meat" would not be more ethical than "conventional meat", i.e. able to significantly improve animal welfare and reduce animal suffering. Likewise, half of the respondents (50.4% of score 1) believe that "cultured meat" would definitively not be more eco-friendly than "conventional meat", i.e. capable of significantly reducing the environmental footprint linked to livestock (water consumption, global warming, greenhouse gas emissions, etc.). Finally, a clear majority of respondents believe that "cultured meat" would have negative impacts on traditional breeding and the meat industry with 55.6% of score 5 (for example on jobs) and on the territories and rural life with 61.8% of score 5 (for example on biodiversity, tourism, landscape maintenance and the vitality of territories) (Table 2 in supplementary materials).

Characteristics of the product

Respondents were also questioned about their opinion on the health and taste characteristics of this new biotechnology. The majority of Respondents had to answer on a scale of 1 to 5 expressing their disagreement (lower scores) or their agreement (with higher scores) to the indicated questions. Respondents had to answer on a scale of 1 to 5 expressing their disagreement (lower scores) or their agreement (with higher scores) on the indicated questions. Respondents had to answer on a scale of 1 to 5, expressing their disagreement (lower scores) or their agreement (with higher scores) to the indicated questions.

É. Hocquette et al. Respondents had to answer on a scale of 1 to 5, expressing their disagreement (lower scores) or their agreement (with higher scores) to the indicated questions. respondents thought that "cultured meat" would not be healthy and safe (39.3% gave a score of 1 and 20.5% a score of 2 on a scale of 5): it would not have a consistent quality and sufficient nutritional quality, especially in proteins and vitamins. Likewise, according to them, this product would not be as tasty as "conventional meat" either (51.9% gave a score of 1 and 21.8% a score of 2 on a scale of 1 to 5): it would not be comparable to conventional meat in terms of taste and flavor (Table 3 in supplementary material). In summary, the French respondents to our survey seem sceptical about the safety as well as the taste and flavor of "cultured meat".

Perception and strategy

The majority of respondents have emotional resistance (disgust, nervousness) to "cultured meat" (55.5% of scores of 4 and 5 on a scale of 5, Table 4 in supplementary material). However, 40.6% of the French people who responded to the survey think that "cultured meat" could be feasible in the medium term, between 6 and 15 years, and 28,0% in the short term (between 1 and 5 years), only 9.3% in the long term, i.e. in 16 years or more. And there are still 22.1% who think that this new biotechnology could never be marketed (Table 4 in supplementary material).

Nearly half of French people (46.4% who voted 1 or 2) are not convinced of the private research model (by start-ups) to potentially develop research on "cultured meat", whereas about a quarter (28.5%) have no clear opinion (score 3). However, they are clearly in the majority (62.8%) to think that public scientific research should not invest resources and money to develop this biotechnology (Table 5 in supplementary material).

Importance of potential drivers and barriers of "cultured meat" acceptance

To better understand the positive drivers, motives and barriers of consumers' acceptance of "cultured meat", a PCA was performed with all the quantitative variables described above.

A first group of variables in the upper right-hand corner of the PCA plot is related to a positive acceptance of "cultured meat" with positive answers to different questions such as "Is 'artificial meat' safe/ healthy/ tasty/ethical/eco-friendly?" or "Should private/public research invest in this area?". High WTT, WTE and WTP are also present among these variables. Indeed, WTT and WTE are positively correlated (r = 0.50) and also positively associated with WTP (r = 0.29) but moderately. This can be explained by the fact that curiosity is the main reason for WTT (Fig. 2) but less for WPP and WTE "artificial meat" regularly. In addition, WTT is sensitive to more demographic factors than WTE. Finally, as described above, WTE and WTP also depend on the income of the respondents and WTE on previous knowledge of "cultured meat" unlike WTT.

A second group of two variables includes strong emotional resistance to eating "cultured meat" and the perception that "cultured meat" will not be successful in the long term or even never. This group of variables is the opposite of the first, especially emotional resistance which is negatively correlated with WTT (r = -0.61). These two opposing groups Respondents had to answer on a scale of 1 to 5, expressing their disagreement (lower scores) or their agreement (with higher scores) to the indicated questions. of variables define a first axis which refers to "cultured meat" acceptance.

A third group of three variables that are highly correlated with each other (0.69 to 0.80) in the lower right-hand corner of the PCA plot are related to perceived issues of "conventional meat" production. These include positive answers to the following questions: "Do you think the on-farm breeding and the meat industry cause major ethical/environmental problems?" and "To address these potential problems, do you think that reducing our meat consumption could be a good solution?"

A fourth group of variables opposite to the third includes positive answers to the following questions: "Do you think 'artificial meat' would have negative impacts on traditional livestock farming and the meat industry and on territories and rural life?', with the answers to these two questions being highly correlated (r = 0.63). These two variables are negatively correlated (-0.48 < r < -0.31) with the variables of the third group. Altogether, these two groups of variables define a second axis related to concerns about current livestock systems and meat production or consumption. When people are concerned about ethics and environmental issues related to livestock, they want to reduce their meat consumption. Additionally, they do not believe that "cultured meat" would reduce rural life and conventional farming. In contrast, people who worry about the decline of rural life and farming induced by "cultured meat" are less concerned (or not at all) about the ethics and environmental issues related to livestock.

The two axes defined above (related to "cultured meat" acceptance on one hand and concerns about meat production on the other) are almost orthogonal which means that they are, at least in part, independent. This can be interpreted as meaning that having ethical and environmental concerns about "conventional meat" production is probably not enough to have a good acceptance of "cultured meat". Indeed, the variables in the upper and lower right-hand parts of the plot are only moderately correlated (around 0.3 to 0.6) indicating that concerns about current meat production contribute modestly to acceptance of "cultured meat". Furthermore, concerns about the negative impact on territories, rural life and local farmers are clearly barriers to "cultured meat" acceptance since positive answers to these questions are negatively correlated with WTT, WTE and WTP (-0.42 < r < -0.19). To summarise, to have good acceptance of "cultured meat", consumers must not only have concerns about "conventional meat" production but, in addition, be convinced that "cultured meat" is ethical, eco-friendly, safe, tasty, etc. This is apparently not the case for all respondents, since among those who already eat meat substitutes, a great majority of them would not accept "cultured meat" as a viable alternative to "conventional meat" and other meat substitutes (Fig. 3).

All these interpretations are confirmed by two other questions in the survey. Indeed, to the question "Why would you NOT be willing to try 'artificial meat'?", respondents answered unnaturalness (72.6%), negative impact on territories and rural life (65,0%) and on local farmers (64.1%), less trust in laboratories and "cultured meat" start-ups (59.2), less taste (37.2%), etc. (Fig. 4A). In contrast, the main expectations of "cultured meat" were reduced environmental footprint (34.4%), safety (33.2%), good taste (31.8%) and no animal pain/suffering (25.3%) (Fig. 4B). In addition, no animal pain/suffering was also the second driver of WTT (for 22.0% of respondents Fig. 4C). The major barrier of unnaturalness was confirmed by the answers to the following question: "Which names are the most relevant to you to qualify 'artificial meat'? (among the names most commonly used by companies in the sector)": in fact, the majority of respondents chose "artificial meat" or "synthetic meat" (55.1%) which both refer to the non-natural process. In contrast, the term "cultured meat" was chosen by only 23% of respondents. In addition, the term "clean meat" was chosen by only by 2.3% which indicates that respondents do not perceive the potential low environmental impact of "cultured meat" despite claims by its proponents.

Effects of demographic factors on drivers, motives and barriers to acceptance of "cultured meat"

"Gender x age" interaction

According to the survey (N = 5418), the respondents the most concerned about the ethical and environmental issues related to livestock are the young females (i.e. under 30 years of age) and the least to be concerned are the older males (over 51 years of age). Similarly, the young females believe the most, and the older males the least, that "cultured meat" would be more ethical and more eco-friendly than "conventional meat" (Table 5A).

The respondents who are concerned the least about the potential impacts of "cultured meat" on traditional livestock farming, the meat industry, territories and rural life, are the young males and females, whereas the respondents who are the most concerned are the men over 31 or 51 years of age (Table 5B).

57.5%

7.1% 9.0%

26.5%

Would you accept arƟficial "meat" as a viable alternaƟve to "convenƟonal meat" in the future, compared to other meat subsƟtutes (such as soy protein) or other soluƟons (such as reducing food waste or developing our farming pracƟces)?

No and I don't eat meat subsƟtues/alternaƟves No but I already eat meat subsƟtutes/alternaƟves Yes but I don't eat meat subsƟtutes/alternaƟves Yes and I already eat meat subsƟtutes/alternaƟves Fig. 3. Potential acceptance of "cultured meat" also called "artificial meat" compared to other solutions and other meat substitutes.

É. Hocquette et al.

To the questions "To what extent do you think that 'artificial meat' is healthy, safe, tasty and of high nutritional value compared to 'conventional meat'?", all the young people (regardless of gender) answered more positively than the other demographic groups (Table 5B).

"Gender x level of meat consumption" interaction

The respondents the most concerned about the ethical and environmental issues related to livestock are the respondents (females and males) who never eat meat and the least concerned are the males who eat meat daily. Similarly, the males who never eat meat believe the most, and the males who eat meat daily the least, that "cultured meat" would be more ethical and more eco-friendly than "conventional meat" (Table 6A).

The respondents who are the least concerned about the potential Reasons not to try "cultured meat" also called "artificial meat" (A), expectations (B) and reasons to try "cultured meat" (C). Results are expressed as a percentage of answers (N = 5418 responses). As these are multiple choices questions, the sum of the percentages is greater than 100.

impacts of "cultured meat" on traditional livestock farming, the meat industry, territories and rural life, are the males and females who never eat meat, whereas the respondents who are the most concerned are the respondents (males and females) who eat meat daily (Table 6B).

To the questions "To what extent do you think that 'artificial meat' is healthy, safe, tasty and of high nutritional value compared to conventional meat?", the males who never eat meat answered the most positively and the respondents (males and females) who eat meat daily the most negatively (Table 6B).

"Occupation x level of meat consumption" interaction

The respondents the most concerned about the ethical and environmental issues related to livestock are the respondents who never eat meat, followed by the respondents who eat meat rarely. The respondents the least concerned about these issues are those familiar with the meat sector and who eat meat daily. Similarly, the respondents who never eat meat believe the most that "cultured meat" would be more ethical and more eco-friendly than "conventional meat", but to a lesser extent for respondents familiar with the meat sector. Those familiar with the meat sector and who eat meat daily believe the least that "cultured meat" would be more ethical and more eco-friendly than conventional meat (Table 7A).

The respondents who are the least concerned about the potential impacts of "cultured meat" on traditional livestock farming, the meat industry, territories and rural life, are the respondents who never eat meat and who are not scientists nor familiar with the meat sector, whereas the respondents who are the most concerned about these issues are those who eat meat daily or regularly and who know the meat sector (scientists or not) (Table 7B).

To the questions "To what extent do you think that 'artificial meat' is healthy, safe, tasty and of high nutritional value compared to conventional meat?", the respondents who never eat meat answered the most positively and the respondents who eat meat daily and familiar with the meat sector the most negatively (Table 7B).

"Level of meat consumption x age" interaction

The respondents the most concerned about the ethical and environmental issues related to livestock are the respondents who never eat meat, followed by the respondents who eat meat rarely, regardless of age. The respondents the least concerned about these issues are the respondents over 31 or 51 years of age and who eat meat daily. Similarly, the respondents who never eat meat believe the most that "cultured meat" would be more ethical and more eco-friendly than "conventional meat", regardless of age. Those over 31 or 51 years of age and who eat meat daily believe the least that "cultured meat" would be more ethical and more eco-friendly than "conventional meat" (Table 8A).

The respondents who are the least concerned about the potential impacts of "cultured meat" on traditional livestock farming, the meat industry, territories and rural life, are the respondents who never eat meat regardless of age, whereas the respondents who are the most concerned by these issues are those who eat meat daily (Table 8B).

To the questions "To what extent do you think that 'artificial meat' is healthy, safe, tasty and of high nutritional value compared to conventional meat?", the respondents who never eat meat answered the most positively and the respondents who eat meat daily and over 31 or 51 years of age the most negatively (Table 8B).

"Occupation x age" interaction

The respondents the most concerned about the ethical and environmental issues related to livestock and who believe the most that "cultured meat" would be more ethical and more eco-friendly than "conventional meat" are the young respondents (under 31 years of age) who do not know the meat sector. In contrast, the respondents the least concerned about the issues related to livestock and who believe the least that "cultured meat" would be more ethical and more eco-friendly than conventional meat are the respondents over 31 or 51 years of age and familiar with the meat sector (Table 9A).

The respondents who worry the least about the potential impacts of "cultured meat" on traditional livestock farming, the meat industry, territories and rural life, are the respondents who are familiar with the meat sector regardless of age or scientists working on meat over 31 years of age, whereas the respondents who are the most concerned by these issues are the respondents not familiar with the meat sector nor scientists (Table 9B).

To the questions "To what extent do you think that 'artificial meat' is healthy, safe, tasty and of high nutritional value compared to 'conventional meat'?", the young scientists not working on meat answered the most positively and the older respondents (above 31 or 51 years of age) familiar with the meat sector and not scientist the most negatively (Table 9B).

Hierarchical classifications of quantitative and qualitative variables and of respondents

Hierarchical classification of all the variables confirmed the two clusters of variables identified by the Principal Component Analysis: one major cluster related to "cultured meat" acceptance and one cluster related to meat production issues.

Hierarchical classification of the respondents identified two major clusters with around 2700 respondents each (Table 10). The respondents in cluster 2 are more concerned about the ethical and environmental issues related to "conventional meat" production (x1.8-1.9), they would agree to reducing their meat consumption (x2.10) and they believe that "cultured meat" would be more ethical and eco-friendly (x2. 40-2.53) as well safer and tastier (x1.9-2.1). They also have less emotional resistance (x0.65), higher WTT (x1.93), and higher WTE and WTP (1.36-1.45).

Table 10

Differences in answers to the different questions in the survey by respondents from the two major clusters. Respondents had to answer on a scale of 1 to 5, expressing their disagreement (lower scores) or their agreement (with higher scores) to the indicated questions, except those indicated in the table. The differences between the two clusters are all highly significant at P < 0.001.

É. Hocquette et al.

Discussion

Respondents are concerned about the ethical and environmental issues related to livestock farming but also about traditional rural life, health and nutritional quality of food products

According to our results and despite the variability between the groups of respondents according to their sociological characteristics, the French are very concerned about the ethical and environmental problems linked to livestock farming and the conventional meat industry, and therefore a large proportion of them (45.6%) think that reducing our meat consumption could be a solution to the current problems as previously indicated by Hocquette et al. (2015) and Bryant et al. (2020). However, more than half do not think that "cultured meat" would be more ethical and more eco-responsible than "conventional meat". Thus, our study confirms a previous study (Hocquette et al., 2015) which indicated that a very large majority of French people (of the order of 60 to 70%) would prefer to consume less meat rather than "cultured meat" (10-15%). Similarly, the majority of Brazilians said they would like to reduce their meat consumption. They are, therefore, future flexitarians [START_REF] Chriki | Brazilian consumers 'attitude towards what is called « cellbased meat[END_REF]Heidemann, Taconeli, Reis, Parisi, & Molento, 2020).

Around three quarters of the respondents recognize the negative impacts of "cultured meat" on traditional livestock and the meat industry as well as on territories and rural life. Such negative impacts on conventional agriculture have also been speculated by Jairath, Mal, Gopinath, and Singh (2021), but mainly for developing countries. The French therefore remain attached to the current production system and are concerned about the future of the territories and rural life. In comparison with the same survey carried out in China, Chinese consumers seem more inclined to eat "cultured meat". They are already more accustomed to eating meat substitutes, and seem less concerned about the environmental issues (Liu et al., 2021). In addition, French people are not yet convinced by the health and nutritional quality as well as the taste of "cultured meat". This confirms a recent study indicating that meat alternatives are often perceived more negatively than conventional meat [START_REF] Michel | Consumers' associations, perceptions and acceptance of meat and plant-based meat alternatives[END_REF], in terms of health, taste and naturalness. "Cultured meat" arouses curiosity but can create some emotional reluctance such as nervousness or disgust, for more than half of the people surveyed, with a lower proportion in Croatia, Greece and Spain [START_REF] Franceković | How do consumers perceive cultured meat in Croatia, Greece, and Spain?[END_REF]. While the French are keen to try it, they would, however, prefer to buy "cultured meat" at the same price or at a lower price than conventional meat, but in the great majority, not more expensive, as confirmed by other recent surveys [START_REF] Franceković | How do consumers perceive cultured meat in Croatia, Greece, and Spain?[END_REF][START_REF] Michel | Consumers' associations, perceptions and acceptance of meat and plant-based meat alternatives[END_REF].

According to our French respondents, "cultured meat" is likely to end up on our plates within 6 to 15 years on average, which is similar to what the Chinese consumers feel (Liu et al., 2021). These same respondents seem more convinced by the model of private research to eventually develop "cultured meat", rather than by public scientific research like the Chinese consumers (Liu et al., 2021). This is probably explained by the fact that they are not generally convinced of the benefits of this new biotechnology despite their concern for the ethical and environmental issues associated with breeding. "Cultured meat" start-ups generally opt for different strategies: Mosa Meat surfs on livestock issues to present "cultured meat" as a viable alternative to "conventional meat" products. In contrast, Aleph Farm presents "cultured meat" as a complementary product to the "conventional meat" offer, although they also criticize the impacts of livestock.

Prior knowledge of the "cultured meat" subject may or not change consumer perception

In this survey, 86.3% of respondents claimed to have heard of "cultured meat", in contrast with a previous survey conducted a year ago (Bryant et al., 2020), in which only 22.6% of Germans and 21.6% of French people had heard of this biotechnology. However, for three studies published this year, the proportions of respondents who had previously heard of "cultured meat" in China (Liu et al., 2021), in Brazil [START_REF] Chriki | Brazilian consumers 'attitude towards what is called « cellbased meat[END_REF] or in Croatia, Greece and Spain [START_REF] Franceković | How do consumers perceive cultured meat in Croatia, Greece, and Spain?[END_REF] was higher (44-73%). Nevertheless, the proportion of respondents in favor of "cultured meat" does not seem to be affected by this change. Indeed, in France, 16.9% of the respondents believe that "cultured meat" is promising and/or feasible in our study against 20.0% of respondents in France who are favorable according to the study Bryant et al. (2020) in which the proportion of people who had heard of this biotechnology was much smaller.

Contrary to some studies (Mancini & Antonioli, 2020;Rolland et al., 2020), being already aware of the "cultured meat" process does not increase "cultured meat" acceptability according to our results. The differences between these observations can be explained by methodological issues. The responses collected in the studies by Rolland et al. (2020) and Mancini and Antonioli (2020) were the result of respondents presented with various information, at the time of the survey, including the potential benefits of "cultured meat". However, it does appear that the type of information provided at the time of the survey affects the perception of respondents differently, for instance, positive information (supposedly good health and nutritional qualities) would increase the acceptability of the product (Bryant & Barnett, 2020;Heidemann et al., 2020).

In our study, respondents were asked about their prior knowledge of the product before the survey. They had therefore constructed a more robust a priori opinion. Having already heard of "cultured meat" allows consumers to have given more thought to the benefits and drawbacks of this biotechnology. More particularly, when consumers have access to new information that is not consistent with what they know, it may induce a response in their opinion based on associations of pre-existing knowledge and/or based on richer knowledge by building new associations with the new information provided [START_REF] Baum | Information, attitudes and consumer evaluations of cultivated meat[END_REF]. Our questionnaire was as neutral as possible to avoid the intention to influence respondents in one direction or another. Indeed, we did not provide any new information, we simply asked respondents if they were already aware of "cultured meat".

