Julie Mardon Elise

Thiel Martine Laniau
email: mlaniau@gem.fr

Sijtsema Siet
email: siet.sijtsema@wur.nl

Zimmermann Dominique Karin
email: karin.zimmermann@wur.nl

Barjolle
email: barjolle@ethz.ch

J Mardon
email: julie.mardon@vetagro-sup.fr

E Thiel

M Laniau

S Sijtsema

K Zimmermann

D Barjolle

Motives underlying food consumption in the Western Balkans: consumers' profiles and public health strategies

Keywords: Western Balkan Countries, Consumers' food choice, Health and weight control, Fruit, Products with nutrition and health claims

Objectives This study aims to identify subgroups of consumers based on the health motives underlying their food choice in Western Balkan Countries. Methods The survey (n = 2943) was based on the Food Choice Questionnaire (FCQ) and elicited information on socio-demographic characteristics, consumption frequency of healthy food products, nutrition knowledge and impulsiveness. Analysis of the FCQ data focused on items of ''health and natural content'' and ''weight control'' factors to identify clusters.

Results

The biggest group of the sample was weight control and health-concerned individuals (34 %), mainly urban women older than 50. The second group of respondents (31 %) was moderately motivated about health and weight. A third group was health concerned but paid less attention to weight control (21 %), mainly comprising men and people living with children. The last group consisted of unconcerned young men (14 %) eating less fruit and showing higher impulsiveness. Conclusions Western Balkan consumers differ in the importance they attach to health and natural content and weight control. This insight is needed to target interventions.

Introduction

Following the demise of communism and the conflicts that affected the former Yugoslavia, Western Balkan Countries (WBC) underwent important changes, including a nutritional transition. This phenomenon affects both the structure and overall composition of the diet [START_REF] Popkin | Nutrition in transition: the changing global nutrition challenge[END_REF]: as a consequence of the economic development, people tend to consume more energy-dense and processed foods [START_REF] Drewnowski | The economics of obesity: dietary energy density and energy cost[END_REF][START_REF] Hawkes | Globalization, trade, and the nutrition transition. Glob Heal Pathway Evid Policy He FJ, Nowson Ca, MacGregor GA (2006) Fruit and vegetable consumption and stroke: meta-analysis of cohort studies[END_REF]) rather than fresh fruit and vegetables, cereals and roots.

These modifications are partly responsible for the high rate of non-communicable diseases that the WBC is currently experiencing [START_REF] Rechel | Public Health Reviews Health in south-eastern Europe : a troubled past, an uncertain future[END_REF]. For example, the age-standardized mortality rate linked to cardiovascular disease and diabetes ranges from 179 to 284 per 100,000 population in Balkan countries (World Health Organization 2009) and obesity prevalence ranges from 20.3 to 27 [START_REF] Branca | The challenge of obesity in the WHO European Region and the strategies for response[END_REF]). Thus, improving this situation for the population requires an urgent implementation of public health strategies in the region. However, it requires a good knowledge of consumers' health concerns and very little work has addressed this topic in WBC until now.

Consumer segmentation was suggested as a relevant first step in designing public health awareness campaigns: this involves targeting messages at particular groups of consumers [START_REF] Moorman | A model of consumers' preventive health behaviors: the role of health motivation and health ability[END_REF]. In former times, socio-demographic variables were widely used to identify these groups. Nevertheless, people in similar demographic groups were shown to have different behaviour and attitudes to health [START_REF] Slater | Health lifestyles: audience segmentation analysis for public health interventions[END_REF]. Nowadays, other variables might be considered to constitute homogeneous groups such as behaviour, misinformation, beliefs and communication patterns [START_REF] Snyder | Health communication campaigns and their impact on behavior[END_REF][START_REF] Onwezen | A crossnational consumer segmentation based on food benefits: the link with consumption situations and food perceptions[END_REF].