The work of Verbeke et al. (2015) highlighted three sequences of reasoning in consumers when they are provided with new information about "cultured meat" (i) they initially express a feeling of disgust as observed in this study, (ii) express more thoughtful reactions concerning the potential advantages of "cultured meat" for society (food safety, environmental protection, respect for animals) and the possible disadvantages at individual level (personal health, reduced pleasure in consuming meat products) or collective level (loss of traditions, less farmers and agricultural activity in rural areas) and, finally, (iii) they will have more in-depth reactions which ultimately induce some scepticism with a mixture of complex concerns (for example, a need for knowledge and transparency). Thus, in the study by Rolland et al. (2020) and Mancini and Antonioli (2020), respondents were likely to be in the first stage (or for some, in stage 2) at the beginning of the survey and then progress positively into stage 2 or 3 after being more informed. In contrast, in our survey, respondents were likely to be already in stage 2 or 3 because the majority of them were already aware of "cultured meat" before the survey and some of the answers to the relatively deep questions in this survey were based on in-depth thinking from the actual participants, some in the positive and others in the negative sense.

In addition, a more recent study has indicated that while providing information will affect explicit attitudes (i.e. consumer opinions that are intentional and well expressed), implicit attitudes do not change because they are less biased by external factors [START_REF] Baum | Information, attitudes and consumer evaluations of cultivated meat[END_REF]. This can be explained, at least in part, by the fact that implicit attitudes are, by definition, not well known, unidentified or inaccurately identified, with affective factors playing a crucial role. Furthermore, willingness to buy (purchase) results from a combination of explicit and implicit É. Hocquette et al. attitudes. This is a major problem for "cultured meat" which poses a new challenge due to both its unfamiliarity to consumers and the low reliance of social studies investigating associations between attitudes, purchase and market development. This underlines the complexity of consumer opinions and behaviors [START_REF] Baum | Information, attitudes and consumer evaluations of cultivated meat[END_REF]. This complexity is likely to explain, at least in part, the above-mentioned discrepancies in the literature.

The sociological characteristics of the respondents must be taken into account when interpreting the results

There is a fairly strong generational effect, particularly with regard to ethical concerns, environmental issues, meat consumption, its impact on society and the environment, the overall perception of "cultured meat" and the purchase price of "cultured meat". Young people have a clearer idea of the potential and limits of science. The fact that older people reject "cultured meat" to a greater extent, confirms the results of another survey in Germany and France (Bryant et al., 2020), and a recent European survey [START_REF] Grasso | Older consumers' readiness to accept alternative, more sustainable protein sources in the European Union[END_REF] which show that older respondents have lower acceptance of "cultured meat". On the other hand, in an Australian survey on Generation Z [START_REF] Bogueva | Cultured meat and Australia's generation Z[END_REF], it is indicated that 72.0% of respondents of this young generation (who are, by definition, under 20 years of age) are not willing to accept "cultured meat" (compared to 40.5% of young people aged 18 to 30 in our study who find this idea absurd and/or disgusting). The way in which the questions are asked or the difference in age or maturity of the respondents may explain this type of different observation.

Gender influences concerns about the ethical and environmental problems caused by the meat industry, opinion on meat consumption and overall perception of "cultured meat". Our results confirm those of Heidemann et al. (2020) who indicate that female specialists in animal production are more in favor (65.0%) of "cultured meat" than male. This could be related to women's sensitivity to animal welfare and the environmental issues related to farming, and the fact that they are more likely to adopt diversified diets including the flexitarian diet (less meat), according to [START_REF] Ruby | Meat, morals, and masculinity[END_REF].

However, in another recent survey (Bryant et al., 2020), it was observed that women are less inclined to consume "cultured meat" than men, both in Germany and especially in France. This type of contradiction can be explained by an interaction between age and gender since it was observed that it is the youngest women and the oldest men who have the closest perceptions (Hocquette et al., 2015). However, despite a significant interaction between age and gender, our study shows, on the contrary, that older men are the most reluctant. The different wording of the questions between the surveys could at least partly explain these contradictions.

Another observation of this survey is that the majority of meat professionals are firmly opposed to "cultured meat" on all aspects: health, environmental, ethics, taste, etc. These results confirm those of Heidemann et al. (2020) and [START_REF] Chriki | Brazilian consumers 'attitude towards what is called « cellbased meat[END_REF] who indicate that animal scientists have a lot of reservations and resistance to "cultured meat", associating this biotechnology with the "unnatural" which therefore has a negative connotation. They believe, moreover, that "cultured meat" would be a danger to their respective jobs, if it will one day replace "conventional meat". These conclusions seem to contradict the results of Bryant et al. (2020) for whom working in the agricultural sector is rather a favorable factor for the acceptance of "cultured meat". However, our results are consistent with other previous results which have shown that the urban population (less familiar with livestock) would be more willing to consume "cultured meat" (Bryant & Barnett, 2018;Shaw & Iomaire, 2019).

Opinion surveys must be interpreted with caution due to their limited representative character

The representativeness of the people surveyed in our study in relation to the French population must be put into perspective. In fact, in the French population, 51.9% are over 51 years of age (under-represented in our sample with only 23.3%), 25.1% are between 31 and 50 (overrepresented in our sample with 36.2%), and 11.1% are between 18 and 30 years of age (also over-represented since they represent 40.5% of our sample). With regard to the gender distribution, it is balanced in the French population with 51.6% women vs. 48.4% men, however women are slightly over-represented in our sample (52.6%). The differences we observe reflect a greater interest in scientific news among women and young people, thus inducing a greater willingness on their part to answer this type of questionnaire. This type of bias in surveys has already been observed, for example in the survey by Bryant et al. (2020) and Heidemann et al. (2020). However, these drawbacks can be partially offset by the consequent size of the sample (N = 5418), which makes it possible to analyze each segment of the population, our survey being to our knowledge the one with the largest number of people among the population.

According to the Ministry of Higher Education, Research and Innovation, in France only 1.0% of citizens have a PhD, 16.0% a master's degree, 10.0% a bachelor's degree and 15.0% have completed short vocational studies. However, the respondents to our survey are more graduates with 45.9% having a master's degree and 11.0% a doctorate, and this is explained by the fact that the questionnaire was initially distributed in scientific networks (universities, research institutes, etc.). Since people with studies are more interested in science than others, one can assume that they respond more easily to this kind of survey. In addition, in the French population there are 3.1% scientists, compared to 44.7% in our survey, which can easily be explained by the interest of scientists in innovations as well as by the essentially university dissemination network. This agrees with the conclusions of Heidemann et al. (2020) who indicate that biotechnology engineers would be more likely to accept "cultured meat" based on their knowledge of the field as well as the professional opportunities generated by this technology once it has been marketed.

In France, 2.5% of citizens work in the meat sector, while this sector of activity represents 40.7% in our study sample. The results must therefore be corrected for this type of factor. However, as noted above, our observations are in contrast to those of Bryant et al. (2020) who, on the contrary, emphasize that professionals in agriculture or the meat sector are open to "cultured meat". The way the questions are asked, the way the subject is presented or the limited number of respondents may explain this type of discrepancy.

The results can also be potentially affected by the diet of the respondents. In an earlier study (Bryant et al., 2020), the proportion of pescatarians (who do not consume meat but fish), vegetarians (who do not consume animal flesh) and vegans (who do not consume any product of origin animal) were 3.2%, compared to 6.2% in this survey, but the proportions of flexitarians (who limit their meat consumption, without being exclusively vegetarian) are more difficult to evaluate. Given the rather low figures, it is unlikely that these different proportions would have a significant impact on the results. However, in the study by Heidemann et al. (2020), vegetarians (7.0% of respondents) and vegans (1.1%) were the most favorable to "cultured meat". Indeed, the authors indicate that those who did not consume meat were more likely to respond positively than those who consumed meat daily.

Finally, more generally, the formulation and sequencing of the questions can modify their interpretation and therefore the results (Bryant & Barnett, 2020). Furthermore, although the survey was conducted in the most neutral way possible, the choice of questions and their order can also influence the respondents, in one direction or another. Indeed, some studies are carried out by promoters of "cultured meat" with the assumed objective of convincing consumers (Tomiyama É. Hocquette et al. et al., 2020). Lastly, due to the importance of the implicit attitudes described above, a recent survey illustrated the inadequacy of relying on self-reported measures when seeking to capture consumer opinions on unfamiliar or unknown products such as "cultured meat" [START_REF] Baum | Information, attitudes and consumer evaluations of cultivated meat[END_REF]. However, despite these limitations, comparing results obtained with the same experimental design between countries such as China as published by [START_REF] Liu | Chinese consumers' attitudes and potential acceptance toward artificial meat[END_REF], Brazil as published by [START_REF] Chriki | Brazilian consumers 'attitude towards what is called « cellbased meat[END_REF] and France, or between similar social groups (within the same study as in this work) is likely to provide useful information.

Conclusion

To the best of our knowledge, this survey was conducted with the highest number of respondents compared to any previous surveys conducted in France or even in the world on the subject of "cultured meat". In addition, this study was conducted by a group of researchers belonging to public research organizations, unrelated to private companies, aiming to market this biotechnology, unlike some previous surveys.

This survey highlights that the French are very concerned about the ethical and environmental issues related to livestock farming and meat production. This is why, as shown in previous surveys, reducing the consumption of meat (known as "flexitarianism") is preferred, at the expense of "cultured meat" consumption which is not perceived as more ethical, more eco-friendly, more sustainable, or even as healthy and tasty as "conventional meat". In addition, the potential negative impacts of "cultured meat" on traditional livestock farming, on the meat industry as well as on territories and rural life are among the concerns of respondents. Hence, for the above reasons, and also because of emotional resistance and negative feelings (disgust, absurdity), the majority of respondents do not support "cultured meat" as a reasonable solution for the future, despite some curiosity for the product. However, confirming some previous results, young people and women, more concerned about ethical and environmental issues, are more likely to accept "cultured meat".

Contrary to other assumptions (especially by the promoters of "cultured meat"), a better understanding of the advantages, limitations and production process of "cultured meat" does not necessarily favor its acceptance. On the contrary, better knowledge of the livestock farming systems, their environment and the conventional meat production process favors the rejection of "cultured meat".

Overall, a very large majority of respondents would not be prepared to pay more for "cultured meat" (if it was ever on the market) than conventional meat as previously observed in other studies. Furthermore, at best, around one fifth of respondents consider this alternative to conventional meat promising and/or feasible. An equivalent proportion of respondents believe that "cultured meat" has no future in France. Between these two extremes, a large proportion of unsure respondents can be influenced. It is therefore important to objectively inform consumers of the advantages/disadvantages of this new technology. Thus, even if "cultured meat" is one day marketed, its societal or environmental impacts would be very weak to moderate due to a low to moderate market share, and due to the fact that the production of conventional meat is not completely replaced by the production of "cultured meat".

Discussion

Cultured "meat", advocated as a novel sustainable food and an alternative to livestock meat, has become an emerging hot topic in both the scientific field and the press media (Chriki et al., 2020). Consumers would play a very important role in the development of the cultured "meat" market if this product gets an agreement to be commercialized. Furthermore, each consumer segment may have its own attitudes and perspectives towards cultured "meat", which are important for the willingness to try, to eat regularly and to pay. We found that for both French and Chinese respondents, demographic factors had impact on respondents' willingness to try, eat and pay for cultured "meat" but with some differences between consumer groups. Such as for Chinese respondents, older people, people work in the meat sector, daily meat eaters would be more willing to try cultured "meat". On the opposite, for French respondents, young people, people work outside the meat sector, vegetarians and light meat eaters would be more willing to try. There are many factors that could explain these differences, such as the economic situation of each county, as well as the historical and cultural backgrounds. In general, respondents in both countries have not shown a strong willingness to accept cultured "meat". However, Chinese respondents had higher willingness with more positive attitudes to try, eat and pay for cultured "meat" than French respondents. Most of our Chinese respondents (85%) perceived cultured "meat" as "intriguing and/or promising". By contrast, 60% of our French respondents found cultured "meat" "absurd and/or disgusting". The most factors that concern French respondents were ethical and environmental issues, while, for Chinese respondents they were safety and overall meat quality (nutritional and sensorial quality). Unnaturalness was the most barrier for both Chinese and French respondents to try cultured "meat". Concerns about local territories and farms contribute to the reluctance of French respondents' willingness to try cultured "meat", but this is not the same case for Chinese respondents. We found that, in comparison with French consumers, Chinese consumers showed less concerns about animal welfare, environment protection and livestock farming systems. Other results generated from the same survey with respondents from other countries also shed light on understanding of consumer perception of cultured "meat". Brazil is one of the biggest meat production and consumption country in the world. Using the same survey translated into Portuguese, we found that around half of the Brazilian respondents thought cultured "meat" is "promising and/or acceptable" and would be willing to try. Nearly 40% of the Brazilian respondents had no willingness to eat cultured "meat" and 71% would pay less for cultured "meat" than for meat. Different consumer groups showed different acceptance of cultured "meat". In addition, some similar results were found between French respondents and Brazilian respondents, such as the respondents who were young, female and outside the meat sector had higher willingness to try and to eat cultured "meat" [START_REF] Chriki | Brazilian consumers 'attitude towards what is called « cellbased meat[END_REF]. The same results were also found with Italian, Portuguese and Spanish respondents: young people and people who work outside the meat sectors showed higher willingness to engage with cultured "meat" (Liu et al., manuscript in preparation). This is understandable due to their own social attribute characteristics, such as young people seem to be more open minded than other generations to some extent and people working in the meat sector are reluctant to accept this novel product, since they felt their own industry was threatened by the rise of this novel meat-like food [START_REF] Reis | Cell-Based Meat and Firms' Environmental Strategies: New Rationales as per Available Literature[END_REF]. In general, the social-demographic characteristics of the respondents have impacts on consumer attitudes and behaviors towards cultured "meat" and more generally, any purchase behavior. Nevertheless, these effects are variable. For instance, according to the previous studies, gender is considered as an important factor in association with different attitudes towards livestock meat and cultured "meat". It seems that females are more sensitive to issues concerning animal welfare and environment protection [START_REF] Ruby | Meat, morals, and masculinity[END_REF]) and hence would be more willing to accept cultured "meat", and this was observed with our French respondents. In one of our ongoing studies, concerning the problems caused by livestock productions, significant difference was noticed between female and male respondents in most of the continents/countries (i.e. Europe, Africa, America). On the opposite, no difference was found between Chinese female and male respondents regarding the same question (i.e. do you think livestock production cause important ethical/environmental problems?). Additionally, according to previous publications, sometimes, females were less willing to accept cultured "meat" since they were more sensitive towards uncertain scientific technologies and outcomes than males [START_REF] Treich | Cultured Meat: Promises and Challenges[END_REF]. It should be emphasized that consumer attitudes and behaviors are difficult to anticipate. This is one of the purposes of this international survey. By providing diverse observations among different consumer segments, more information on consumer perception and potential motivations towards the conventional meat industry and the growing industry of cultured "meat" can be disclosed based on interpretations in different food consumption contexts. Chapter 6. General discussion 6.1. The importance of developing a consumer qualitybased beef grading scheme in Europe Consumers' purchase willingness of beef is closely linked to the sensory properties of the beef product, with consistent eating quality being one of the most important attributes [START_REF] Mccarthy | Evaluation of beef eating quality by Irish consumers[END_REF]. At the moment of purchasing, consumers use mainly price, origin, appearance and visible fat to perceive beef quality [START_REF] Santos | The most important attributes of beef sensory quality and production variables that can affect it: A review[END_REF]. After consuming the beef product, if the real sensory experience is not as good as previously expected, this would result in dissatisfaction accompanied by a declined willingness of consumption. The complexity of guaranteeing beef eating quality and providing consistent palatability to consumers is one of the major challenges facing the beef industry (Hocquette et al., 2018). In agreement with European Commission regulations, in Europe, beef grading is based on production yield description through carcass weight, muscle shape and fat covering assessment according to the EUROP grid. However, without consideration of eating quality, the EUROP grid cannot provide effective guarantees for consumers, who are actually the end users throughout the supply chain. To ensure competitiveness of the European beef sector which faces a tough competition with imported beef from other animal origins and also with the booming emergency of meat alternatives, the improvement and guarantee of beef eating quality is becoming increasingly important. Beef eating quality is characterized by inherently variability and depends on many interacting factors that are difficult to handle together, such as antemortem factors including animal sex, age, maturity, breed, primal muscle properties (muscle, connective tissue and fat composition), and postmortem factors involving the slaughter process, carcass handling, aging, storage and cooking. It is therefore important to develop a grading scheme that could handle all factors that affect beef eating quality. Meanwhile, this grading scheme should be established on the consumer underpinned eating quality. As such, the real consumer demands on beef quality can be met.

The development of a consumer quality-based beef grading scheme in Europe according to the MSA specifications

There are several beef grading schemes (i.e. MSA, USDA, JMGA) around the world to evaluate beef eating quality. Their basic principles are to assign quality grades to a beef product in line with consumer underpinned eating quality or meat attributes (i.e. marbling). It has been shown that quality grade is a reliable predictor of expected quality in studies of consumer willingness to purchase. Indeed, [START_REF] Lyford | Is willingness to pay (WTP) for beef quality grades affected by consumer demographics and meat consumption preferences[END_REF] found that consumers from different countries (Japan, Australia, the United States and Ireland) would be willing to pay more for beef with a higher quality grade [START_REF] Lyford | Is willingness to pay (WTP) for beef quality grades affected by consumer demographics and meat consumption preferences[END_REF]. The MSA standard is considered as a better system due to its relevant accuracy to link the MSA quality grades with consistent eating quality of each cut to consumer real eating experience and satisfaction. Consumer underpinned quality grades are indeed more relevant than any other indirect and unique meat attribute such as marbling level only to differentiate quality grades. For instance, studies of consumer preference for beef eating quality based on the USDA quality grade have shown a less relevant relationship on the different USDA grades with consumer preference determined using the MSA grading scheme (Smith et al., 2008). Over the past two decades, independent and/or collaborative studies have concluded that this consumer-underpinned and quality-based beef grading scheme (the MSA grading system) is applicable in many countries such as Ireland, the United States, South Korea, Northern Ireland, Japan, France, Poland, South Africa and China. Therefore, the International Meat Research 3G Foundation (3G Foundation) was launched to encourage a worldwide collaborative meat research according to the MSA specifications throughout the beef supply chain. Thanks to the 3G Foundation platform, comprehensive data pooling and analysis has been performed to maximize research efficiency and to benefit the global beef industry (Hocquette et al., 2020). Within Europe, there are many different rearing and feeding systems, environmental conditions, animal types, breeds and processing practices that extend the variability of quality evaluation between countries. However, for scientific research on meat quality evaluation and for further industrial applications, there is a need to develop and/or share a set of generic principles (Hocquette et al., 2020). The development of an MSAlike grading scheme is a good option if different practices including breeding, production, pre-slaughter, processing and value-adding aspects across the European beef sector can be considered. Carcass grading is one critical step in the MSA system, since the carcass grading factors would be used as predictors of the eating quality score. For certain important traits such as marbling and rib fat thickness, the preferred grading site is at the ribeye muscle at the 10 th thoracis vertebrae in the Australian context. However, cutting at the 10 th rib would cause economic loss for the European meat plant since beef carcasses are traded in quarter, therefore the best grading site in the European context is at the 5 th rib. This problem related to marbling assessment has been solved in this thesis (Liu et al., 2021), since our study observed no significant difference in marbling score between the 5 th and the 10 th ribs and in the final prediction of eating quality taken into account marbling score from these two sites. However, other issues related to the practical application of the MSA principles in Europe still need to be fixed. Indeed, from a practical point of view, it is difficult to assess rib fat thickness at the 5 th rib (instead at the 10 th rib as in the MSA protocol), which may lead to less accurate measurement and therefore a poor prediction of eating quality. First, rib fat thickness is an important trait that is used in practice in Australia as a threshold to determine if the carcass could enter the MSA process grading and therefore reach an MSA quality grade (Polkinghorne et al., 2019). Second, if the assessment of rib fat thickness can be conducted at the quarter site, the value of fat thickness can be used together with other traits (such as marbling) in the prediction model of beef eating quality. Further work concerning these issues are required to estalish the applicability of the MSA standards in the European context. Otherwise, the MSA prediction model would be less accurate to predict eating quality for some animals which might be non-MSA-compliant in the European context. This is our case with the grass-fed Angus x Salers crossbred animals, the current MSA prediction model cannot accurately predict the eating quality scores derived from consumer tests. This might be a problem if a prediction model cannot provide accurate prediction due to different reasons such as the animals are non-compliant to the grading standard, or the animal breed is out of the range of the current MSA use. For a viable development of the MSA standards in Europe, carcass grading adjustments may be required. Therefore, further research should be developed on one hand, on specific crossbred animals including new genetic types and, on the other hand, on local livestock production systems for a more accurate and robust modelling system to predict beef eating quality.

European consumers perception of beef eating quality according to the MSA specifications

An accurate grading scheme for beef eating quality is paramount for consumer preference in order to enable value-based purchasing for consumers. The preference on eating quality attributes varies from countries, but tender and juicier meat is always preferred by consumers and the preference for flavor differs depending on different cultures [START_REF] Santos | The most important attributes of beef sensory quality and production variables that can affect it: A review[END_REF]. In the MSA system, beef eating quality is defined by four attributes: tenderness, juiciness, flavor liking and overall liking. Following the MSA consumer testing protocol, large-scale consumer tests have been conducted in different countries. Under the MSA protocol, the actual consumer response has been shown to be consistent with a high degree of accuracy when tasting samples with a wide range of quality variance. Therefore, the application of the MSA consumer testing is confirmed to be consistent in the European context. In addition to that, it is notable that in the MSA system, overall liking is used as an eating quality trait in parallel with tenderness, juiciness and flavor liking. It was well demonstrated by Watson et al. (2008) that the use of overall liking as a component is to improve the discriminatory efficiency and stability by smoothing the varying importance of the other three traits across the quality grades. In general, overall liking can be attributed to the other three traits instead of being used as an independent indicator of eating quality. Unlike a great deal of research on eating quality so far based on sensory evaluation by panelist, in the MSA system, a combined score (MQ4) is used to represent the overall eating quality, which is finally used to determine the MSA quality grades. The MQ4 score is determined by the combination of weighted tenderness, juiciness, flavor liking and overall liking. The current combination for MQ4 score is 0.3 tenderness + 0.1 juiciness + 0.3 flavor liking + 0.3 overall liking. The MQ4 weightings vary from countries, such as in France, with the animals that represent a common commercial production, the MQ4 weightings are 0.31, 0.04, 0.30 and 0.36 for tenderness, juiciness, flavor liking and overall liking (Legrand et al., 2013). In some other cases, the weighting of juiciness is close to 0 or even negative. This presentation raises the questions of the importance of juiciness for consumers. In fact, discussions about which trait is the most important for consumers to accurately perceive eating quality of beef have been lasted for many years. The main conclusion is that for consumers, flavor liking is becoming more and more important than tenderness. Based on the data collected from European consumer tests under the MSA protocol, overall liking was used as a dependent variable to distinguish the contributions of tenderness, juiciness and flavor liking. It was found that, for European consumers, flavor liking indeed contributes the most to overall liking, then followed by tenderness and juiciness. Notably, juiciness accounts 26% variability to overall liking. Therefore, tenderness, juiciness and flavor are all critical for the determination of beef eating quality. Based on this consensus, the proposal to include overall liking as a sensory trait then in combination with tenderness, juiciness and flavor liking to create an overall quality score -MQ4, is also relevant and applicable in the European context.

Beef quality from novel livestock systems -Adapting

the genetic type and mixing the species to strengthen their sustainability Novel livestock farming systems have emerged in response to the growing ecological concerns about sustainable agriculture and the increasing demand for meat of high nutritional and eating quality (Beudou et al., 2017;[START_REF] Chizallet | Supporting the design activity of farmers in transition to agroecology: Towards an understanding[END_REF][START_REF] De Souza | Agricultural labor in global value chains: a bibliometric review from Web of Science[END_REF][START_REF] Dumont | Prospects from agroecology and industrial ecology for animal production in the 21st century[END_REF]. These novel farming systems may be based for instance on animal crossbreeding between local breed and commercial breed, local feeding resources utilization (i.e. permanent pasture) and mixed species rearing for a more flexible management and a more adaptive, diverse and sustainable supply chain. For a better economic efficiency, especially for farmers and meat producers, local breeds were so far easily replaced by high-yielding commercial breeds with better performances of quality attributes. However, to some extent, crossbreeding may help animals to express their genetic potential in a better way for instance both with a better adaptation to diverse environments and with meat production of good eating quality [START_REF] Leroy | Review: Sustainability of crossbreeding in developing countries; definitely not like crossing a meadow[END_REF]. Proper implementation of crossbreeding can induce substantial increases in animal performances and in meat production, promote local ecosystem services and favor integration within a reliable market chain [START_REF] Leroy | Review: Sustainability of crossbreeding in developing countries; definitely not like crossing a meadow[END_REF]. As such, the sustainability of local livestock production can be enhanced. The merit of the crossbreeding strategy between an early-maturing breed (Angus) and a hardy breed (Salers) for a better adaption of mountainous environment and grass feeding system to produce beef of good quality has been confirmed. Mixing beef cattle and sheep within one grazing area had no adverse effect on bovine animal/carcass characteristics and meat quality, which means the mixed system is also beneficial regarding pasture utilization efficiency, parasite control and labor input. Therefore, novel livestock farming systems with agroecological benefits can be achieved by flexible crossbreeding and grass-based diets, as well as mixed species system. The crossbreeding between early-maturing and hardy breeds (Angus x Salers) within a grass feeding strategy is associated with a "better-than-average" eating quality, as well as with a low IMF content and a high ω-3 fatty acid content with a low ω-6/ω-3 ratio in beef. Generally speaking, the European Union promotes protection of agricultural traditions and production of high-quality food through official quality signs associated with origin, tradition or quality. The origin of food products is indeed important for European consumers. This was also observed in our survey about cultured "meat". Indeed, by comparisons with consumers from other continents, French consumers expressed higher attention and concern about whether the food is produced locally. Beef products in association with a local identity is favored by the local beef sector, livestock farmers and consumers. Although the Angus x Salers animals had a low commercial value regarding low carcass weight and meat yield, perhaps, consumers would still like to pay more for this beef product from local animal sources due to its good eating and nutritional quality.

Beef quality constructions from French signs and the

MSA specifications

Labels and quality signs contribute to the image value of food products, such as the Label Rouge in France. Label Rouge is an official regulated sign that represents a superior quality in terms of palatability and credence (Hocquette et al., 2014). Breed is one of the major criterions of the Label Rouge specifications, where the beef cattle breeds such as Limousin, Charolaise and Salers are recognized. On-farm productions including rearing and transporting practices, especially pasture grazing and/or grass feeding are highlighted with different rules in some Label Rouge specifications. For instance, 5 months of pasture or grazing may be compulsory. In addition, dried forage can be used but fermented forage and supplements are restricted (Raulet et al., 2021). In the beef sector, Or Rouge is a premium beef brand for Limousin breed. It commits to provide consumer beef products as tender, flavorful and healthy as possible. Through focusing both on intrinsic and extrinsic traits (thanks to the implementation of a relevant quality grading system, to the excellent image of the Limousin terroir and to pasture/grass feeding systems), Or Rouge would increasingly meet a wide range of expectations from consumers.

The superior and specific quality of Or Rouge beef product is associated to Limousin cows that are reared in the Limousine terroir. In addition to specific grazing and production practices, efficient processes (e.g. carcass handling and assessment) have been considered to further improve eating quality. This can be considered as a good progress by contrast with the main focus on production yield by the French beef industry. During the period of this thesis, around 1,000 carcasses have been graded according to the MSA specifications in the chiller of Beauvallet CV. Plainemaison, which is the company that launched the Or Rouge brand. Thanks to this work, on one hand, the relevance between high quality of beef and the assessment of marbling and color has been highlighted by this French meat company to further support the Or Rouge brand. On the other hand, it can be a win-win strategy for both the Or Rouge system and the MSA system. For the Or Rouge system, the guarantee of superior quality can be enhanced through the MSA consumer-focused quality control; for MSA, the combination of local-identified beef breed and specific on-farm production practices especially pasture-grazing and grass-feeding could expand the current MSA scope with additional image-value to be added. The improvement on both intrinsic qualityeating quality and extrinsic quality -image value would contribute both to consumer purchase willingness and the development of national/local labels based on a holistic approach from the farm to the plate. Some concerns have been raised with the implementation of the MSA system in France. For instance, the mixing of quality signs and labels would weaken the indication of one single brand and may cause confusion for consumers instead of providing clear guidelines to them. Also, it is difficult to completely implement the MSA system in the French context or to combine it with the Or Rouge specifications, since certain critical points involved in the MSA system are difficult to implement (such as measurement fat thickness at the 10 th -13 th ribs as in Australia) (Raulet et al., 2021). But the introduction of other critical points such as the assessment of marbling would be helpful for the French labels to endorse beef with superior quality and to exclude beef without superior quality. This is nowadays a key objective of the strategic plan of the French beef industry. In addition, the superior quality of Label Rouge meat over standard-commodity meat is occasionally determined through annual sensory testing, which might be not enough. For Label Rouge, a perspective could be to take the best of the MSA system such as the robust determination of meat sensory eating quality through sensory testing protocols well adapted for normal consumers. Since this untrained consumer focused system has been conducted more frequently and extensively throughout the world to enhance its quality control, it might be suitable also for the Label Rouge / Or Rouge systems to strengthen their superior quality determination and control.

6.6. For the improvement of sustainability, is the development of cultured "meat" a good strategy?

As consumer awareness of meat production continues to grow, consumer concerns about the sustainability of animal production is increasingly being considered by consumers in their purchase intention (Clinquart et al., 2022). Meat production can promote sustainability through providing employment [START_REF] Salmon | Exploring the landscape of livestock 'Facts[END_REF], good human nutrition [START_REF] Zaharia | Sustained intake of animal-sourced foods is associated with less stunting in young children[END_REF] and converting inedible plant residues into human foods [START_REF] Mottet | Global poultry production: current state and future outlook and challenges[END_REF]. Meanwhile, the sustainability of these different dimensions of meat production is undermining since highly advertised negative impacts of livestock on environmental footprints, human health, animal welfare and biodiversity loss [START_REF] Henry | The role of global dietary transitions for safeguarding biodiversity[END_REF][START_REF] Xu | Global greenhouse gas emissions from animal-based foods are twice those of plant-based foods[END_REF]. Therefore, reducing meat consumption is considered as a viable pathway to reduce the negative impacts of meat production on the global sustainability (environmental, economic, social, etc.) [START_REF] Poore | Reducing food's environmental impacts through producers and consumers[END_REF]. Reducing meat consumption could bring many advantages. By decreasing the great demand of meat consumption, by removing animals from the food production process, by decreasing intensive production practices and farming systems and animal slaughtering, animal suffering is supposed to be reduced. Nevertheless, with the increase of human population, the demand for meat will inevitably increase. According to some authors, the increasing demand for meat across the world makes it necessary to look for more meat alternatives, such as cell-based cultured "meat", which is currently hyped as a sustainable solution to mitigate the negative impacts of meat production on environment and climate, animal welfare and human health. However, some aspects concerning quality, health, environment and animal welfare still warrant further inspection holistically [START_REF] Parlasca | Meat Consumption and Sustainability[END_REF]. To date, the use of animal-derived component such as fetal bovine serum (FBS) for cultured "meat" production seems to be addressed, but the large-scale production of cultured "meat" without FBS still seems to be an issue. Although animal products are linked to deadly diseases to some extent and meat production brings bacterial pathogens to the food chain, the production of cultured "meat" is not sure can be fully safe due to the chance of antibiotics uses (Chriki & Hocquette, 2020). Nevertheless, cultured "meat" is still considered to be a long-term sustainable supply of meat and, most importantly, a solution to the environmental problems associated with livestock production, but skepticism still remains. For instance, in a long term, cultured "meat" would cause more warming with more CO2 emissions and accumulations than that of cattle beef production. In contrast, green gas emissions of ruminants are mainly composed of methane which does not accumulate in the atmosphere [START_REF] Lynch | Climate Impacts of Cultured Meat and Beef Cattle[END_REF]. Indeed, cultured "meat" has the potential to be environmentally friendlier than conventional farmed meat. However, given the complexity of cultured "meat" construction with different ingredients and processes, more robust and reliable data is still lacking up to now. The claimed advantages in environmental impacts are mainly derived from speculative production process rather than empirical applications [START_REF] Escobar | Analysis of the cultured meat production system in function of its environmental footprint: Current status, gaps and recommendations[END_REF][START_REF] Mancini | The future of cultured meat between sustainability expectations and socio-economic challenges[END_REF]. Furthermore, as part of the whole ecosystem, livestock system, including animal rearing and production, play an important role in agroecology and, in particular, in the maintenance of biodiversity and sustainability of all species (animals, plants, insects and micro-organisms). Reducing conventional meat production in favor, for instance, of cultured "meat" will reduce the positive impacts of livestock systems and thus reduce ecological sustainability. The high development of cultured "meat" could also bring negative impacts on the economical sustainability of agriculture-based developing countries, where livestock accounts for a big proportion of total agriculture GDP (gross domestic product) [START_REF] Treich | Cultured Meat: Promises and Challenges[END_REF]. In fact, whether cultured "meat" will be superior to conventional meat as regularly claimed depends largely on the use of animal-free culture and sustainable processing techniques as well as on rigorous scientific inspection concerning food security and environmental issues [START_REF] Smetana | Meat alternatives: life cycle assessment of most known meat substitutes[END_REF].

Consumer perception, willingness and acceptance of cultured "meat"

Cultured "meat" may come soon and is already on the plates of some consumers in Singapore. If the technical and institutional issues will be fixed, cultured "meat" may quickly become a reality [START_REF] Mancini | The future of cultured meat between sustainability expectations and socio-economic challenges[END_REF]. Guided by its stockholders and supporters, perhaps cultured "meat" will become politically correct in the future, for the sake of world hunger, public health, animal welfare and environmental sustainability. In the recent 5 years, numerical studies have explored consumers' perspective and acceptance towards cultured "meat", some representative results are summarized here. In general, ethics is a strong driver and unnaturalness, safety and healthiness and the negative emotions generated from neophobia are strong barriers of the acceptance of cultured "meat". Regarding consumer segments, on average, younger, more educated and more liberal people are more willing to accept cultured "meat" [START_REF] Treich | Cultured Meat: Promises and Challenges[END_REF]. However, different findings are also emerging, such as the one from our own research that young Chinese people had the least willingness to accept cultured "meat" compared to middle-aged and old people. For consumer studies, the representativeness of the participants should be firstly taken into account, and the different origins, cultures, backgrounds and socio-economic patterns of the studied consumers should be highly involved in the results interpretation. Consumers have commonalities in the issues they have concerns, albeit to different extent. It seems a good opportunity for the cultured "meat" industry, since as long as those problems will be resolved, consumers concerns would be largely alleviated. Nevertheless, in our studies, French respondents expressed ethical and environmental concerns more than Chinese respondents, their willingness to try and to eat cultured "meat" being lower than for Chinese respondents. This is likely because that most of the French respondents do not believe cultured "meat" could solve the ethical and environmental problems related to food. Food safety is the most issue that concerned Chinese respondents. However, in China, where food safety issues are quite serious, most of the Chinese respondents does not believe cultured "meat" could be safer than conventional meat. The perception of cultured "meat" varies between different consumer segments. Those different attitudes would lead to different levels of acceptability. Consumers' attitudes and behaviors are tricky to be anticipated. Even the fact that cultured "meat" has been highly advertised for its potential benefits in comparison with conventional livestock meat production, it was demonstrated that providing information is not always efficient to motivate consumers to pay more for sustainably produced meat and even meat substitutes [START_REF] Katare | Consumer willingness to pay for environmentally sustainable meat and a plant-based meat substitute[END_REF]. Therefore, it is necessary to understand consumers' attitudes, perspectives, potential acceptance, and willingness to engage with cultured "meat", and to provide different references to the market according to the technological updates of cultured "meat".

The development of beef industry from the consumer perspective

As the end user of food supply chain, consumers are the most important driver for food purchase. In the conventional meat sector, a substantial investment in efforts and innovations to improve meat quality for consumers should be achieved. In addition, conventional meat production has been criticized due to its negative impacts on animal welfare, environment and human health (Dumont et al., 2018;[START_REF] Lynch | Climate Impacts of Cultured Meat and Beef Cattle[END_REF]. Those factors are considered as extrinsic quality traits of meat products and its importance is increasing for consumers nowadays. Meanwhile, plenty of meat alternatives are emerging with the purpose to satisfy more consumers who care about more quality traits than only eating quality by considering adding values with better extrinsic quality. Consumers are willing to pay a premium for meat products if a premium quality can be guaranteed, this is the target of current labels and quality signs. In the MSA system, consumers do display willingness to pay more for superior sensory quality (Bonny et al., 2018). Consumers also have higher willingness to pay for meat production in association with animal welfare and environmental sustainability [START_REF] Edenbrandt | Is food labelling effective in reducing climate impact by encouraging the substitution of protein sources?[END_REF]. Cultured "meat" is well known to be animal free with less environmental impacts (but this need to be confirmed scientifically). Although this is the main concept of this novel product, consumers do not seem to be convinced at all. In most cases, consumers would be willing to try but the majority of them would only be willing to pay less, much less, even nothing at all for cultured "meat" in comparison with livestock meat products. Although consumers agree with the fact that plant-based substitutes are better for environmental sustainability, in reality, only few people are willing to consume plant-based substitutes on a long-term base. It was found in a choice preference study, most of the participants still preferred farm-raised beef rather than plant-based beef [START_REF] Escribano | Stated preferences for plant-based and cultured meat: A choice experiment study of spanish consumers[END_REF]. This can be the same for cultured "meat". Although positive information in favor of cultured "meat" increases its acceptance (Rolland et al., 2020), consumers might still prefer farm-raised meat. However, all our surveys showed that consumers disagree to pay more for cultured "meat". This is in line with the perceptions by consumers who do not trust that startups would produce cultured "meat" of good eating quality and in a sustainable way. Therefore, it is pivotal importance for the livestock meat industry to improve both sensory quality and extrinsic quality with a sustainable production process, and also to provide consumers with accurate quality description and/or ontology. In the end, cultured "meat" can be perceived as a commodity product and beef from specific production process would be considered more and more as premium products [START_REF] Bonny | What is artificial meat and what does it mean for the future of the meat industry[END_REF]. Accurate beef grading schemes like MSA and sustainable livestock farming systems could contribute to the achievement of the livestock beef products to be premium products. Consumers would agree to pay more with improved beef quality, and the competitiveness and success of the conventional meat industry can therefore be enhanced.