Health motivation [START_REF] Moorman | A model of consumers' preventive health behaviors: the role of health motivation and health ability[END_REF]) is indicative of the degree to which someone is willing to behave in a healthy manner. It was shown to significantly affect people's food behaviour as evidenced by the studies of [START_REF] Bhaskaran | Buyer beliefs, attitudes and behaviour: foods with therapeutic claims[END_REF] on people with high blood cholesterol level and [START_REF] Steinhilper | Health behavior change among breast cancer patients[END_REF] on patients with breast cancer. The Food Choice Questionnaire (FCQ) [START_REF] Steptoe | Development of a measure of the motives underlying the selection of food : the food choice questionnaire Department of Psychology[END_REF]) is an interesting tool to assess different motives underlying the selection of food, including health motivation and weight control (recognized as a factor to maintain healthiness). [START_REF] Carrillo | Main factors underlying consumers' food choice: a first step for the understanding of attitudes toward ''healthy eating[END_REF] acknowledged its relevance to a better understanding of dietary behaviour and the search for a healthy food consumption. The authors focused their analysis on the items linked to ''weight control'' to explore the relationship between consumers' behaviour regarding this motive and the consumption frequency of low-calorie and functional ingredients-added food.

Consumption frequency and perception of different kinds of foods are widely used to characterise consumers' behaviour [START_REF] Zandstra | Influence of health and taste attitudes on consumption of low-and high-fat foods[END_REF][START_REF] Pohjanheimo | Explaining the liking for drinking yoghurt: the role of sensory quality, food choice motives, health concern and product information[END_REF]. Among food products, fruit and products with nutritional and health claims, insofar as these claims can be substantiated, are two categories commonly considered as healthy and thus emblematic for persons keen to avoid chronic diseases. Fruit consumption is an important part of the recommendations of experts with regard to public health, given the associated health benefits reported in the literature (He et al. 2006;[START_REF] Vioque | Intake of fruits and vegetables in relation to 10-year weight gain among Spanish adults[END_REF]. The use of evidence-based claims for food products is fully relevant to the strategy on diet, physical activity and health of the WHO to encourage consumers to alter their dietary patterns towards healthy ones (World Health Organization 2004).

Nutritional knowledge may influence healthy food consumption [START_REF] Grunert | Nutrition knowledge, and use and understanding of nutrition information on food labels among consumers in the UK[END_REF]. For example, significant differences in willingness to try functional food were found among consumer groups with different levels of knowledge about the relationship between diet and diseases [START_REF] Ares | Influence of nutritional knowledge on perceived healthiness and willingness to try functional foods[END_REF]. Impulsiveness is another recognized important factor explaining food purchasing and consumption that could distinguish consumers [START_REF] Verplanken | Individual differences in impulse buying tendency: feeling and no thinking[END_REF]. It refers to rapid decision making with reduced inhibitory control of the consequences of the actions. Indeed, individuals having impulsive food behaviour would be more likely to consume unhealthy food [START_REF] Jasinska | Impulsivity and inhibitory control deficits are associated with unhealthy eating in young adults[END_REF].

The present work aims to determine health motivations of WBC consumers in food choice, since very few data are currently available on this topic. Participants were asked to rate their level of agreement for each item by endorsing the statement: ''It is important to me that the food I eat on a typical day…'' using a five-category scale (scored from ''I do not agree at all'' to ''I absolutely agree'') (Table 1).

The survey also elicited information on socio-demographic characteristics of respondents.

Self-defined health status was reported by asking respondents to rate their overall current state of health. This measurement is a valuable indicator of people's health status [START_REF] Eikemo | Educational health inequalities in former Yugoslavia: evidence from the South-East European Social Survey Project[END_REF].

Consumption frequency of two healthy food categories, fruits and products with health claims, was measured by self-reported questions. A portion of fruit was defined as approximately 80-100 g, for example, an apple, two spoons of fruit salad or one glass of freshly squeezed juice. Health claims were defined as ''claims that link a nutrient to a normal functioning of the body or a specific disease'' (e.g., ''calcium helps build strong bones. Adequate calcium throughout life, as part of a well-balanced diet, may reduce the risk of osteoporosis'') and presented by means of pictures. In the same way, intention to consume both targeted products was measured. Participants were asked the following question: ''How often would you consume products with health claims if they were more available to you (e.g., cheaper, more accessible in stores… etc.)?'' Nutritional knowledge was measured: an ''objective'' scale (true/false) including five items was used for fruits (''A medium sized apple contains more calories than a medium sized banana''; ''Eating fruit is only beneficial for the health when also vegetables are eaten daily''; ''Adding an extra piece of fruit to the diet results in weight loss''; ''It is generally recommended to eat at least 2 portions of fruit each day''; ''It is generally recommended to eat not more than five portions of fruit each day'') and for products with health claims (''Vitamin A helps absorb calcium''; ''Vitamin C fights colds and has anticancer power''; ''Carbohydrates convert to sugar and fuel the body''; ''Polyunsaturated fats are more likely to raise people's blood cholesterol level than saturated fats''; ''Risk of high blood pressure is most likely to be reduced by eating a diet with less salt'').

An impulsive food consumption scale composed of nine items and adapted from [START_REF] Bosnjak | Measuring impulsive buying tendencies in Croatia: towards a parsimonious measurement scale[END_REF] was used to assess perceived control of food behaviour. The questionnaire initially developed in English was submitted to steps of translation and back-translation to ensure correctness of the translation. Clusters were characterised in terms of socio-demographic data, consumption frequencies, knowledge of fruit or products with health claims and impulsiveness scores. Anova F tests with Tukey multiple comparisons and Chisquare tests were used to identify significant differences between clusters. The use of test values [START_REF] Lebart | Statistique exploratoire multidimentionnelle[END_REF] was useful to better understand each resulting cluster compared to global sample characteristics.

Results

Among items linked to ''health and natural content'' and ''weight control'' factors, ''keeps me healthy'' and ''contains natural ingredients'' were considered as the most important (Table 1). Conversely, ''is low in calories'' and ''is high in protein'' registered the lowest scores.

The two first principal components from PCA explained 38 and 10 % of the total variance, respectively, in the data set (Fig. 1). All studied items were positively correlated to the first principal component. Concerning the second one, the main contributions were those of the 4 items related to weight control, which were positively correlated to axis 2 but also the contributions of 3 items about health and natural content (''keeps me healthy'', ''is nutritious'' and ''makes me feel good'') which were negatively correlated to axis 2. Overall, the first axis conveyed the gradual importance accorded to health and natural content and weight control (from left to right) whereas axis 2 separated items linked to weight control (at the top) from those linked to health and natural content (standing lower).

The clustering identified four groups of consumers with distinctive motivational profiles. Cluster 1 clearly appeared highly attentive to both health and natural content and weight control, they allocated the highest scores to all items (Table 1) compared to other groups. Conversely, cluster 4 showed very poor attention to both factors, attributing the lowest scores to almost all items. Two other clusters (2 and 3) exhibited an intermediate attention to both factors. Cluster 3 gave higher ratings to items linked to the health and natural content factor than those linked to weight control. Cluster 2 paid more attention to items linked to weight control. The four clusters are described below and characterised by additional variables (Tables 2,3).

Cluster 1 included 34 % of consumers that were highly attentive to weight control and health and natural content. It comprised a higher proportion of females (59 %), more urban people (62 %) than average and people older than 55 were overrepresented (36 %). Their reported consumption frequencies of fruit and products with health claims were higher than the other consumers. The consumption frequency of fruit that respondents declared they would attain if it was more available to them would rise to 50 portions of fruit a month on average (?28.2 % of current consumption). Furthermore their consumption of products with health claims would rise by 75 % if more were available. No significant difference in knowledge or impulsiveness stood out for this cluster.

Cluster 2 consisted of 31 % of respondents. These consumers were less attentive to weight control and health and natural content than average but still considered these two factors to be important for them (mean scores above 3). This group contained a higher proportion of females (56 %) but also slightly more people with a high level of education than clusters 3 and 4. Their consumption frequency of products with health claims was a bit lower than in the sample but their expected frequency did not differ from average. There was no difference in knowledge or of impulsiveness in this cluster compared to average values in the sample.

Cluster 3 comprised approximately 21 % of the total sample. This group placed greater emphasis on its health and was less preoccupied by weight control. Cluster 3 contained a significantly higher proportion of males (57 %), of people between 35 and 54 years old (39 %), of people with children (43 %) and slightly fewer people with a high level of education compared to clusters 1 and 2. In this cluster, consumption frequencies of fruit and products with health claims were lower than in the whole sample, so as their expected ones, but they would consume more if more available (potential increase of 37.5 % for fruit and 83.3 % for functional food). Their knowledge about functional food was slightly higher than average and they were a bit less impulsive people.