Figure 1 .Figure 2 . 1 Figure 1 . 2 Figure 1 . 3 Figure 1 .Figure 2 . 4 Figure 1 . 5 Figure 1 .Figure 2 .Figure 3 . 6 Figure 1 .Figure 2 .Figure 3 .Figure 4 .Figure 5 .Figure 6 .Figure 7 .Figure 8 .Figure 9 .Figure 10 . 7 Figure 1 .Figure 2 .Figure 3 .

 1211213124151236123456789107123 Figure 1. The formation of consumer beef quality perception……………………………………6 Figure 2. Structure of the international meat research 3G foundation…………………………38 Publication 1 Figure 1. Variables correlation plot on the first and second components of the PCA …………67 Publication 2 Figure 1. Scatterplots of the Meat Standards Australia (MSA) index and meat quality (MQ4) scores with inputs of marbling scores from the fifth rib (x-axis) or the tenth rib (y-axis)………77 Publication 3 Figure 1. Scatterplot matrix of variables densities (diagonal), correlations (above the diagonal), relationship between each pair of variables (under the diagonal)………………………………85 Figure 2. Relative weights of tenderness, juiciness, flavor liking to overall liking………………86 Publication 4 Figure 1. (A) Principal component analysis (PCA) and (B) Pearson correlation matrix with hierarchical cluster analysis (HCA) of animal and carcass characteristics and meat eating quality (striploin, 14-day-aged, MSA grilled)…………………………………………105 Publication 5 Figure 1. The two methods used for slicing internal flank plate…………………………………118 Figure 2. Distribution of MQ4 score in MSA quality grades……………………………………122 Figure 3. Variance analysis of mean values of sensory scores among three muscle cuts……128 Publication 6 Figure 1. Briefing provided to respondents regarding the introduction of artificial meat………140 Figure 2. General opinion of the respondents about artificial meat……………………………143 Figure 3. Word cloud plot on the reasons for willingness to try (WTT), no willingness to try (NO WTT) and the expectations from artificial meat…………………………………………………148 Figure 4. Mosaic plot of the distribution of responses about willingness to pay (WTP) according to age and familiarity………………………………………………………………………………151 Figure 5. Multiple components analysis: (a) the relationships between societal challenges and willingness to try (WTT) artificial meat; (b) the relationships between societal challenges and willingness to eat (WTE) artificial meat……………………………………………………………152 Figure 6. Multiple components analysis: (a) the relationships between perceptions about artificial meat characteristics and willingness to try (WTT); (b) the relationships between perceptions about artificial meat characteristics and willingness to eat (WTE) artificial meat…………………………………………………………………………………………………153 Figure 7. Cross analysis between food purchase criteria and respondents willingness to try (WTT)…………………………………………………………………………………………………154 Figure 8. Cross analysis between willingness to try (WTT) and willingness to eat regularly (WTE)………………………………………………………………………………………………155 Figure 9. Cross analysis between willingness to try (WTT), willingness to eat regularly (WTE) and accepting artificial meat as an alternative……………………………………………………155 Figure 10. Cross analysis between respondents' perspective and willingness to eat regularly (WTE)………………………………………………………………………………………………156

Figure 4 .

 4 Figure 4. Reasons not to try "cultured meat" also called "artificial meat" (A), expectations (B)

Figure 1 .

 1 Figure 1. The formation of consumer beef quality perception

Figure 2 .

 2 Figure 2. Structure of the international meat research 3G foundation

Fig. 1 .

 1 Fig. 1. Variables correlation plot on the first and second components of the PCA. EUFat = European fat score, EUConf = European conformation score, OSS = Ossification score, RiBFat = Rib fat thickness, USAMB = USA marbling score, CarcWt = carcass weight.

a

  European conformation score converted from P (-/=/+), O (-/=/+), R (-/=/+), U (-/=/+), and E (-/=/+) to 1-15; b European fat score converted from 1 (-/=/+), 2 (-/=/+), 3 (-/=/+), 4 (-/=/+), and 5 (-/=/+) to1-15.

FIGURE 1 |

 1 FIGURE 1 | Scatterplots of the Meat Standards Australia (MSA) index and meat quality (MQ4) scores with inputs of marbling scores from the fifth rib (x-axis) or the tenth rib (y-axis). CUB045, M. longissimus thoracis; STA045, M. longissimus thoracis et lumborum, anterior striploin piece; STP045, M. longissimus thoracis et lumborum, posterior striploin piece; AT, hang method-Achilles tendon; TX, hang method: tenderstretch; 10 days, 10 days aging; 20 days, 20 days aging; GRL, cooking method-grill; and RST, cooking method-roast.

Frontiers

  in Veterinary Science | www.frontiersin.org 6 March 2021 | Volume 8 | Article 611153

  . Besides, a https://doi.org/10.1016/j.meatsci.2020.108190 Received 20 February 2020; Received in revised form 27 April 2020 ⁎ Corresponding author. E-mail address: jean-francois.hocquette@inrae.fr (J.-F. Hocquette). Meat Science 168 (2020) 108190 Available online 19 May 2020 0309-1740/ © 2020 Elsevier Ltd. All rights reserved.

Fig. 1 .

 1 Fig. 1. Scatterplot matrix of variables densities (diagonal), correlations (above the diagonal), relationship between each pair of variables (under the diagonal). T = Tenderness; J = Juiciness; FL = Flavor liking; OL = Overall Liking. *** indicate that correlation is significant different at the 0.001 level (P < .001).

  multiple linear regression (model 4), overall liking can be explained by the following equation: overall liking = -0.8 + 0.3 Tenderness +0.1 Juiciness +0.7 Flavor liking (R 2 = 94%). Furthermore, a standardized model (model 5) was obtained when the intercept was adjusted into zero, in this way, it was much easier to compare the effect of each sensory trait to overall liking. In this case, overall liking can be explained by the new equation: overall liking = 0.3 Tenderness +0.1 Juiciness +0.6 Flavor liking (R 2 = 94%), It means that 1 unit change in flavor liking or 2 units change in tenderness or 6 units change in juiciness have the same impact on overall liking. An approximately identical conclusion was obtained in a Polish consumer study where the three-variable discriminant function was used to weight the importance of tenderness, juiciness and flavor liking by following the MSA and MQ4 calculation methodology

Fig. 2 .

 2 Fig. 2. Relative weights of tenderness, juiciness, flavor liking to overall liking.

  1* + 0.4*** × T 1 + 0.1*** × J 2 + 0.6*** × F 3 7 0.79 T: 37-52 -0.2 + 0.2*** × T + 0.1*** × J + 0.7*** × F 8 0.74 T: 52-66 -0.9 + 0.2*** × T + 0.1*** × J + 0.7*** × F 9 0.74 T: 66-97 -1.8* + 0.2*** × T + 0.1*** × J + 0.8*** × 05, ⁎⁎ P < .01, ⁎⁎⁎ P < .001.

Foods

Foods

Foods 2022 , 17 Figure 1 .

 2022171 Figure 1. (A) Principal component analysis (PCA) and (B) Pearson correlation matrix with hierarchical cluster analysis (HCA) of animal and carcass characteristics and meat eating quality (14-day-aged striploin). *: p < 0.05; **: p < 0.01; ***: p < 0.001.

Figure 1 .

 1 Figure 1. (A) Principal component analysis (PCA) and (B) Pearson correlation matrix with hierarchical cluster analysis (HCA) of animal and carcass characteristics and meat eating quality (14-day-aged striploin). *: p < 0.05; **: p < 0.01; ***: p < 0.001.

Figure 1 .

 1 Figure 1. The two methods used for slicing internal flank plate French slice Ⅰ: French way of slicing muscles (along muscle fibers) into 75 × 60 × 25 mm steaks and grilling on the 75 x 60 mm side; French slice Ⅱ: the normal way of slicing muscles (across the grain) into 75 x 25 x 25 mm steaks and grilling on the 75 x 25 mm thin side

Figure 2 .

 2 Figure 2. Distribution of MQ4 score in MSA quality grades Note: X-axis: MQ4 score is a combined score to indicate overall eating quality and calculated by MQ4 = 0.36*tn -0.02*ju + 0.29*fl + 0.37*ov. Y-axis: density -proportion of samples assigned in different quality grades; frequency -number of samples assigned in different quality grades. The boundaries shown by orange lines are the points of intersection between the distribution curves, which were the optimal points at which consumers distinguished between the grades of 2/3 star, 3/4 star and 4/5 star.

Figure 3 .

 3 Figure 3. Variance analysis of mean values of sensory scores among three muscle cuts BLD096: m. triceps brachii caput longum; STP045: m. longissimus dorsi et thoracis; TFL064: m. rectus abdominis; Tender: tenderness; Juicy: juiciness; Flavour: flavor liking; Overall: overall liking; ns P > .05, *P < .05, **P < .01, ***P < .001. The three muscle cuts (n=31 for each cut) were 14 days aged and grill cooked to rare degree of doneness. The grey line connecting the three different colored dots indicates that the scores on three muscle cuts are all collected from one animal. Table6presents the variance analyses of consumer sensory scores of BLD096 at the different ageing times (5 and 14 days). On average, no significant differences of consumer sensory scores were observed between 5 days and 14 days of ageing (P>0.05). However, with increasing ageing time, sensory scores at 14 days are about 2 points higher than those at 5 days, but this is non-significant on a statistical point of view. Moreover, the MSA model predicts no effect of aging between 5 and 14 days for the BLD096 and TFL064 (stir fry); 2-3 points for the STR045 depending on whether anterior of posterior. Whilst no difference was observed, the current finding is still consistent with other studies showing that ageing muscles from 5 to 21 days increases the sensory score of the striploin by up to 4 palatability units(Bonny et al., 2018).

Foods

Foods 2021 ,

 2021 10, x FOR PEER REVIEW 4 of 27

Figure 1 .

 1 Figure 1. Briefing provided to respondents regarding the introduction of artificial meat.

Figure 1 .

 1 Figure 1. Briefing provided to respondents regarding the introduction of artificial meat.

Figure 2 .

 2 Figure 2. General opinion of the respondents about artificial meat.

Figure 2 .

 2 Figure 2. General opinion of the respondents about artificial meat.

Figure 3 .

 3 Figure 3. Word cloud plot on the reasons for willingness to try (WTT), no willingness to try (NO WTT) and the expectations from artificial meat.

  1.

Figure 3 .

 3 Figure 3. Word cloud plot on the reasons for willingness to try (WTT), no willingness to try (NO WTT) and the expectations from artificial meat.

Foods 2021 ,

 2021 10, x FOR PEER REVIEW 12 of 27

Figure 4 .

 4 Figure 4. Mosaic plot of the distribution of responses about willingness to pay (WTP) according to age and familiarity.

- 30 /

 30 No (1.89 a ) > 31-50/Yes (1.74 b ); 18-30/Yes (1.73 b ); 51-/No (1.7 b ); 31-50/No (1.7 b ) > 51-/Yes (1.59

Figure 4 .

 4 Figure 4. Mosaic plot of the distribution of responses about willingness to pay (WTP) according to age and familiarity.

  -30/No (1.89 a ) > 31-50/Yes (1.74 b ); 18-30/Yes (1.73 b ); 51-/No (1.7 b ); 31-50/No (1.7 b ) > 51-/Yes (1.59

Figure 5 .

 5 Figure 5. Multiple components analysis: (a) the relationships between societal challenges and willingness to try (WTT) artificial meat; (b) the relationships between societal challenges and willingness to eat (WTE) artificial meat. WTE: willing to eat regularly; No WTE: no willing to eat regularly; WTT: willing to try; No WTT: no willing to try; AM: artificial meat; CM: conventional meat.

Figure 5 .

 5 Figure 5. Multiple components analysis: (a) the relationships between societal challenges and willingness to try (WTT) artificial meat; (b) the relationships between societal challenges and willingness to eat (WTE) artificial meat. WTE: willing to eat regularly; No WTE: no willing to eat regularly; WTT: willing to try; No WTT: no willing to try; AM: artificial meat; CM: conventional meat.

FoodsFigure 6 .

 6 Figure 6. Multiple components analysis: (a) the relationships between perceptions about artificial meat characteristics and willingness to try (WTT); (b) the relationships between perceptions about artificial meat characteristics and willingness to eat (WTE) artificial meat. WTE: willing to eat regularly; No WTE: no willing to eat regularly; WTT: willing to try; No WTT: no willing to try; WTP: willing to pay the same as conventional meat; WTP more: willing to pay more than conventional meat; WTP less: willing to pay less than conventional meat; AM: artificial meat; CM: conventional meat.

Figure 6 .

 6 Figure 6. Multiple components analysis: (a) the relationships between perceptions about artificial meat characteristics and willingness to try (WTT); (b) the relationships between perceptions about artificial meat characteristics and willingness to eat (WTE) artificial meat. WTE: willing to eat regularly; No WTE: no willing to eat regularly; WTT: willing to try; No WTT: no willing to try; WTP: willing to pay the same as conventional meat; WTP more: willing to pay more than conventional meat; WTP less: willing to pay less than conventional meat; AM: artificial meat; CM: conventional meat.

3. 4 .

 4 Relationships among Respondents' Attitudes, Perspective, Acceptance and Willingness to Try and to Eat Regularly

Foods 2021 ,

 2021 10, x FOR PEER REVIEW 15 of 27

Figure 7 .

 7 Figure 7. Cross analysis between food purchase criteria and respondents' willingness to try (WTT). Note: the number in the parentheses is the number of respondents who choose the corresponding option. WTT: willing to try; WTT--: definitely no WTT; WTT-: probably no WTT; Unsure: unsure to try; WTT+: probably WTT; WTT++: definitely WTT.

Figure 7 .

 7 Figure 7. Cross analysis between food purchase criteria and respondents' willingness to try (WTT). Note: the number in the parentheses is the number of respondents who choose the corresponding option. WTT: willing to try; WTT--: definitely no WTT; WTT-: probably no WTT; Unsure: unsure to try; WTT+: probably WTT; WTT++: definitely WTT.

Foods 2021 , 27 Figure 8 .

 2021278 Figure 8. Cross analysis between willingness to try (WTT) and willingness to eat regularly (WTE). WTT: willing to try; No WTE: no willing to eat regularly

Figure 9 .

 9 Figure 9. Cross analysis between willingness to try (WTT), willingness to eat regularly (WTE) and accepting artificial meat as an alternative. WTT: willing to try; No WTT: no willing to try; WTE: willing to eat regularly; No WTE: no willing to eat regularly.

Figure 8 .

 8 Figure 8. Cross analysis between willingness to try (WTT) and willingness to eat regularly (WTE). WTT: willing to try; No WTE: no willing to eat regularly.

Figure 8 .

 8 Figure 8. Cross analysis between willingness to try (WTT) and willingness to eat regularly (WTE). WTT: willing to try; No WTE: no willing to eat regularly

Figure 9 .

 9 Figure 9. Cross analysis between willingness to try (WTT), willingness to eat regularly (WTE) and accepting artificial meat as an alternative. WTT: willing to try; No WTT: no willing to try; WTE: willing to eat regularly; No WTE: no willing to eat regularly.

Figure 9 .

 9 Figure 9. Cross analysis between willingness to try (WTT), willingness to eat regularly (WTE) and accepting artificial meat as an alternative. WTT: willing to try; No WTT: no willing to try; WTE: willing to eat regularly; No WTE: no willing to eat regularly.

Figure 10 .

 10 Figure 10. Cross analysis between respondents' perspective and willingness to eat regularly (WTE). No WTE: no willing to eat regularly 4. Discussion 4.1. Chinese Consumers Are Not Eager for Artificial Meat

Figure 10 .

 10 Figure 10. Cross analysis between respondents' perspective and willingness to eat regularly (WTE).No WTE: no willing to eat regularly.

  Consumers Are Not Eager for Artificial Meat

Part 1 :

 1 Introduction and context of the survey. -Part 2: Demographic information, with 7 personal questions to characterize the respondents, including gender, age, income, sector of activity and diet, -Part 3: Preamble of 2 questions, "Have you ever heard of 'artificial meat' before?" and "What are the most important criteria when you do your food shopping?" -Part 4: Societal challenges, with 7 questions about societal issues, including two starting with "To what extent do you think on-farm breeding and the meat industry pose important problems" and relating to ethical or environmental problems (respondents were asked to answer on a scale of 1 to 5, expressing their disagreement (lower scores) or agreement (higher scores), -Part 5: Characteristics of the product, with 2 questions on expected or assumed characteristics of the product, -Part 6: Potential interest with 4 questions. -Part 7: Perception. -and finally Part 8: Development strategies, with 2 questions on the "artificial meat" development strategy.

Fig. 1 .

 1 Fig. 1. General opinion of respondents on "cultured meat" also called "artificial meat" (N = 5418 responses).

Fig. 2 .

 2 Fig. 2. Principal component analysis showing relationships between the studied variables corresponding to the main questions with potential answers from 1 to 5. AM: "Artificial Meat". WTT: Willingness to try. WTE: Willingness to eat. WTP: Willingness to pay.

Fig. 4 .

 4 Fig. 4.Reasons not to try "cultured meat" also called "artificial meat" (A), expectations (B) and reasons to try "cultured meat" (C). Results are expressed as a percentage of answers (N = 5418 responses). As these are multiple choices questions, the sum of the percentages is greater than 100.

Table 1 .

 1 Factors affecting the major quality attributes of beef at various stages of the farm-to-

	fork continuum………………………………………………………………………………………21

Table 2 .

 2 Beef quality grading systems around the world …………………………………………30

	Publication 1

Table 1 .

 1 Number of carcasses from young bulls (B), females (F) and steers (S) for European conformation and fat scores …………………………………………………………………………65

Table 2 .

 2 Carcass traits of young females, old females, steers and young bulls………………65

Table 3 .

 3 Correlation coefficients between marbling and Australian indicators and European classification scores…………………………………………………………………………………65

Table 4 .

 4 Multiple regression equations for indicating marbling using European classification and other assessment indicators……………………………………………………………………67

Table 5 .

 5 Mean-square prediction error for observed marbling and predicted marbling scores..68

	Publication 2

Table 1 .

 1 Number, mean, SD, minimum, and maximum values for the basic carcass traits.……74

Table 2 .

 2 Number, mean, minimum, maximum scores, SEM of marbling, meat color, and fat

Table 10 .

 10 Contributions of tenderness, juiciness and flavor liking to overall liking depending on muscle cuts for all the cooking methods……………………………………………………………90

	XXII

Table 1 .

 1 Unadjusted mean, standard deviation, minimum and maximum values for animal age, carcass traits, meat pH, color, lipid content, FA content and FA index…………………………..99

Table 2 .

 2 Estimated marginal means of animal and carcass characteristics as affected by animal gender and farming system………………………………………………………………………102

Table 3 .

 3 Estimated marginal means of FA content, antioxidant content, vitamin content as affected by animal gender and farming system …………………………………………………103

Table 4 .

 4 Estimated marginal means of eating quality scores evaluated by untrained consumers and trained panelists and WBSF as affected by animal gender and farming system…………104

Table 5 .

 5 Distribution of beef samples to different MSA quality grades by consumers based on the present database versus the European database utilized…………………………………106

Table 6 .

 6 Consumer scores on meat eating quality traits based on the present database versus the European database utilized ……………………………………………………………………106

	Publication 5

Table 1 .

 1 Mean, standard deviation (SD), minimum and maximum values for animal age and carcass characteristics……………………………………………………………………………117

Table

Table 1 .