Cluster 4 (14 % of respondents) contained people completely inattentive to their weight and even less to health. Compared with the whole sample, males (61 %), single people (35 %), 18-to 34-year-olds (42 %) and people without children (68 %) were overrepresented. Their consumption frequencies of fruit were significantly lower than in the whole sample (29 portions a month) as well as the expected consumption of fruit (although it could potentially rise by 37.9 %) and the same was true for products with health claims with a potential consumption below the average, although better availability would lead to an increase of 66.7 % above current consumption. Their knowledge about products with health claims was slightly lower than average in the sample and they showed an impulsiveness which scored higher.

Discussion

This study adds insights into motives of Western Balkan consumers, based on a large quantitative consumer survey undertaken in six WBCs in which different groups of consumers were segmented based on health-related motives. As previously undertaken in a quite similar approach [START_REF] Carrillo | Main factors underlying consumers' food choice: a first step for the understanding of attitudes toward ''healthy eating[END_REF], consumers were partitioned through a cluster analysis based on the scores they attributed to items linked to both ''health and natural content'' and ''weight control'' factors in the FCQ. Results describe different profiles of consumers identified as a function of their health motives, interesting information that was missing until now in the WBC. Health-concerned WBC consumers generally consumed more fruit and products with health claims than unconcerned respondents. The group comprising more parents than others was not preoccupied that much by weight control. Low motivated consumers exhibited a higher impulsiveness in food consumption than other groups. Consumers' nutrition knowledge poorly differed among groups.

As underlined by Milosevic et al. (2012), in WBC ''health and natural content'' is one of the strongest motivations underlying food choice after ''sensory appeal'' and ''purchase convenience''. Thus, in this region the ranking of motives is quite similar to those encountered in western European countries. One of the peculiarities of the region is the association made by the consumers between ''health'' and ''natural content''. This distinctive characteristic was previously reported in Belgian and Italian consumers [START_REF] Eertmans | The food choice questionnaire: factorial invariant over western urban populations?[END_REF]. The authors suggested that this merging showed that a food's natural content was viewed as essential to ensure its healthfulness.

Cluster 1 is ''the hard core'' of health-concerned consumers. In this group the link between food choice, weight control and health seems obvious. In this cluster, women, people over 50 and cohabiting or married people are overrepresented. This result is in line with previous findings reported in other European countries [START_REF] Hoefkens | European consumers' perceived importance of qualifying and disqualifying nutrients in food choices[END_REF]. Given the age of these consumers, it can be hypothesized that their high health motivation may be due to a higher perceived susceptibility to disease or being diagnosed with a disease [START_REF] Dutta | Health orientation and disease state as predictors of Online Health Support Group use[END_REF], although they do not declare a different state of health from others. As expected for health-concerned people, their consumption of fruit is quite high and their consumption of products with nutrition and health claims is greater than in other groups. This may be explained by a higher awareness of functional food ingredients, associated with health motivation [START_REF] Bornkessel | What determines ingredient awareness of consumers? A study on ten functional food ingredients[END_REF]. The willingness to consume more fruit and products with health claims is also higher. Logically, groups that greatly value health benefits in their food choices usually perceive fruit relatively more positively on a broader range of aspects [START_REF] Onwezen | A crossnational consumer segmentation based on food benefits: the link with consumption situations and food perceptions[END_REF]. Although the objective level of knowledge regarding nutrition issues is not very different from the average in the sample, we may suppose that such consumers will try harder to understand food labeling. Socio-demographic profiles from clusters 1 and 2 do not really differ; consumers moderately concerned about weight and health (cluster 2) only show a lower consumption of functional foods. In terms of health concerns, cluster 3 is not so different from cluster 2, but the concern for weight control is significantly lower. In the current study, the declared BMI distribution in this group did not differ from others. Cluster 3 includes more families with children. Given the impact that parents have on their children's behaviour [START_REF] Antonogeorgos | The mediating effect of parents' educational status on the association between adherence to the Mediterranean diet and childhood obesity: the PANACEA study[END_REF], their disinterest in health may be a risk of young and teenage children becoming overweight in the medium-to long-term future. Cluster 4 appears to be the [START_REF] Trienekens | Assessment of innovation and performance in the fruit chain: the innovationperformance matrix[END_REF]. Nutrition knowledge on fruits of unconcerned consumers did not differ from that of the other groups. These individuals exhibited greater impulsiveness in their food consumptions. Our results confirm those of [START_REF] Verplanken | Individual differences in impulse buying tendency: feeling and no thinking[END_REF] who suggested that impulsiveness measurement would be an interesting criterion to distinguish consumers. In contrast with other groups, it seems that the link between health and a well-balanced diet is largely underestimated by these young men. Mean scores of knowledge remain both quite low for the whole sample (3.33 for products with nutrition and health claims and 2.81 for fruits). In this survey, knowledge regarding specific nutritional value for each category of products cannot be identified as one of the main drivers of consumption. Indeed, individuals from cluster 1 eat more fruits and products with claims without appearing to have better knowledge of these products than other groups. However, we did not investigate the level of usage of food labels. It was previously suggested that usage is mainly related to interest in healthy eating, whereas understanding of nutrition information on food labels is mainly related to nutrition knowledge [START_REF] Grunert | Nutrition knowledge, and use and understanding of nutrition information on food labels among consumers in the UK[END_REF]. Our results run counter to the view that understanding of nutrition information is affected by differences in interest in healthy eating and nutrition knowledge and by social status. The same authors demonstrated that lack of use of nutrition information is a question of not only understanding, but also motivation. The present work is in agreement with this finding since it seems that the level of consumption of products with claims is linked with the level of health motives in our sample.

From a public health perspective, particular attention should be paid to specific groups of WBC consumers:

-First, it could be interesting to rely on cluster 1 (urban women) which can be considered as ahead in healthy food consumption awareness. Health motivation was shown to influence consumers' information strategies [START_REF] Bornkessel | What determines ingredient awareness of consumers? A study on ten functional food ingredients[END_REF]. In particular, people having strong health motives in food choices are likely to search the World Wide Web for health information. Indeed, intrinsic health concern is a positive predictor of participation in an online health community [START_REF] Dutta | Health orientation and disease state as predictors of Online Health Support Group use[END_REF]. Thus, women with high health motivation and a good level of education could be sensitive to messages on health websites. -Parents with young children (cluster 3) may influence the future food behaviour of their offspring. This group was found to have a slightly lower educational level and to be less concerned about weight control than clusters 1 and 2. However, high parental education status was found to offer protection against childhood obesity and to be positively associated with adherence to the Mediterranean diet [START_REF] Antonogeorgos | The mediating effect of parents' educational status on the association between adherence to the Mediterranean diet and childhood obesity: the PANACEA study[END_REF].

In a region already characterised by excessive obesity, it is important to communicate on weight control, since after a period of food shortage the population is liable to increase its food intake as their countries develop (European Environment Agency 2010). Thus, when attempting to prevent childhood obesity, health professionals should take into account parents' views on diet since these views are likely to affect the way they raise their children [START_REF] Lopez-Dicastillo | Parents' contrasting views on diet versus activity of children: implications for health promotion and obesity prevention[END_REF]. Another strategy proposed by [START_REF] Olafsdottir | Young children's screen habits are associated with consumption of sweetened beverages independently of parental norms[END_REF] could be to influence children's dietary patterns by means of TV, which has been shown to influence children's behaviour independently of parental norms. -Finally, a specific focus should be made on cluster 4 (single young men), totally unconcerned by weight and health. The challenge is specifically to raise young people's awareness of the impact of food choice and eating habits on their health. Since this group is more impulsive and possibly more governed by emotions it might be more efficient to target them by ''appealing to emotion rather than intellect'' [START_REF] Fitzgibbon | Communicating healthy eating: lessons learned and future directions[END_REF]. It would also be interesting to determine whether these consumers are internet users or not. If they are, the internet, and more specifically social networks, might be useful for targeting them, since social networks were reported to influence healthy eating [START_REF] Nam | Social networks and future direction for obesity research: a scoping review[END_REF].

Other ways have been suggested to reach unconcerned consumers, such as increasing shelf-space in supermarkets or increasing the variety of products [START_REF] Visschers | A consumer segmentation of nutrition information use and its relation to food consumption behaviour[END_REF]).