 1 Factors affecting the major quality attributes of beef at various stages of the farm-to-fork continuum.

	Stage	Factors	Beef Quality Attributes
			Intrinsic Quality	Extrinsic Quality

Appearance Sensory Nutritional Safety Technological Convenience Image Commercial

		Breed/Genetics
	Animal	Gender
		Age
	Farming	Feeding-grass/grain Grazing-indoor/outdoor
	Pre-slaughter Transport/load/mix/rest-stress
		Slaughter practices
	Slaughter	Hygiene
		Animal welfare
	Carcass handling	Electrical stimulation Aging time/temperature Hanging
		Carcass weight
		Conformation
		Marbling
	Carcass	Maturity/ossification
	characteristic	Rib fat thickness/fat cover
		Hump height
		Temperature/pH
		Meat color/fat color
		Muscle cut
	Meat	Nutrients: proteins/FA/minerals
		and vitamins
		Packaging/portioning/shelf life
	Meat products	Brand/origin/label/grade and traceability Ethical and environmental
		sustainability
	Processing	Storage Cooking/smoking/fermentation

Table 2 .

 2 Beef quality grading systems around the world

	Grading	Description	Trait			Current scope
	scheme
	Carcass-based grading and classification
	European	Animal type	Sex			Young bull, bull, steer, heifer, and
	grid					cow
	(EUROP)	Yield grade	Conformation score	5-point scale: E, U, R, O, P
						Subgrade: E+, E=, E-, U+, U=, U-,
						R+, R=, R-, O+, O=, O-, P+, P=, P-
			Fat score			5-point scale: 1-5; Subgrade: 1-15
	Japan	Quality grade Marbling, meat color,	5-point scale: 1-5
	carcass		brightness,	fat	color,
	grading		lustre;	texture/firmness
	system		and rib fat thickness
	(JMGA)	Yield grade	Carcass weight and eye	A, B, C
			muscle area
	South Africa	Quality grade Age (dentition)		A, AB, B, C: 0~>6 permanent
	National					incisors
	beef grading	Yield grade	Conformation		1-5: very flat-very round
			Fat cover			0-6: no visible fat-excessively
						overfat
			Damage			1-3: slight-severe damage
	South Korea	Quality grade Marbling, meat color, fat	5 Quality grades
	grading		color, firmness of rib eye	1++, 1+, 1, 2, 3
	system		and maturity
	Canadian	Quality grade Marbling,		carcass	5 Quality grades
	beef grading		maturity,		back-fat	Subgrade: A, AA, AAA, Prime, B1-
	system		thickness, muscling, meat	B4, D1-D4, E
			and fat color
	MLC	Quality grade Age, sex, growth rate; diet;	Pass/fail quality grades
	Blueprints		EUROP conformation and
	UK		fat class; transport and
			lairage handling; slaughter
			techniques,	defects;
			hanging;		electrical
			stimulation,	chilling
			temperature	and	pH;
			maturation
	Red Tractor	Quality grade Age,	sex,	EUROP	Pass/fail quality grades
	and Quality		conformation and fat class;
	Standard		maturation
	Marks
	UK

Table 1

 1 Number of carcasses from young bulls (B), females (F) and steers (S) for European conformation and fat scores.

	European conformation

a Numerical values on a 15-point scale in European conformation class. b Numerical values on a 15-point scale in European fat class.

Table 2

 2 Carcass traits of young females, old females, steers and young bulls.

		Young females	Old females		Steers		Young bulls
		n	Mean	n	Mean	n	Mean	n	Mean	SEM	P value
	Marbling score	175	329.77 b	47	396.06 a	320	339.19 b	168	293.51 c	15.18	< 0.001
	Rib fat thickness (mm)	129	7.04 a	45	5.27 b	274	6.96 a	121	4.43 b	0.56	< 0.001
	EU Fat score	155	8.53 a	46	7.89 b	276	8.09 ab	167	6.67 c	0.27	< 0.001
	EU Conformation score	154	7.71 a	46	6.37 b	276	6.78 b	167	6.86 b	0.38	0.001
	Carcass weight (kg)	174	305.26 b	47	345.53 a	320	343.46 a	167	347.33 a	8.06	< 0.001
	Ossification score	129	181.3 b	47	455.3 a	274	149.6 c	121	178.4 b	6.8	< 0.001
	Age (days)	175	706.71 b	44	2835 a	276	784.13 b	166	566.47 c	45.78	< 0.001

a,b,c Within a row means with the different letter are significantly different (P < .05).

Table 3

 3 Correlation coefficients between marbling and Australian indicators and European classification scores.

			Marbling	EUConf a	EUFat b	Rib fat	Weight	Ossification	Age
	EUConf a	All	-0.16***	1.00
		Young females	0.20*	1.00
		Old females	-0.46***	1.00
		Steers	-0.10 NS	1.00
		Young bulls	-0.27***	1.00
	EUFat b	All	0.45***	0.05 NS	1.00
		Young females	0.34***	0.09 NS	1.00
		Old females	0.45**	-0.23 NS	1.00
		Steers	0.59***	0.07 NS	1.00
		Young bulls	0.36***	0.01 NS	1.00
	Rib fat	All	0.26***	0.12***	0.51***	1.00
		Young females	0.33***	0.09 NS	0.50***	1.00
		Old females	0.47**	-0.14 NS	0.34*	1.00
		Steers	0.25**	-0.03 NS	0.45***	1.00
		Young bulls	0.18**	0.14*	0.47***	1.00
	Weight	All	0.15***	0.60***	0.19***	0.18***	1.00
		Young females	0.31**	0.46***	0.22*	0.32***	1.00
		Old females	0.17 NS	0.19 NS	0.40**	0.25 NS	1.00
		Steers	0.38***	0.58***	0.43***	0.08 NS	1.00
		Young bulls	-0.07 NS	0.78***	0.21**	0.29***	1.00
	Ossification	All	0.25***	0.05 NS	0.07 NS	-0.06 NS	0.21***	1.00
		Young females	0.38***	0.15 NS	0.14 NS	0.05 NS	0.47***	1.00
		Old females	0.21 NS	-0.12 NS	-0.06 NS	0.20 NS	-0.24 NS	1.00
		Steers	0.35***	0.39***	0.33***	0.00 NS	0.68***	1.00
		Young bulls	-0.03 NS	0.30***	0.08 NS	0.09 NS	0.40***	1.00
	Age	All	0.21***	-0.16 NS	0.06 NS	-0.02 NS	0.10**	0.89***	1.00
		Young females	0.07 NS	-0.22*	-0.06 NS	-0.03 NS	0.14 NS	0.33***	1.00
		Old females	0.09 NS	0.08 NS	-0.11 NS	0.19 NS	-0.30 NS	0.75***	1.00
		Steers	0.20*	0.22*	0.02*	-0.35***	0.56***	0.51***	1.00
		Young bulls	-0.03 NS	0.46***	0.10 NS	0.06 NS	0.56***	0.42***	1.00

***, **, * indicate that correlation is significant different at the 0.001 level (P < .001), 0.01 level (P < .01) and 0.05 level (P < .05), NS means the correlation is non-significant (P > .05).

a EUConf = European conformation score b EUFat = European fat score.

Table 4

 4 Multiple regression equations for indicating marbling using European classification and other assessment indicators.

	Marbling	variables	Equation

Table 5

 5 Mean-square prediction error for observed marbling and predicted marbling scores.

		n	Mean O-marbling c	SD-O d	Mean P-marbling e	SD-P f	MSPE g	MPE h	R d
	Whole (Eq. 7)	497	328	114	327	65	8911	0.29	0.32
	Females (Eq. 8)	149	337	121	335	74	10,02	0.30	0.34
	Young females (Eq. 9)	109	303	99	303	53	8324	0.30	0.30
	Old females (Eq. 10)	44	336 b	137	430 a	70	23,35	0.45	0.39
	Steers (Eq. 11)	232	342	125	341	85	8179	0.26	0.21
	Young bulls (Eq. 12)	119	292	102	290	47	7667	0.30	0.46

TABLE 1 |

 1 Number, mean, SD, minimum, and maximum values for the basic carcass traits.

		n	Mean	SD	Min	Max
	Age (days)	204	3,458	1,835	228	7,422
	Carcass weight (kg)	204	356.5	95.8	126.8	729.7
	Ultimate pH	198	5.7	0.16	5.3	6.3
	Ossification score	185	480	160.8	100	590
	European conformation score a	204	8.7 (R =)	3.1	1	13
	European fat score b	204	7.0 (3-)	1.00	3	9

TABLE 2 |

 2 Number, mean, minimum, maximum scores, SEM of marbling, meat color, and fat color scores determined at the fifth and the tenth ribs.

			5 th rib				10 th rib
		n	Mean	Min	Max	n	Mean	Min	Max	SEM	P-value
	AUS MB 1	208	0.7	0	5	207	0.7	0	4	0.06	0.95
	MSA MB 2	208	288	100	750	207	291	100	680	7.2	0.73
	Meat color	208	2.5 b	1B	6	208	2.9 a	1B	7	0.07	0.003
	Fat color	197	2.5 b	0	6	196	3.5 a	0	9	0.11	<0.001
	MQ4 CUB045 3	164	58	46	72	164	58	46	69	0.39	0.97
	MQ4 STA045 4	164	52	40	67	164	52	40	65	0.42	0.95
	MQ4 STP045 5	164	50	37	66	164	50	37	63	0.44	0.94
	MQ4 OYS036 6	164	64	57	73	164	64	57	72	0.27	0.94
	MQ4 BLD096 7	164	48	37	61	164	48	37	58	0.39	0.98
	MQ4 RMP131 8	164	46	36	58	164	46	36	57	0.39	0.92
	MQ4 KNU066 9	164	41	31	53	164	41	31	51	0.37	0.97
	MQ4 OUT005 10	164	38	28	49	164	38	28	49	0.39	0.95
	MQ4 EYE075 11	164	37	25	54	164	37	25	51	0.47	0.99
	MQ4 CHK074 12	164	55	45	67	164	55	45	65	0.34	0.99
	MSA index	164	52	43	64	164	52	42	62	0.38	0.92

TABLE 3 |

 3 Correlation coefficients (r) among the assessment scores at the fifth rib and the tenth rib.

		AUSMB 1 10 th	MSAMB 2 5 th	MSAMB 10 th	MC 3 5 th	MC 10 th	FC 4 5 th	FC 10 th	OSS 5
	AUSMB 5 th	0.77***	0.88***	0.74***	0.06	0.16*	-0.1	-0.2**	0.21
	AUSMB 10 th	1	0.75***	0.91***	0.09	0.13	-0.14	-0.2**	0.18
	MSAMB 5 th		1	0.79***	0.02	0.21***	-0.14	-0.19**	0.31*
	MSAMB 10 th			1	0.06	0.21***	-0.15*	-0.19**	0.18
	MC 5 th				1	0.43***	0.1	0.06	0.1
	MC 10 th					1	0.1	0.13	0.03
	FC 5 th						1	0.7***	0.39***
	FC 10 th							1	0.36***

1

AUSMB, AUS-MEAT Marbling; 2 MSAMB, MSA Marbling; 3 MC, Meat Color; 4 FC, Fat Color; and 5 OSS, Ossification. ***, **, *indicate that correlation is significantly different at the 0.001 level (p < 0.001), 0.01 level (p < 0.01), and 0.05 level (p < 0.05).

TABLE 4 |

 4 Pearson correlations and average differences between the MQ4 scores predicted by the marbling score assessed at the fifth rib and the tenth rib.

	Cut		Cooking method: Grill			Cooking method: Roast
			Hang method: Achilles Tendon			Aging time: 10 days
			Aging time			Hang method
			10 days	20 days	Achilles Tendon	Tenderstretch
		Coefficient	Average	Coefficient	Average	Coefficient	Average	Coefficient	Average
			difference		difference		difference		difference
			(SD)		(SD)		(SD)		(SD)
	CUB045 1	0.91***	0.03 (2.05)	0.91***	0.03 (2.05)	0.91***	0.03 (2.05)	0.91***	0.03 (2.05)
	STA045 2	0.89***	0.07 (2.44)	0.89***	0.07 (2.44)	0.90***	0.07 (2.44)	0.89***	0.07 (2.44)
	STP045 3	0.88***	0.07 (2.67)	0.88***	0.07 (2.67)	0.89***	0.07 (2.67)	0.89***	0.07 (2.67)
	OYS036 4	0.97***	0.04 (0.77)	0.97***	0.04 (0.77)	0.97***	0.04 (0.77)	0.97***	0.04 (0.77)
	BLD096 5	0.97***	0.04 (1.15)	0.97***	0.04 (1.15)	0.97***	0.04 (1.15)	0.97***	0.04 (1.15)
	RMP131 6	0.98***	0.02 (0.76)	0.99***	0.02 (0.76)	0.99***	0.02 (0.76)	0.98***	0.02 (0.76)
	KNU066 7	0.97***	0.02 (1.14)	0.97***	0.02 (1.14)	0.97***	0.02 (1.14)	0.97***	0.02 (1.14)
	OUT005 8	0.98***	0.01 (0.98)	0.98***	0.01 (0.98)	0.98***	0.01 (0.98)	0.98***	0.01 (0.98)
	EYE075 9	0.98***	0.02 (1.14)	0.98***	0.02 (1.14)	0.98***	0.02 (1.14)	0.98***	0.02 (1.14)
	CHK074 10	0.94***	0.03 (1.52)	0.94***	0.03 (1.52)	0.93***	0.03 (1.52)	0.93***	0.03 (1.52)

1 CUB045, M. longissimus thoracis; 2 STA045, M. longissimus thoracis et lumborum. anterior striploin piece; 3 STP045, M. longissimus thoracis et lumborum. posterior striploin piece; 4 OYS036, M. infraspinatus; 5 BLD096, M. triceps brachii caput longum; 6 RMP131, M. gluteus medius; 7 KNU066, M. rectus femoris; 8 OUT005, M. biceps femoris; 9 EYE075, M. semitendinosus; and 10 CHK074, M. semispinalis capitis. ***p < 0.001. MQ4 scores are indicated on a scale from 0 to 100.

1

  CUB045, M. longissimus thoracis; 2 STA045, M. longissimus thoracis et lumborum, anterior striploin piece; 3 STP045, M. longissimus thoracis et lumborum, posterior striploin piece; 4 OYS036, M. infraspinatus; 5 BLD096, M. triceps brachii caput longum; 6 RMP131, M. gluteus medius; 7 KNU066, M. rectus femoris; 8 OUT005, M. biceps femoris; 9 EYE075, M. semitendinosus; and 10 CHK074, M. semispinalis capitis.

***p < 0.001.

3.3. Contributions of tenderness, juiciness and flavor liking to overall liking of beef in Europe This

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Assessing marbling scores of beef at the 10 th rib vs. 12 th rib of longissimus thoracis in the slaughter line using camera grading technology in Germany. Meat Sci. (2019) 152:116-20. doi: 10.1016/j.meatsci.2019.02.021 28. Bonny SPF, Pethick DW, Legrand I, Wierzbicki J, Allen P, Farmer LJ, et al. European conformation and fat scores have no relationship with eating quality. Animal. (2016) 10:996-1006. doi: 10.1017/S1751731115002839 29. Liu J, Chriki S, Ellies-Oury MP, Legrand I, Pogorzelski G, Wierzbicki J, et al. European conformation and fat scores of bovine carcasses are not good indicators of marbling. Meat Sci. (2020) 170:108233. doi: 10.1016/j.meatsci.2020.108233 30. Normand J. Institut de L'Elevage, Lyon. "En France, quelles pistes pour enrayer. la baisse de consommation de viande bovine ?" PowerPoint. (2017). http://idele.fr/fileadmin/medias/Documents/Perception_du_persille_et _du_conditionnement_sous_vide_-_Colloque_Interbev_2018-02-02.pdf (accessed February 2, 2017). 31. 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Pethick DW, Harper GS, Hocquette JF, Wang Y. Marbling biology-what do we know about getting fat into muscle. In: Proceedings of Australian Beef-the Leader. Armidale, NSW. (2006). p. 103-10. 37. Pflanzer SB, de FelícioPE. Moisture and fat content, marbling level and color of boneless rib cut from Nellore steers varying in maturity and fatness. Meat Sci. (2011) 87:7-11. doi: 10.1016/j.meatsci.2010.08.009 38. Malau-Aduli AEO, Edriss MA, Siebert BD, Bottema CDK, Pitchford WS. Breed differences and genetic parameters for melting point, marbling score and fatty acid composition of lot-fed cattle. J Anim Physiol Anim Nutr. (2000) 83:95-105. doi: 10.1046/j.1439-0396.2000.00254.x 39. Kern SA, Pritchard RH, Blair AD, Scramlin SM, Underwood KR. The influence of growth stage on carcass composition and factors associated with marbling development in beef cattle. J Anim Sci. (2014) 92:5275-84. doi: 10.2527/jas.2014-7891 40. Smith GC, Belk KE, Sofos JN, Tatum JD,Williams SN. 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(2000) 54:65-70. doi: 10.1016/S0309-1740(99)00072-8 45. McKenna DR, Mies PD, Baird BE, Pfeiffer KD, Ellebracht JW, Savell JW. Biochemical and physical factors affecting discoloration characteristics of 19 bovine muscles. Meat Sci. (2005) 70:665-82. doi: 10.1016/j.meatsci.2005.02.016 46. Totland GK, Kryvi H. Distribution patterns of muscle fibre types in major muscles of the bull (Bos taurus). Anat Embryol. (1991) 184:441-50. doi: 10.1007/BF01236050 47. Wicks J, Beline M, Gomez JFM, Luzardo S, Silva SL, Gerrard D. Muscle energy metabolism, growth, and meat quality in beef cattle. Agriculture. (2019) 9:195. doi: 10.3390/agriculture9090195 48. Dunne PG, Monahan FJ, O'Mara FP, Moloney AP. Colour of bovine subcutaneous adipose tissue: a review of contributory factors, associations with carcass and meat quality and its potential utility in authentication of dietary history. Meat Sci. (2009) 81:28-45. doi: 10.1016/j.meatsci.2008.06.013 49. Ardeshiri A, Rose JM. 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Table 2

 2 Relative importance and 95% CI attributes of sensory traits for overall liking. coefficient is probably due to the existence of multicollinearity (i.e. high correlations between variables). In that case, the coefficients of the model are very sensitive and hence unstable. In order to test the rationality of this speculation, an indicator called Variance Inflation Factors (VIF) was used to detect the multicollinearity: if VIF = 1, it means there is no correlation between predictors; if 1 < VIF < 5, it means there is moderately correlations; if VIF is greater than 10, it means the regression coefficients are poorly estimated and significance tests might be misleading due to multicollinearity

	Sensory trait	Relative importance	95% lower CI a	95% upper CI
	Flavor liking	39%	0.38	0.39
	Tenderness	31%	0.31	0.32
	Juiciness	24%	0.24	0.25

a CI, confidence interval. regression

Table 3

 3 multicollinearity test for multiple regression model of flavor liking, tenderness and juiciness to overall liking.

		Coefficient	Contribution	VIF a
	Flavor liking	0.6	39%	4.4
	Tenderness	0.3	31%	3.8
	Juiciness	0.1	24%	3.4

a VIF, Variance Inflation Factors.