Thus, this exploratory work provides new data to improve understanding of consumers' health motives in food choice in the WBC. Particularly, it provides information on the different profiles of consumers and recommendations to create suitable public health messages.

Some limitations of the study have to be mentioned. In particular, declared overweight (36.9 %) and obesity (12.3 %) rates do not match WHO data [START_REF] Branca | The challenge of obesity in the WHO European Region and the strategies for response[END_REF]). In fact, the use of self-reported weight and height data is subject to well-known biases due to a trend to under-report their weight which results in an underestimation of the true prevalence of obesity in the studied population [START_REF] Gil | The determinants of misreporting weight and height: the role of social norms[END_REF]. Hence it would have been interesting to combine health motivation data with measured BMI data.

Further advice can be given to implement specific strategies: an approach involving policy makers, producers, the food industry and consumers can be suggested [START_REF] Fitzgibbon | Communicating healthy eating: lessons learned and future directions[END_REF]. Involvement of a broad set of stakeholders has previously been suggested as a key factor regarding nutritional communication to promote messages that everyone embraces and supports [START_REF] Ferna ´ndez-Celemı ´n | Mapping public health nutrition awareness campaigns across Europe[END_REF]. This may also be an efficient way to promote fruit availability and accessibility [START_REF] Lucan | Perceptions of the food environment are associated with fast-food (not fruit-and-vegetable) consumption: findings from multi-level models[END_REF], which currently do not seem to meet consumers' expectations and have previously been identified as a major problem with fruit marketing to meet consumption recommendations [START_REF] Onwezen | Which perceived characteristics make product innovations appealing to the consumer? A study on the acceptance of fruit innovations using cross-cultural consumer segmentation[END_REF]. Moreover, since the diet is a complex concept and that habits are hard to change, campaigns might adopt an entire lifestyle approach, instead of trying to make people change a bad habit [START_REF] Dutta-Bergman | Psychographic profiling of fruit and vegetable consumption: the role of health orientation[END_REF]. Lessons from marketing such as the use of a brand, transposed to public health strategies by conveying an ''enviable and fun lifestyle'' [START_REF] Fitzgibbon | Communicating healthy eating: lessons learned and future directions[END_REF]) and ''develop a culture of wellness'' [START_REF] Fernstrom | Communication strategies to help reduce the prevalence of noncommunicable diseases: proceedings from the inaugural IFIC Foundation Global Diet and Physical Activity Communications Summit[END_REF]) could be an interesting direction with the promotion of enviable subjective social norms and family models. Another perspective highlighted by this work is the need for future research regarding nutrition knowledge and use of nutritional information in WBC. Nevertheless, it can already be suggested that the improvement of knowledge on healthy foods among the population should be a goal.

Fig. 1

 1 Fig. 1 Principal component analysis on the 14 Food Choice Questionnaire items related to ''Health and natural content'' and ''Weight control'' factors (Western Balkan Countries in 2010). a Plot of variables on the correlation circle; W stands for weight control and HN for health and natural content. b Plot of individuals (identified into clusters) on the factorial map

  The research was part of the European Research program FOCUS-BALKANS. A large quantitative consumer survey was carried out in six WBCs to better understand consumers' behaviour and expectations. The purpose of this study is twofold:

	concern). In this study, the FCQ developed by Steptoe
	(Steptoe et al. 1995) in England did not prove totally in-
	variant in the WBC. Therefore, an adaptation with 8 factors
	(1: Health and natural content, 2: Mood, 3: Preparation
	convenience, 4: Purchase convenience, 5: Sensory appeal,
	6: Price, 7: Weight control, 8: Familiarity and ethical
	concern) was proposed (Milos ˇevic ´et al. 2012).
	-To identify different WBC consumer groups on the
	basis of their health motives underlying food
	consumption.
	-To explain the health motives in food consumption
	through a characterisation of clusters by socio-demo-
	graphic, consumption, nutrition knowledge and
	impulsiveness variables.
	Methods
	General description of the survey
	A total of 3085 consumers from Bosnia-Herzegovina,
	Croatia, Former Yugoslav Republic of Macedonia, Mon-
	tenegro, Serbia and Slovenia were included in the survey.
	Data were gathered via IPSOS country offices in the re-
	gion. The sample was stratified to optimize geographical
	and cultural uniformity. Then, weighting procedures re-
	garding age, sex and type of settlement classes were
	applied to the sample to correctly represent the population
	as described by Milos ˇevic ´et al. (2012). Interviews were
	carried out face-to-face at respondents' homes.
	Questionnaire
	A FCQ measuring the reported importance of various
	motivational factors underlying food choice was given to
	participants. It consists of 36 items related to nine factors
	likely to influence food choice (1: Health, 2: Mood, 3:
	Convenience, 4: Sensory appeal, 5: Natural content, 6:
	Price, 7: Weight control, 8: Familiarity, 9: Ethical