Table 5

 5 Contributions of sensory traits to overall liking based on four levels of tenderness evaluation.

	Sensory traits Contribution to overall liking
		T a : 5-37	T: 37-52	T: 52-66	T: 66-97
		(No.7)	(No.8)	(No.9)	(No.10)
	Flavor liking	44%	52%	54%	50%
	Tenderness	22%	9%	7%	20%
	Juiciness	13%	14%	14%	17%

Table 9

 9 Consumer palatability scores of tenderness, juiciness, flavor liking and overall liking from eighteen muscles.

	Muscle	Cuts	n	Tenderness	Juiciness	Flavor liking	Overall liking
				LS Mean 19 ± SE	LS Mean ± SE	LS Mean ± SE	LS Mean ± SE
	TDR062	Tenderloin 1	443	80 ± 0.7 a	71 ± 0.6 a	75 ± 0.5 a	76 ± 0.6 a
	RMP231	Rump 2	471	62 ± 0.7 b	57 ± 0.6 b	60 ± 0.5 b	60 ± 0.5 b
	OYS036	Oyster blade 3	82	63 ± 1.6 b	68 ± 1.4 a	60 ± 1.2 bcd	60 ± 1.2 bcd
	RMP005	Rump 4	296	59 ± 0.9 b	57 ± 0.7 b	59 ± 0.6 bc	59 ± 0.7 bc
	STR045	Striploin 5	2711	56 ± 0.3 c	54 ± 0.2 c	57 ± 0.2 cd	56 ± 0.2 de
	KNU066	Knuckle 6	324	52 ± 0.8 d	54 ± 0.7 bc	56 ± 0.6 de	55 ± 0.6 ef
	BLD096	Blade 7	113	50 ± 1.4 de	53 ± 1.2 bcd	56 ± 1.0 bcde	55 ± 1.1 cdef
	RMP131	Rump 8	1056	54 ± 0.5 d	52 ± 0.4 d	55 ± 0.3 e	55 ± 0.4 f
	EYE075	Eye round 9	133	44 ± 1.3 e	46 ± 1.1 e	48 ± 1.0 f	47 ± 1.0 g
	TOP001	Topside 10	208	45 ± 1.0 e	41 ± 0.9 ef	47 ± 0.8 f	46 ± 0.8 g
	KNU099	Knuckle 11	81	35 ± 1.6 fg	43 ± 1.4 ef	48 ± 1.2 f	45 ± 1.3 g
	TOP073	Topside 12	1456	37 ± 0.4 f	43 ± 0.3 ef	47 ± 0.3 f	44 ± 0.3 g
	OUT005	Outside flat 13	701	30 ± 0.6 g	42 ± 0.5 f	43 ± 0.4 g	39 ± 0.4 h
	Note: Similar results were observed for another 5 cuts (CUB081 [Cube roll] 14 ; CUB045 [Cube roll] 15 ; CHK078 [Chuck] 16 ; RMP087 [Rump] 17 and CTR085 [Chuck
	tender]

18

) with a too small sample size to be included in the analysis. 1 m. psoas major; 2 m. gluteus medius; 3 m. infraspinatus; 4 m. biceps femoris; 5 m. longissimus dorsi et thoracis; 6 m. rectus femoris; 7 m. triceps brachii caput longum; 8 m. gluteus medius; 9 m. semitendinosus; 10 m. adductor femoris; 11 m. vastus lateralis; 12 m. semimembranosus; 13 m. biceps femoris; 14 m. spinalis dorsi; 15 m. longissimus lumborum; 16 m. serratus ventralis cervicis; 17 m. tensor fascia latae; 18 m. supraspinatus. 19 LS Mean: Least Squares Means.

Table 10

 10 Contributions of tenderness, juiciness and flavor liking to overall liking depending on muscle cuts for all the cooking methods.

		OUT005 (Outside flat)	TOP073 (Topside)	RMP131 (Rump)	STR045 (Striploin)	RMP231 (Rump)	TDR062 (Tenderloin)
	Flavor liking	39%	40%	40%	40%	39%	48%
	Tenderness	34%	27%	30%	29%	28%	27%
	Juiciness	15%	22%	23%	24%	26%	17%

Study 2 :

 2 French consumer evaluation of eating quality of Angus x Salers beef: effects of muscle cut, muscle slicing and ageing This study investigated the application of the MSA grading scheme with grass-fed Angus x Salers beef. Eating quality was evaluated by French consumers across different muscle cuts (m. triceps brachii caput longum, m. longissimus dorsi et thoracis and m. rectus abdominis), ageing time (5 and 14 days) and muscle slicing methods. The feasibility of using a combined score (MQ4) to indicate the overall meat eating quality in the French context was examined. The accuracy of the MQ4-based assignation of MSA quality grade and the differentiation of the various eating quality scores of different beef samples by French consumers were investigated. The application of the current MSA prediction model with Angus x Salers animals was thus demonstrated.

Table 1 .

 1 Unadjusted means, standard deviations, minimum and maximum values for animal age, carcass traits, meat pH, color, lipid content, FA content and FA index.

	Traits	Mean	SD	Min	Max
	Age (days)	422	16.8	386	458
	HCW (hot carcass weight, kg)	226.6	17.4	190.2	254.2
	CCW (cold carcass weight, kg)	226.0	17.5	190.2	254.2
	Hump height (cm)	3.5	0.7	2	5
	Ossification (100-590)	130	20.8	100	190
	Marbling (100-1190)	240	56.4	160	350
	pH	5.66	0.13	5.48	5.93
	EU conformation (1-15)	8	0.5	7	8
	EU fat score (1-15)	6	0.8	5	8
	Fat % HCW 1 (%)	15.35	1.34	12.90	18.50
	Muscle % HCW 2 (%)	66.57	1.38	63.80	69.10
	CIE L* (lightness)	31.32	2.07	26.39	36.94
	CIE a* (redness)	14.02	1.37	10.66	16.69
	CIE b* (yellowness)	14.66	1.42	11.10	17.34
	Total lipids 3	2.92	0.54	1.92	3.99
	C18:3n-3 4 (ALA 5 )	51.30	10.16	33.89	73.35
	C20:5n-3 (EPA 6 )	11.04	3.95	6.26	21.02
	C22:5n-3 (DPA 7 )	20.11	4.62	11.96	27.93
	C22:6n-3 (DHA 8 )	4.30	4.40	0.00	12.7
	PUFA/SFA ratio 9	0.23	0.05	0.14	0.37
	ω-6/ω-3 ratio 10	1.60	0.18	1.26	1.97

1

Fat % HCW: fat percentage based on hot carcass weight [fat weight (kg)/hot carcass weight (kg) %]; 2 Muscle % HCW: muscle percentage based on hot carcass weight [muscle weight (kg)/hot carcass weight (kg) %];

3

Total lipids unit: g/100 g muscle;

4

FAs [fatty acids] are expressed in content (mg/100 g muscle); 5 ALA: alpha-linolenic acid; 6 EPA: eicosapentaenoic acid; 7 DPA: docosapentaenoic acid; 8 DHA: docosahexaenoic acid;

Table 2 .

 2 Estimated marginal means of animal and carcass characteristics as affected by animal gender and farming system.

	System (S)		MONO		MIX		Significance of F-Value
	Gender (G)	Female (n = 10)	Male (n = 10)	Female (n = 6)	Male (n = 5)	SEM 1	G	S	G × S
	Age (day)	415	421	430	429	7.34	0.74	0.07	0.54
	HCW 2 (kg)	208.9	238.8	225.9	238.7	5.23	<0.001	0.06	0.06
	CCW 3 (kg)	208	238.5	224.8	238.3	5.19	<0.001	0.07	0.06
	Hump (cm)	3.0	4.0	3.0	3.5	0.31	0.08	0.23	0.41
	Ossification	140 a	120 b	150 a	110 b	6.58	<0.001	0.97	0.09
	Marbling	230	250	250	220	26.1	0.75	0.82	0.33
	pH	5.7	5.7	5.6	5.7	0.06	0.27	0.49	0.52
	EU conformation 4 7	7	7	7	0.23	0.2	0.79	0.80
	EU fat score 4	5	5	6	6	0.35	0.96	0.2	0.54
	Fat % HCW 5	14.6 b	15.1 b	17.1 a	15.2 b	0.47	0.08	<0.01	<0.01
	Muscle % HCW 6	67.3 a	66.6 ab	65.3 b	66.6 ab	0.55	0.5	<0.01	<0.01
	CIE L*	32.2	31.1	30.5	31.0	0.93	0.73	0.25	0.29
	CIE a*	13.4	14.4	14.5	13.9	0.61	0.70	0.60	0.15
	CIE b*	14.3	14.9	14.8	14.8	0.66	0.64	0.78	0.62
			1 SEM: standard error of the mean; 2 HCW: hot carcass weight; 3 CCW: cold carcass weight; 4 on a 15-point European
			grid; 5 Fat % HCW: fat percentage based on hot carcass weight [fat weight (kg)/hot carcass weight (kg) %]; 6 Muscle %
			HCW: muscle percentage based on hot carcass weight [muscle weight (kg)/hot carcass weight (kg) %]. a, b Within a
			row, means with different letters are significantly different (p < 0.05) between different groups.
			3.2. Effect of Animal Gender and Farming System on FA Content, Antioxidant Content, Vitamin
			Content and Eating Quality

Table 3 .

 3 Estimated marginal means of FA content, antioxidant content, and vitamin content as affected by animal gender and farming system.

	System (S)		MONO		MIX		Significance of F-Value
	Gender (G)	Female (n = 10)	Male (n = 9)	Female (n = 6)	Male (n = 5)	SEM 1	G	S	G × S
	(g/100 g muscle)
	Total lipids	2.68	2.92	3.35	2.89	0.23	0.57	0.11	0.09
	Total FA	2.47	2.56	3.12	2.77	0.24	0.54	<0.05	0.30
	(mg/100 g muscle)
	C12:0	3.73	4.39	5.96	6.17	0.65	0.44	<0.001	0.68
	C14:0	62.32	72.09	92.46	78.24	8.98	0.77	<0.05	0.13
	C16:0	566.4	600.2	730.7	639.5	62.61	0.59	0.07	0.25
	C18:0	437.4	426.7	525.7	472.1	50.9	0.46	0.13	0.63
	Linear SFA	1130.3	1166.3	1432.1	1265.3	127.1	0.55	0.08	0.36
	Branched SFA	34.99	37.54	45.33	42.16	3.97	0.93	<0.05	0.40
	Total SFA	1165.3	1203.9	1477.5	1307.4	130.8	0.56	0.07	0.36
	C16:1n-7	45.81	50.70	59.92	56.57	5.66	0.87	<0.05	0.40
	C18:1n-9	686.4	715.4	894.1	788.2	72.96	0.54	<0.05	0.29
	C18:1 9trans	9.15	9.43	12.32	10.57	1.07	0.43	<0.05	0.27
	C18:1 10-11trans	50.90	56.66	69.90	57.89	6.91	0.6	0.09	0.14
	MUFA cis	822.7	862.4	1067.5	949.2	86.23	0.60	<0.05	0.29
	MUFA trans	102.51	111.00	137.03	119.92	12.45	0.69	0.05	0.24
	Total MUFA 2	925.2	973.4	1204.6	1069.1	96.42	0.60	<0.05	0.27
	C18:2n-6 (LA) 3	76.80	79.52	81.25	74.40	4.37	0.58	0.93	0.21
	C20:4n-6 (ARA) 4	25.96	24.39	31.93	24.20	2.40	<0.05	0.17	0.14
	PUFA n-6 LC 5	38.71	34.85	46.77	37.01	3.48	<0.05	0.09	0.33
	PUFA n-6 trans	25.93	29.52	33.76	30.47	2.87	0.95	0.08	0.17
	Total PUFA n-6	147.4	152.0	169.6	146.6	9.94	0.29	0.33	0.12
	C18:3n-3 (ALA)	50.28	49.48	55.97	51.03	4.66	0.48	0.37	0.61
	C20:5n-3 (EPA)	11.60	9.68	13.17	9.80	1.75	0.09	0.57	0.63
	C22:5n-3 (DPA)	19.32	18.39	23.34	20.91	2.00	0.33	0.07	0.66
	C22:6n-3 (DHA)	4.68	3.66	4.71	4.21	2.07	0.67	0.87	0.88
	PUFA n-3	96.31	91.33	107.87	98.50	9.81	0.40	0.27	0.80
	CLA 6 9cis11trans	20.37	23.96	28.57	25.64	2.55	0.88	<0.05	0.15
	Total CLA	24.07	27.07	31.61	27.95	2.87	0.89	0.09	0.18
	Total PUFA	272.7	275.1	315.3	279.2	21.70	0.37	0.22	0.31
	C16:0/C18:0	1.38	1.36	1.32	1.37	0.06	0.05	0.34	0.59
	PUFA/SFA ratio	0.25	0.20	0.25	0.22	0.02	<0.05	0.49	0.58
	ω-6/ω-3 ratio	1.53	1.74	1.47	1.68	0.07	0.001	0.31	0.96
	C18:2n-6/C18-3n:3	1.51	1.65	1.45	1.54	0.08	0.09	0.24	0.72
	CAT 7	1.84	2.04	2.20	2.32	0.19	0.32	0.06	0.80
	GPx 8	0.04	0.04	0.38	0.34	0.01	0.76	0.48	0.78
	SOD 9	3.24	3.07	3.16	3.45	0.24	0.76	0.46	0.25
	Vitamin 10 A	0.03	0.03	0.04	0.04	0.01	0.31	0.23	0.47
	Vitamin 10 E	2.53	2.49	2.66	2.15	0.19	0.10	0.52	0.17

Table 4 .

 4 Estimated marginal means of eating quality scores evaluated by untrained consumers and trained panelists and WBSF as affected by animal gender and farming system.

	System (S)		MONO		MIX		Significance of F-Value
	Gender (G)	Female (n = 10)	Male (n = 10)	Female (n = 6)	Male (n = 5)	SEM 1	G	S	G × S
	Cut-striploin	Consumer testing
	Tenderness	60.99	47.63	59.86	51.5	6.78	0.07	0.81	0.66
	Juiciness	62.08	52.54	55.70	57.44	3.75	0.23	0.82	0.09
	Flavor liking	62.35	48.72	58.64	53.7	5.27	<0.05	0.89	0.34
	Overall liking	60.45	48.69	58.95	50.84	5.52	<0.05	0.95	0.70
	MQ4 2	61.17	48.24	59.25	51.78	5.83	<0.05	0.90	0.49
	Cut-striploin	Panelist testing
	Initial tenderness	7.45	7.58	7.76	7.41	0.23	0.58	0.71	0.22
	Overall tenderness	7.56	7.41	7.73	7.54	0.27	0.44	0.51	0.93
	Juiciness	7.38	6.99	7.27	7.49	0.31	0.74	0.45	0.25
	Initial juiciness	6.92	7.08	6.92	7.04	0.27	0.54	0.93	0.93
	Typical flavor	6.80	6.75	6.98	6.91	0.25	0.77	0.43	0.97
	Abnormal flavor	9.67	9.71	9.75	9.66	0.09	0.78	0.86	0.43
	Mastication residual	8.06	7.76	8.25	7.91	0.32	0.24	0.54	0.94
	WBSF (N/cm 2 )	84.01	78.26	67.95	72.25	8.05	0.92	0.12	0.46
	Cut-internal flank plate	Consumer testing
	Tenderness	63.12	69.09	65.39	67.53	4.74	0.32	0.93	0.64
	Juiciness	58.70	65.51	60.92	62.01	4.25	0.28	0.86	0.43
	Flavor liking	63.12	68.73	60.75	66.57	3.53	0.07	0.45	0.97
	Overall liking	62.10	67.50	61.39	66.85	4.05	0.12	0.84	0.99
	MQ4	62.84	68.47	62.55	67.10	4.01	0.14	0.81	0.87
	Cut-bolar blade	Consumer testing
	Tenderness	70.05	63.82	62.42	64.50	4.74	0.53	0.33	0.26
	Juiciness	70.14	64.00	63.58	69.53	3.85	0.98	0.88	0.07
	Flavor liking	71.70	66.22	63.22	68.63	3.70	0.99	0.34	0.09
	Overall liking	71.34	64.98	63.11	67.10	4.44	0.75	0.42	0.18
	MQ4	71.35	64.94	62.85	66.57	4.25	0.71	0.35	0.17

1 SEM: standard error of the mean; 2 MQ4: combined meat eating quality score, which is used to describe the overall eating experience of consumers based on the perception of tenderness, juiciness, flavor liking, and overall liking. All data used in this table were from 14-day-aged beef samples.

Table 5 .

 5 Distribution of beef samples to different MSA quality grades by consumers based on the present database versus the European database utilized. Salers: the present dataset; 2 n: the number of the samples tested by consumers in the corresponding category; 3 %: the proportion of samples of each MSA quality grade in all the samples of each dataset. In MSA consumer testing, each beef sample was divided into 10 portions and each portion was evaluated by one consumer.

	Samples	Consumer-Assigned MSA Quality Grade	Chi-Squared Test
	Dataset	2 star	3 star	4 star	5 star	p
		n 2 (% 3 )	n (%)	n (%)	n (%)
	Angus x Salers 1 (A)	271 (17%)	401 (26%)	462 (30%)	412 (27%)	<0.001
	EU dataset (B) 22,686 (26%)	33,809 (39%)	20,430 (24%)	9699 (11%)

(A) 1546 beef samples of striploin, bolar blade and internal flank plate from the present study (1550 beef samples from 155 muscle cuts were tested by the current French consumers, 4 consumer data missed); (B) 86,624 beef samples from 22 muscle cuts were tested by consumers from France, Poland, Ireland, and Northern Ireland. 1 Angus x

Table 6 .

 6 Consumer scores on meat eating quality traits based on the present database versus the European database utilized.

	Cut	Dataset	n	Tenderness	Juiciness	Flavor Liking	Overall Liking	MQ4
	Bolar blade	Angus x Salers 1 EU 2	31 54	66.19 a 50.36 b	66.79 a 56.98 bc	67.79 a 55.60 b	67.01 a 54.29 b	66.91 a 52.44 c
	Striploin	Angus x Salers 3 EU 4	31 326	54.94 ab 52.74 b	57.01 bc 53.61 c	55.84 b 55.58 b	54.82 b 54.58 b	55.36 bc 53.69 c
			SEM	2.99	2.52	2.28	2.49	2.48
			p	<0.001	<0.001	<0.001	<0.001	<0.001
		1 Angus x Salers: 31 14-day-aged bolar blade samples from the present dataset; 2 EU: 54 blade samples from
		45 young animals of 3 breeds; 3 Angus x Salers: 31 14-day-aged striploin samples from the present dataset;
		4 EU: 326 striploin samples from 267 young animals of 9 breeds. Internal flank plates were not analyzed in the EU
		dataset, so the comparison was made based only on striploin and bolar blade. a, b, c Within a row, means with
		different letters are significantly different (p < 0.05) between different groups.

  are cited in Supplementary Materials. Conceptualization, S.P. and J.-F.H.; methodology, S.P. and J.-F.H.; validation, S.P. and J.-F.H.; formal analysis, J.L.; investigation, J.L., M.-P.E.-O., L.P., D.G., D.D., F.N., B.S., I.L.; data curation, M.-P.E.-O., L.P., D.G., D.D., F.N., B.S., I.L.; writing-original draft preparation, J.L.; writing-review and editing, J.L., M.-P.E.-O., L.P., S.P. and J.-F.H.; supervision, S.P. and J.-F.H.; project administration, S.P. and J.-F.H.; funding acquisition, S.P. and J.-F.H. All authors have read and agreed to the published version of the manuscript.

	Author Contributions:

Table 1

 1

	Mean, standard deviation (SD), minimum and maximum values for animal age
	and carcass characteristics
	Carcass characteristic	Mean	SD	Min	Max
	Age (day)	422	16.8	386	458
	Carcass weight (kg)	226.0	17.5	190.2	254.2
	Hump height (cm)	3.5	0.7	2.0	5.0
	Ossification score (100-590)	130	20.8	100	190
	Marbling score (100-1190)	240	56.4	160	350
	Rib fat thickness (mm)	0.3	0.7	0.0	2.0
	pH	5.66	0.13	5.48	5.93

Muscle sampling

Left and right striploins (m. longissimus dorsi et thoracis, MSA code: STP045), bolar blade (m. triceps brachii caput longum, MSA code: BLD096) and internal flank plate (m. obliquus internus abdominis, MSA code: TFL052) (McGilchrist

Table 2

 2 STP045: m. longissimus dorsi et thoracis; 4 TFL064: m. rectus abdominis; ageing time is indicated in days; 5 AUS grilling: 25 mm thick steaks were sliced across the grain and grilled on the 75 × 60 × 25 mm side; 6 French slice Ⅰ grilling: 75 × 60 × 25 mm steaks were sliced along muscle fibers and grilled on the 75 x 60 mm side; 7 French slice Ⅱ grilling: 75 x 25 x 25 mm steaks were sliced across the grain and grilled on 75 x 25 mm thin side Continual validation and model development over the 25-year history of MSA in

			Mean (clipped data) 1 , standard error (SE), minimum and maximum values for
		consumer scores of eating quality traits and MQ4 score for each type of beef sample
		Beef samples		Consumer scores (0-100) -Mean (SE) [Min-Max]	Combined
									score
	Cut	Ageing	Cooking	n	Tenderness	Juiciness	Flavor	Overall	MQ4
							liking	liking
	BLD096	5	AUS grilling 31	64.6 (1.6)	65.8 (1.4)	65.6 (1.6)	65.2 (1.6)	65.1 (1.6)
					[45.0-80.7]	[47.0-77.8]	[44.0-86.8]	[41.8-84.7]	[43.41-83.9]
	BLD096	14	AUS grilling 31	65.9 (1.9)	66.7 (1.5)	67.8 (1.5)	67.0 (1.8)	66.9 (1.7)
					[40.0-82.8]	[36.5-82.0]	[43.3-84.6]	[40.3-84.3]	[41.2-83.9]
	STP045	14	AUS grilling 5 31	54.9 (2.8)	57.0 (1.6)	55.8 (2.3)	54.8 (2.3)	55.1 (2.5)
					[27.2-89.0]	[42.8-80.0]	[31.0-83.0]	[33.0-85.3]	[32.7-86.3]
	TFL064	14	French slice	31	66.2 (1.9)	61.9 (1.7)	65.0 (1.5)	64.5 (1.6)	65.3 (1.6)
			Ⅰ grilling 6		[40.3-82.5]	[33.7-79.2]	[44.0-77.8]	[40.8-79.0]	[41.9-79.1]
	TFL064	14	French slice	31	70.2 (1.9)	67.3 (1.5)	68.7 (1.7)	69.1 (1.9)	69.4 (1.8)
			Ⅱ grilling 7		[41.0-87.8]	[49.2-82.8]	[37.3-85.5]	[36.2-84.5]	[38.0-86.1]

Table 3

 3

	Accuracy of MQ4-assigned quality grade in comparison with actual consumer-
	selected grade
	MQ4-assigned	Actual consumer-selected grade Total	% 1 consumer	% 2 consumer
	quality grade	2star	3star	4star	5star	n	well	well-plus over-
							estimated	estimated
	2star	177	90	4	0	271	65.3%	100%
	3star	47	269	83	2	401	67.1%	88.3%
	4star	9	93	289	71	462	62.6%	77.9%
	5star	1	5	144	262	412	63.6%	63.6%
	Total samples	234	457	520	335	1546
	Total correct	177	269	289	262	997
	% 3 accuracy	75.6% 58.9% 55.6% 77.7% 64.5%

Table 4

 4

	Simple correlation coefficients and mean differences between observed and
	predicted MQ4 scores
		Observed MQ4 score (MQ4 = 0.36 * tn -0.02 * ju+ 0.29 * fl + 0.37 * ol)
		BLD096 1 STP045 2 TFL064 3	BLD096	STP045	TFL064
		Correlation coefficients	Mean difference 4 (RMSE 5 )
	Predicted MQ4	-0.12 ns	-0.23 ns	0.26 ns	-13.01	-0.98	-4.50 (9.61)
	score				(16.4)	(14.2)

1 BLD096: m. triceps brachii caput longum; 2 STP045: m. longissimus dorsi et thoracis; 3 TFL064: m. rectus abdominis;

4

Mean difference: predicted MQ4 score -observed MQ4 score; 5 RMSE: Root Mean Square Error = √[ Σ(predicted MQ4 score -observed MQ4 score) 2 / 31]; ns means the correlation is non-significant (P > .05); The three muscle cuts (n=31 for each cut) were 14 days aged and MSA grilling cooked to rare degree of doneness. The prediction of MQ4 score for TFL064 is based on stir fry.

Table 5

 5 BLD096: m. triceps brachii caput longum; 2 STP045: m. longissimus dorsi et thoracis; 3 TFL064: m. rectus abdominis; 4 MQ4obs: observed MQ4 score calculated from actual consumer scores; 5 MQ4pre: predicted MQ4 score provided by the MSA prediction model;

		Multiple regression models for indicating MQ4 score using main carcass
		grading factors
	Cut	MQ4 score estimation models	6 R 2	Model
				No
	BLD096 1 MQ4obs 4 =-0.18CW+0.98Hump+0.02Ossification+0.08Age+0.03Marbling+34.44pH*	0.10	1
		MQ4pre 5 = 0.02CW-0.38Hump-0.09Ossification***-0.01Age+0.02Marbling***-5.45pH***	0.87	2
	STP045 2 MQ4obs=-0.34CW-5.37Hump+0.01Ossification+0.21Age-0.01Marbling +36.32pH	0.19	3
		MQ4pre=0.04CW**-0.01Hump-0.07Ossification***-0.01Age+0.04Marbling***-5.90pH***	0.92	4
	TFL064 3 MQ4obs=0.12CW-0.07Hump-0.01Ossification+0.12Age+0.02Marbling +10.58pH	0.04	5
		MQ4pre=0.03CW**-0.12Hump-0.08Ossification***+0.004Age+0.03 Marbling***-5.87pH*** 0.92	6
		1

Table 6

 6 Variance analysis of mean values of sensory scores of BLD096 between 5

	and 14 days of ageing
	Cut-BLD096	5 days ageing	14 days ageing (n=31)
			(n=31)
		Mean Min Max	Mean	Min	Max	SE	P
								M
	Tenderness	64.6	45	80.7	66.0	40	82.8	1.8 0.6
	Juiciness	65.8	47	77.8	66.8	36.5	82	1.5 0.6
	Flavor liking	65.6	44	86.8	67.8	43.3	84.5	1.6 0.3
	Overall liking	65.2	41.8 84.7	67.0	40.3	84.3	1.7 0.4
	MQ4	65.1	43.4 83.9	66.9	41.2	83.8	1.6 0.4

Table 7

 7 Variance analysis of mean values of sensory scores of TFL052 between two

	muscle slicing methods
	Cut-TFL052	French slice Ⅰ 1 (n=31)	French slice Ⅱ 2 (n=31)
		Mean	Min	Max	Mean	Min	Max	SEM	P
	Tenderness	70.2	41	87.8	66.2	40.3	82.5	1.9	0.12
	Juiciness	67.3 a	49.2	82.8	61.9 b	33.7	79.2	1.6	<0.05
	Flavor liking	68.7	37.3	85.5	65.0	44	77.8	1.6	0.11
	Overall liking	69.1	36.2	84.5	64.5	40.8	79	1.7	0.07
	MQ4	69.1	39.3	84.7	64.9	41.1	78.8	1.6	0.07

Table 1 .

 1 Demographic information of the respondents of our survey (4666 responses).

	Question	Response Option	No. 1	% 2	Mean	SD
		No wish to answer = 0	148	3.17
	Sex	Female = 1	2360	50.6	1.43	0.56
		Male = 2	2158	46.2
		18-30 years of age = 1	2149	46.1
	Age	31-50 years of age = 2	1470	31.5	1.76	0.79
		>51 years of age = 3	1047	22.4
		No wish to answer = 0	141	3.02
		Primary/High/Engineer school = 1	695	14.9
	Education	College = 2	2596	55.6	2.12	0.84
		Master = 3	951	20.4
		PhD = 4	283	6.07
		Meat scientist = 1	276	5.92
	Activity area	Other scientist = 2 Meat worker = 3	760 164	16.3 3.51	3.46	0.96
		Others = 4	3466	74.3

Table 1 .

 1 Cont.

	Question	Response Option	No. 1	% 2	Mean	SD
		Never: vegetarian or vegan diet = 1	157	3.36
	Meat consumption	Rarely: weekly or less = 2 Regularly: several times a week = 3	1099 1631	23.6 34.9	3.08	0.86
		Daily or within each meal = 4	1779	38.1
	Familiarity 4	Yes = 1 No = 2	2611 2055	55.9 44.1	1.44	0.50
	1 No.: number of responses; 2 %: percentage of responses; 3 yuan: Chinese yuan renminbi (CNY),
	1 CNY = 0.15 USD (American dollar); 1 CNY = 0.13 Euros, on 21 January 2021; 4 Familiarity: have you heard
	about artificial meat?
	3.1.2. Societal Challenges

Table 2 .

 2 Responses about societal challenges related to the meat industry and the perceptions about artificial meat (4666 responses).

	A. Question-Societal Challenges

Table 5 .

 5 Pairwise comparisons between significant demographic groups for willingness to try.

		A-Age			B-Education			C-Activity Area
	Sex	18-30	31-50	>51	P/H/E 1	University	Master	PhD	MeatS 2	Meat W 3	Other S 4	Others 5
	Female	3.05 d	3.41 c	3.69 b	3.3 c,d	3.16 d	3.72 b	3.31 c	3.81 a,b	4.16 a	3.31 c	3.18 c
	Male	3.08 d	3.87 a	3.89 a	3.41 b	3.47 b	3.91 a	3.62 b,c	3.83 ab	3.57 b	3.54 b	3.56 b
				D-Income					E-Meat consumption
	Sex	<1 k	1-3 k		3-5 k	5-7 k	8-10 k	>10 k	Never	Rarely	Regularly	Daily
	Female	3.07 d	3.37 c		3.29 c,d	3.69 b	3.50 a,b,c	3.72 a,b	2.26 e	3.02 d	3.22 c	3.68 b
	Male	3.12 d	3.59 b,c		3.81 a,b	3.90 a	3.81 a,b	3.55 b	1.86 e	3.20 c	3.55 b	3.84 a
				F-Education					G-Meat consumption
	Age	P/H/E		University	Master	PhD		Never	Rarely	Regularly	Daily
	18-30	3.04 e			2.99 e	3.35 c,d	3.39 c,d		2.54 e,f	2.95 e	3.09 d,e	3.15 d
	31-50	3.4 d			3.66 c	3.92 b	3.42 c,d		2.04 f	3.09 d,e	3.56 c	4 b
	>51	3.39 d			3.86 b	4.15 a	3.88 a,b,c	1.72 f,g	3.33 c	3.83 b	4.16 a

Table 6 .

 6 Analysis of variance of willingness to eat regularly/willingness to pay according to demographics.

	Willing to Eat	p Value	Willing to Eat	p Value
	Sex	0.026	Familiarity × Income	0.014
	Age	0.292	Income × Age	0.005
	Education	0.070	Income × Sex	0.042
	Activity area	0.017
	Income	0.004
	Meat consumption	0.920
	Familiarity	0.000
	Willing to Pay	p Value	Willing to Pay	p Value
	Sex	0.060	Familiarity × Age	0.005
	Age	0.008	Age × Education	0.003
	Education	0.150	Age × Meat consumption	0.008
	Activity area	0.870	Sex × Meat consumption	0.024
	Income	0.009
	Meat consumption	0.170
	Familiarity	0.190

Table 1

 1 Demographic information of the French survey respondents (N = 5418 responses).

	Questions	Response options	Number of	Percentage of
			responses	responses
	Gender	Female	2852	52.6
		Male	2566	47.4
	Age	18-30 years of age	2194	40.5
		31-50 years of age	1959	36.2
		>51 years of age	1265	23.3
	Education	Primary or high school	67	1.2
		Baccalaureate	782	14.4
		Two years of education	249	4.6
		after the Baccalaureate
		Licence (Bachelor's	1131	20.9
		degree)
		Master's degree	2489	45.9
		PhD	596	11.0
		Do not wish to answer	104	1.9
	Business area	Meat scientist	429	7.9
		Other scientist	1996	36.8
		Familiar with the meat	1778	32.8
		sector
		Other	1215	22.4
	Net monthly income*	€0-1500	1528	28.2
		€1500-2000	948	17.5
		€2000-2500	747	13.8
		€2500-3000	521	9.6
		€3000-4000	498	9.2
		€4000 and more	467	8.6
		Do not wish to answer	709	13.1
	Meat consumption	Never: vegetarian or	334	6.2
	habits	vegan diet
		Rarely: weekly or less	726	13.4
		Regularly: several	2954	54.5
		times a week
		Daily or at every meal	726	13.4
	Familiarity with the	Yes	4678	86.3
	topic of "artificial	No	740	16.7
	meat"

* €1 corresponds to approximately $1.12.

É.

Hocquette et al.

  Table 1 in supplementary material,). However, WTE depends on many factors. It differs the most according to

Table 2

 2 Pairwise comparisons for willingness to try between demographic groups for occupation interacting with age or level of meat consumption (N = 5418 responses).

	A	Familiar with	Scientist	Scientist not	Not scientist
		the meat sector	working on	working on	not familiar
		and not scientist	meat	meat	with the meat
					sector
	18-30	2.75 c	3.54 ef	3.75 f	3.36 e
	years of
	age 31-50	2.32 a	3.12 de	3.31 e	2.76 c
	years of
	age >51 years	2.34 ab	3.20 de	2.87 cd	2.62 bc
	of age
	B	Familiar with	Scientist	Scientist not	Not scientist
		the meat sector	working on	working on	not familiar
		and not scientist	meat	meat	with the meat
					sector
	Daily Regularly Rarely Never	2.16 a 2.52 a 3.40 cd 3.73 cd	3.14 bc 3.37 c 3.32 c 3.80 d	3.42 cd 3.45 cd 3.73 d 3.20 c	2.57 a 2.87 ab 3.20 c 3.18 bc

WTT is expressed on a 1-5 scale with only one possible answer to the questions "Would you be willing to try 'artificial meat'?": 1: Definitively not, 2: Probably not, 3: Unsure, 4: Probably yes, 5: Definitively yes. The means with different superscript letters within the same sub-table differ significantly at P < 0.05.

É.

Hocquette et al.

Table 4

 4 Pairwise comparisons for willingness to pay between demographic groups for level of meat consumption interacting with age (N = 5418 responses).

		Never	Rarely	Regularly	Daily
	18-30 years of age 31-50 years of age >51 years of age	2.85 f 2.78 f 2.59 ef	2.51 e 2.10 cde 2.24 de	2.22 de 1.88 abc 1.78 ab	1.86 abc 1,77 ab 1.69

a

The means with different superscript letters within the same sub-table differ significantly at P < 0.05. 1: Definitively not, 2: Probably not, 3: Unsure, 4: Probably yes, 5: Definitively yes.

Table 5

 5 Effects of "gender × age" interaction on drivers, motives and barriers of acceptance of "artificial meat".A. Mean answers to four questions: Does meat production cause ethical (Answer 1) or environmental problems (Answer 2)? Is "artificial meat" ethics (Answer 3) or ecofriendly (Answer 4)?

			Answer	Answer	Answer	Answer
			1	2	3	4
	18-30 years of age 18-30 years of age 31-50 years of age 31-50 years of age More than 51 years	Men Women Men Women Men	2.88 c 3.51 d 2.35 a 3.02 c 2.44 b	3.17 b 3.65 c 2.48 a 3.00 b 2.54 a	2.48 c 2.79 d 1.81 a 2.07 b 1.73 a	2.29 c 2.47 d 1.70 a 1.91 b 1.66 a
	of age More than 51 years	Women	3.20 c	3.21 b	2.27 bc	2.14 c
	of age
	B. Mean answers to four questions: Has "artificial meat" negative impacts on livestock
	(Answer 1) or rural life (Answer 2)? Is "artificial meat" safe (Answer 3) or tasty
	(Answer 4)?
			Answer	Answer	Answer	Answer
			1	2	3	4
	18-30 years of age 18-30 years of age 31-50 years of age 31-50 years of age More than 51 years	Men Women Men Women Men	4.18 ab 4.20 ab 4.26 b 4.37 c 4.11 a	3.99 a 4.00 a 4.20 b 4.30 b 4.43 c	2.52 d 2.60 d 2.02 a 2.19 bc 2.09 ab	2.09 d 2.20 d 1.66 ab 1.74 bc 1.57 a
	of age More than 51 years	Women	4.31 bc	4.50 c	2.29 c	1.83 c
	of age

Table 6

 6 Effects of "gender × meat consumption level" interaction on drivers, motives and barriers of acceptance of "artificial meat"

	A. Mean answers to four questions: Does meat production cause ethical (Answer 1) or
	environmental problems (Answer 2)? Is "artificial meat" ethics (Answer 3) or eco-
	friendly (Answer 4)?
	Meat consumption	Gender	Answer	Answer	Answer	Answer
	level		1	2	3	4
	Never Rarely Regularly Daily	Women Men Women Men Women Men Women Men	4.74 e 4.83 e 4.07 d 3.82 d 3.13 c 2.54 b 2.42 b 1.97 a	4.76 f 4.77 f 4.20 e 4.14 e 3.22 d 2.72 c 2.40 b 2.11 a	3.46 e 3.90 f 2.89 d 2.92 d 2.37 c 1.94 b 1.86 b 1.62 a	3.11 e 3.49 f 2.57 d 2.52 d 2.17 c 1.83 b 1.72 ab 1.55 a
	B. Mean answers to four questions: Has "artificial meat" negative impacts on livestock
	(Answer 1) or rural life (Answer 2)? Is "artificial meat" safe (Answer 3) or tasty
	(Answer 4)?
	Meat consumption	Gender	Answer	Answer	Answer	Answer
	level		1	2	3	4
	Never Rarely Regularly Daily	Women Men Women Men Women Men Women Men	3.50 a 3.55 ab 3.95 c 3.71 b 4.26 d 4.32 d 4.54 e 4.49 e	2.81 b 2.06 f 3.84 d 3.62 c 4.28 e 4.40 f 4.56 f 4.60 f	3.47 e 4.06 f 2.72 d 2.83 d 2.35 c 2.20 b 1.85 a 1.80 a	3.28 f 3.66 g 2.33 d 2.50 e 1.85 c 1.71 b 1.50 a 1.46 a

Table 7

 7 Effects of "occupation × level of meat consumption" interaction on drivers, motives and barriers of acceptance of "artificial meat"

	A. Mean answers to four questions: Does meat production cause ethical (Answer 1) or
	environmental problems (Answer 2)? Is "artificial meat" ethics (Answer 3) or eco-
	friendly (Answer 4)?
	Occupation	Level of meat	Answer	Answer	Answer	Answer
		consumption	1	2	3	4
	Familiar with the meat sector and non-scientist Scientist working on meat Scientist not working on meat Non-scientist not familiar with the meat sector	Never Rarely Regularly Daily Never Rarely Regularly Daily Never Rarely Regularly Daily Never Rarely Regularly Daily	4.49 h 3.62 g 2.27 b 1.80 a 5.00 h 3.87 g 2.53 cd 2.16 b 4.73 h 4.07 g 3.25 f 2.73 d 4.94 h 4.08 g 3.08 e 2.37 bc	4.45 gh 3.69 ef 2.31 b 1.83 a 4.80 h 4.03 fg 2.66 c 2.22 b 4.75 h 4.35 g 3.50 e 2.94 d 4.96 h 4.13 g 3.14 d 2.40 bc	3.15 f 2.32 de 1.60 b 1.30 a 3.60 g 2.70 e 1.95 c 1.80 bc 3.60 g 3.14 f 2.64 e 2.47 e 3.75 g 2.74 ef 2.22 d 1.86 c	2.85 fg 2.16 d 1.53 b 1.28 a 3.4 h 2.41 ef 1.75 c 1.72 bc 3.11 gh 2.63 f 2.36 e 2.15 d 3.55 h 2.62 f 2.18 d 1.78 c
	B. Mean answers to four questions: Has "artificial meat" negative impacts on livestock
	(Answer 1) or rural life (Answer 2)? Is "artificial meat" safe (Answer 3) or tasty
	(Answer 4)?
	Occupation	Level of meat	Answer	Answer	Answer	Answer
		consumption	1	2	3	4
	Familiar with the meat sector and non-scientist Scientist working on meat Scientist not working on meat Non-scientist not familiar with the meat sector	Never Rarely Regularly Daily Never Rarely Regularly Daily Never Rarely Regularly Daily Never Rarely Regularly Daily	3.79 a 4.13 b 4.54 bc 4.64 a 3.00 ef 4.11 fg 4.45 g 4.52 efg 3.64 cd 3.88 de 4.13 de 4.35 d 3.18 bc 3.75 cd 4.14 cd 4.36 bc	2.79 b 4.20 de 4.68 f 4.78 f 2.50 ab 4.11 de 4.68 f 4.70 f 2.84 b 3.77 c 4.12 de 4.32 e 2.14 a 3.56 c 4.09 d 4.32 de	3.26 fg 2.59 de 1.83 b 1.47 a 3.80 g 2.97 f 2.57 de 2.23 cd 3.65 g 2.96 f 2.66 e 2.47 d 3.75 g 2.42 f 2.16 c 1.74 b	3.02 f 2.16 de 1.48 b 1.29 a 3.50 fg 2.51 e 1.78 c 1.61 bc 3.29 f 2.40 e 2.00 d 1.80 c 3.71 g 2.40 e 1.83 c 1.50 bc

Table 8

 8 Effects of "level of meat consumption × age" interaction on drivers, motives and barriers of acceptance of "artificial meat" A. Mean answers to four questions: Does meat production cause ethical (Answer 1) or environmental problems (Answer 2)? Is "artificial meat" ethics (Answer 3) or ecofriendly (Answer 4)?

	Level of meat	Age	Answer	Answer	Answer	Answer
	consumption		1	2	3	4
	Never	18-30 years	4.70 f	4.74 g	3.46 de	3.08 e
		of age 31-50 years	4.89 f	4.81 g	3.74 e	3.39 ef
		of age More than	4.90 f	4.81 g	3.97 e	3.68 f
		51 years of
	Rarely	age 18-30 years	4.05 e	4.32 f	3.21 d	2.76 d
		of age 31-50 years	3.94 e	3.99 e	2.43 c	2.23 c
		of age More than	3.94 e	3.99 e	2.50 c	2.32 c
		51 years of
	Regularly	age 18-30 years	3.17 d	3.39 d	2.63 c	2.38 c
		of age 31-50 years	2.65 c	2.75 c	1.93 b	1.80 b
		of age More than	2.69 c	2.77 c	1.89 b	1.81 b
		51 years of
	Daily	age 18-30 years	2.39 b	2.53 b	1.98 b	1.89 b
		of age 31-50 years	1.99 a	2.01 a	1.53 a	1.43 a
		of age More than	2.03 a	2.09 a	1.59 a	1.49 a
		51 years of
		age
	B. Mean answers to four questions: Has "artificial meat" negative impacts on livestock
	(Answer 1) or rural life (Answer 2)? Is "artificial meat" safe (Answer 3) or tasty
	(Answer 4)?
	Level of meat	Age	Answer	Answer	Answer	Answer
	consumption		1	2	3	4
	Never	18-30 years	3.58 a	2.82 b	3.52 f	3.28 e
		of age 31-50 years	3.43 a	2.15 a	3.88 f	3.56 e
		of age More than	3.23 a	2.36 ab	3.68 f	3.61 e
		51 years of
	Rarely	age 18-30 years	3.90 b	3.69 c	2.96 e	2.58 d
		of age 31-50 years	3.94 b	3.94 de	2.36 cd	2.02 c
		of age More than	3.77 b	3.86 cd	2.60 d	2.19 c
		51 years of
	Regularly	age 18-30 years	4.27 c	4.15 e	2.54 d	2.05 c
		of age 31-50 years	4.33 cd	4.44 f	2.13 c	1.66 b
		of age More than	4.27 c	4.44 f	2.16 c	1.61 b
		51 years of
	Daily	age 18-30 years	4.50 e	4.43 f	1.95 b	1.61 b
		of age 31-50 years	4.54 e	4.68 g	1.71 a	1.41 a
		of age More than	4.45 de	4.68 g	1.80 ab	1.37 a
		51 years of
		age

Table 9

 9 Effects of "occupation × age" interaction on drivers, motives and barriers of acceptance of "artificial meat"

	A. Mean answers to four questions: Does meat production cause ethical (Answer 1) or
	environmental problems (Answer 2)? Is "artificial meat" ethics (Answer 3) or eco-
	friendly (Answer 4)?
	Occupation	Age	Answer	Answer	Answer	Answer
			1	2	3	4
	Familiar with the	18-30	2.53 c	2.55 c	1.79 b	1.71 c
	meat sector and	years of
	non-scientist	age 31-50	2.05 a	2.08 a	1.45 a	1.38 a
		years of
		age More than	2.15 ab	2.22 ab	1.48 a	1.47 ab
		51 years of
	Scientist working	age 18-30	2.88 d	3.09 d	2.28 c	2.07 d
	on meat	years of
		age 31-50	2.41 bc	2.44 bc	1.83 b	1.67 bc
		years of
		age More than	2.38 bc	2.42 bc	1.83 b	1.73 c
		51 years of
	Scientist not	age 18-30	3.68 e	3.96 f	3.12 e	2.73 e
	working on meat	years of
		age 31-50	3.18 d	3.35 d	2.39 c	2.10 d
		years of
		age More than	3.14 d	3.27 d	2.31 c	2.03 d
		51 years of
	Non-scientist not	age 18-30	3.56 e	3.70 e	2.79 d	2.64 e
	familiar with the	years of
	meat sector	age 31-50	3.12 d	3.15 d	2.21 c	2.14 d
		years of
		age More than	3.14 d	3.13 d	2.15 c	2.15 d
		51 years of
		age
	B. Mean answers to four questions: Has "artificial meat" negative impacts on livestock
	(Answer 1) or rural life (Answer 2)? Is "artificial meat" safe (Answer 3) or tasty
	(Answer 4)?
	Occupation	Age	Answer	Answer	Answer	Answer
			1	2	3	4
	Familiar with the	18-30	4.57 c	4.55 f	1.91 b	1.60 bc
	meat sector and	years of
	non-scientist	age 31-50	4.57 c	4.70 f	1.66 a	1.42 a
		years of
		age More than	4.44 c	4.63 f	1.78 ab	1.48 ab
		51 years of
	Scientist working	age 18-30	4.35 bc	4.46e f	2.64 e	2.14 f
	on meat	years of
		age 31-50	4.49 c	4.69 f	2.47 de	1.65 bcd
		years of
		age More than	4.39 bc	4.64 f	2.40 cde	1.61 bcd
		51 years of
	Scientist not	age 18-30	4.05 a	3.78 b	2.95 f	2.42 g
	working on meat	years of
		age 31-50	4.09 a	4.19 d	2.56 e	1.87 de
		years of
		age

(continued on next page) É.

Hocquette et al.

Table 9

 9 (continued ) B. Mean answers to four questions: Has "artificial meat" negative impacts on livestock (Answer 1) or rural life (Answer 2)? Is "artificial meat" safe (Answer 3) or tasty (Answer 4)?

	Occupation	Age	Answer	Answer	Answer	Answer
			1	2	3	4
		More than	4.12 ab	4.28 de	2.53 e	1.75 cd
		51 years of
	Non-scientist not	age 18-30	3.92 a	3.54 a	2.41 cde	2.28 fg
	familiar with the	years of
	meat sector	age 31-50	4.13 ab	3.97 c	2.15 c	1.94 ef
		years of
		age More than	4.03 a	4.10 d	2.21 cd	1.84 cde
		51 years of
		age

⁎ P < .05, ⁎⁎ P < .01, ⁎⁎⁎ P < .001.a Whole = all the animals within young bulls, females and steers. b EUFat = European fat score. c EUConf = European conformation score. d Wt = Carcass weight. e Oss = ossification score. f NS = non-significant. g Young F: females younger than 987 days of age. h Old F: females older than 987 days of age. i DF: Degrees of Freedom. j Eq no = equation number.

a,b Within a row means with the different letter are significantly different (P < .05). c Mean O-marbling: mean value of observed marbling score. d SD-O: standard deviation of observed marbling score. e Mean P-marbling: mean value of predicted marbling score. f SD-P: standard deviation of predicted marbling score. g MSPE: mean-square prediction error. h MPE: mean prediction error.

Frontiers in Veterinary Science | www.frontiersin.org 2 March 2021 | Volume 8 | Article 611153

Frontiers in Veterinary Science | www.frontiersin.org

March 2021 | Volume 8 | Article 611153

AUSMB, AUS-MEAT Marbling score;

Meat Standards Australia (MSA) MB, MSA Marbling score;

Meat quality CUB045, MQ4 score of CUB045 (M. longissimus thoracis);

MQ4 STA045, MQ4 score of STA045 (M. longissimus thoracis et lumborum, anterior striploin piece);

MQ4 STP045, MQ4 score of STP045 (M. longissimus thoracis et lumborum, posterior striploin piece);

MQ4 OYS036, M. infraspinatus;

MQ4 BLD096, M. triceps brachii caput longum;

MQ4 RMP131, M. gluteus medius;

MQ4 KNU066, M. rectus femoris;

MQ4 OUT005, M. biceps femoris;

MQ4 EYE075, M. semitendinosus; and

MQ4 CHK074, M. semispinalis capitis; hang method: AT (Achilles tendon), aging time: 10 days, cooking method: grill. a,b Within a row, means with different letters are significantly different (p < 0.05) between the fifth and the tenth ribs.

T = Tenderness;

J = Juiciness;

F = Flavor liking;

T*J = interaction between Tenderness and Juiciness;

T*F = interaction between Tenderness and Flavor liking;

J*F = interaction between Juiciness and Flavor liking. ⁎ P < .05, ⁎⁎ P < .01, ⁎⁎⁎ P < .001.J.Liu, et al. Meat Science 168 (2020) 1081906

LS Mean: Least Squares Means. a, b, c Within a column, means with the different letter are significantly different (P < .05).

Foods 2022, 11, 2493. https://doi.org/10.3390/foods11162493 https://www.mdpi.com/journal/foods

SEM: standard error of the mean;

MUFA: monounsaturated fatty acid;

LA: linoleic acid;

ARA: arachidonic acid

;[5][6]

PUFA;6 CLA, conjugated

linoleic acid; 7 CAT, catalase (µmol H 2 O 2 consumed/min

per mg protein); 8 GPx, glutathione peroxidase (µmol NADPH/

min per mg protein); 9 SOD, superoxide

dismutase (IU/mg protein); 10 vitamin unit: µg/g tissue. All data used in this table are from 14-day-aged striploin. FA content results are presented for data from 30 animals, since the abnormal FA content of one sample from one male animal was not considered, due to a sampling problem.

Clipped data: two smallest and two largest answers were removed and the remaining six answers were used to calculate the mean value;

BLD096: m. triceps brachii caput

Well estimated: Percentage of real consumer-selected grade compared to the same grade after MQ4-assigned quality (i.e. consumer-selected

2star vs. MQ4-predicted

Foods 2021, 10, 353. https://doi.org/10.3390/foods10020353 https://www.mdpi.com/journal/foods

1 Much less,

2 Less, much less-1 and less-2 sometimes were analyzed together;

3 Neutral;

4 More,

5 Much more, more-4 and much more-5 sometimes were analyzed together;

Number of responses;

(percentage of responses).

Table 4. Analysis of variance of willingness to try according to demographics. Willing to Try p Value Willing to Try p Value Sex 0.000 Sex × Age 0.000 Age 0.000 Sex × Education 0.000 Education 0.000 Sex × Activity 0.004 Activity area 0.000 Sex × Income 0.04 Income 0.000 Sex × Meat Consumption

P/H/E: primary school/high school/engineering school;

Meat S: meat scientist;

Meat W: meat worker;

Other S: scientist work outside meat sector;

Others: people not scientist and outside the meat sector; Based on estimated marginal means; a-e : different letters in the same section indicate means with significant statistical differences at the 0.05 level; To correct for multiple testing, a Bonferroni-adjustment was used.

É.Hocquette et al.

Acknowledgements

The research work was sponsored by Chinese Scholarship Council (CSC). Data were obtained through the financial contributions of the European research project ProSafeBeef (Contract No. FOOD-CT-2006-36241), the Polish ProOptiBeef Farm to Fork project funded by the EU Innovative (POIG.01.03.01-00-204/09), the French 'Direction Générale de l'Alimentation' and FranceAgriMer, the Irish Department of Agriculture Food and The Marine under the FIRM program, and the Northern Ireland Department of Agriculture and Rural Development 'Vision' program. Furthermore, this project would not have been possible without the practical support of representatives of the beef industry across Europe.

Acknowledgements

The research work was sponsored by Chinese Scholarship Council (CSC). Data were obtained through the financial contributions of the European research project ProSafeBeef (Contract No. FOOD-CT-2006-36241), the Polish ProOptiBeef Farm to Fork project funded by the EU Innovative (POIG.01.03.01-00-204/09), the French 'Direction Générale de l'Alimentation' and FranceAgriMer, the Irish Department of Agriculture Food and The Marine under the FIRM programme, and the Northern Ireland Department of Agriculture and Rural Development 'Vision' program. Furthermore, this project would not have been possible without the practical support of representatives of the beef industry across Europe.

Acknowledgments:

The authors express their thanks to the staff of the experimental unit, Herbipôle of INRAE (Theix) for management and slaughtering of the animals. The authors also express their thanks to the staff and the panelists of Bordeaux Science Agro for the sensory quality evaluations. The authors are further thankful to the French Livestock Institute (Meat Quality Team), for muscle sampling and sensorial analyses with untrained consumers.

Acknowledgments

The authors wish to thank Dr. Caroline Brand, social and human sciences specialist from Isara, for careful proofreading of the questionnaire before dissemination. The authors also thank Céline Hocquette for clustering analysis and the respondents for their answers and their time spent to answer all questions.

. 谢谢你,我的刘嘉琦。最想感谢的 人是我妈,这么多年来难为你了,我没有让你过上在你这个年龄段该过的正常生活,Agriculture and Food Development Authority TPA Texture profile analysis TSG Traditional Specialty Guaranteed UKROFS UK Register of Organic Food Standards UNECE United Nations Economic Commission for Europe WBSF Warner-Bratzler shear force WHC Water holding capacity WTE Willing to eat WTP Willing to pay WTT

FUNDING

This research was included within the EcoMeat3G project funded by FEDER (convention no. AV0019409) and the Auvergne-Rhône Alpes Region and within the EcoRegMeat3G project funded by the French Agency of Research through the Institut Carnot France Futur Elevage. JL's scholarship was sponsored by the Chinese Scholarship Council (CSC).

Funding: This research was included within the EcoMeat3G project funded by FEDER (convention AV0019409) and the Auvergne-Rhône Alpes Region and within the EcoRegMeat3G project funded by the French Agency of Research through the Institut Carnot "France Futur Elevage". J.L.'s scholarship was sponsored by the Chinese Scholarship Council (CSC).

Institutional Review Board

Statement: The animal study protocol was approved by the Ethics Committee of INRAE (APAFIS#1417-2015081011477291 v3 and APAFIS#24191-2015043014541577 v4, June 2015).

DATA AVAILABILITY STATEMENT

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Data Availability Statement: Data are available on request to the authors.

Data Availability Statement:

The data presented in this study are available on request from the corresponding author. The data are not publicly available because they concern consumers' expression.

ETHICS STATEMENT

Ethical review and approval was not required for the animal study because Data from cows was used from a commercial slaughterhouse at Limoges, France according to normal rules.

AUTHOR CONTRIBUTIONS

JL performed a statistical analysis and wrote the manuscript. GP and AN provided the data. IL and M-PE-O made a high contribution to the structure of the paper. DP provided prediction of the MQ4 score. J-FH conceived the study. All authors contributed to the article and approved the submitted version.

Conflict of Interest:

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Author statement

Jingjing Liu performed statistical analysis and wrote the first draft of the manuscript, Marie-Pierre Ellies-Oury and Sghaier Chriki highly contributed to statistical analysis, Isabelle Legrand, Grzegorz Pogorzelski, Jerzy Wierzbicki, Linda Farmer, Declan Troy, and Rod Polkinghorne provided data from different experiments, Jean-François Hocquette conceived the study. All authors have read and agreed to the published version of the manuscript.

Carcass characteristics and beef quality of young grass-fed Angus x Salers bovines

This section is the version that was published in Foods. Liu, J., Ellies-Oury, M. P., Pannier, L., Gruffat, D., Durand, D., Noel, F., Sepchat, B., Legrand, I., Pannier, L., Prache, S. & Hocquette, J. F. (2022). Carcass Characteristics and Beef Quality of Young Grass-Fed Angus x Salers Bovines. Foods, 11 (16), 2493.

Conflicts of Interest:

The authors declare no conflict of interest.

Chinese consumers' attitudes and potential acceptance toward artificial meat

This section is the version that was published in Foods.

Liu, J., Hocquette, É., Ellies-Oury, M. P., Chriki, S., & Hocquette, J. F. (2021). Chinese Consumers' Attitudes and Potential Acceptance toward Artificial Meat. Foods,10(2), 353. Funding: This research received no external funding.

Institutional Review Board Statement:

This work is part of an international survey (available in different languages including English, French, Chinese, Portuguese, Arabic, Spanish and Italian) which was conducted following local guidelines based on the laws and regulations of the countries in which the research has been performed (in this case, based on practices of China agricultural University) and this includes ethical approval by ethics committees when required (such as in Brazil with number CAAE: 37924620.5.0000.5404).

Informed Consent Statement:

The process adhered entirely to the ESOMAR guidelines on ethical online research. This includes assurances that respondents gave informed explicit consent to take part in the survey and had their personal data protected. Indeed, after being informed of the objectives of the survey and how the given information will be used, all respondents gave their informed consent for inclusion of their answers before and after they participated in the study. Respondent details

Conflicts of Interest:

The authors declare that the current research was conducted in the absence of any commercial or financial relationships with any private company related to artificial meat production that could be construed as a potential conflict of interest.

Does the future of meat in France depend on cultured muscle cells? Answers from different consumer segments

This section is the version that was published in Meat Science.

Declaration of Competing Interest

This study was not financed by any external fund but only from inkind resources corresponding to the time spent by the researchers from the different institutions involved in this work.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi. org/10.1016/j.meatsci.2022.108776.