Table 1

 1 Mean scores and standard deviations of Food Choice Questionnaire items loading onto health and natural content and weight control factors for the four clusters of consumers identified in Western Balkan Countries in 2010

	Factors and items	Total sample	Cluster 1		Cluster 2		Cluster 3		Cluster 4		Statistical
		(n = 2943;	(n = 990;	(n = 928;	(n = 605;	(n = 420;	significance
		100 %)		33.6 %)		31.5 %)		20.6 %)		14.3 %)
		Mean	SD	Mean	SD	Mean	SD	Mean	SD	Mean	SD	(ANOVA)
	Health and natural content
	Keeps me healthy	4.44	0.76	4.87 a	0.36	4.15 b	0.67	4.81 a	0.43	3.54 c	0.94	0.000***
	Contains natural ingredients	4.40	0.79	4.84 a	0.40	4.28 c	0.65	4.50 b	0.74	3.45 d	0.95	0.000***
	Is nutritious	4.34	0.78	4.70 a	0.59	4.02 b	0.69	4.66 a	0.59	3.73 c	0.92	0.000***
	Contains a lot of vitamins and minerals 4.29	0.86	4.82 a	0.46	4.04 c	0.69	4.59 b	0.57	3.16 d	1.00	0.000***
	Makes me feel good	4.21	0.89	4.73 a	0.55	3.82 c	0.75	4.60 b	0.61	3.26 d	1.00	0.000***
	Good for my skin/teeth/hair…	4.17	0.98	4.77 a	0.57	3.85 c	0.78	4.56 b	0.72	2.94 d	1.04	0.000***
	Contains no artificial ingredients	4.15	0.99	4.77 a	0.53	3.93 c	0.84	4.34 b	0.86	2.93 d	1.00	0.000***
	Keeps me awake/alert	4.10	0.96	4.73 a	0.53	3.79 c	0.78	4.38 b	0.76	2.90 d	0.98	0.000***
	Is high in protein	3.87	1.00	4.43 a	0.79	3.58 c	0.85	4.05 b	0.84	2.87 d	1.02	0.000***
	Is high in fibre and roughage	3.81	1.01	4.47 a	0.71	3.70 b	0.78	3.63 b	0.98	2.75 c	1.02	0.000***
	Weight control
	Contains no additives	3.90	1.07	4.55 a	0.72	3.84 b	0.88	3.58 c	1.15	2.93 d	1.03	0.000***
	Is low in fat	3.87	1.04	4.66 a	0.56	3.93 b	0.75	3.10 c	1.01	2.95 d	1.01	0.000***
	Helps me control my weight	3.64	1.18	4.45 a	0.81	3.57 b	0.97	3.15 c	1.19	2.62 d	1.06	0.000***
	Is low in calories	3.47	1.18	4.31 a	0.87	3.65 b	0.86	2.44 c	0.92	2.55 c	0.99	0.000***

a,b,c,d

Values with same letters on the same line do not differ significantly (P \ 0.05) according to ANOVA and Tukey's multiple range test fied. A consolidation to stabilize the clusters was added by combination of a k-means algorithm using AHC clusters' centroids to initiate iterations. The value of the consolidation is to combine the advantages of the two complementary clustering methods whilst limiting their respective drawbacks.

Table 2

 2 Demographic and socio-economic characteristics and declared state of health of the whole sample and of the four clusters of consumers identified in Western Balkan Countries in 2010

		Total sample (%)	Cluster 1 (%)	Cluster 2 (%)	Cluster 3 (%)	Cluster 4 (%)	Statistical
		(n = 2943)	(n = 990)	(n = 928)	(n = 605)	(n = 420)	significance
							(Chi square tests
							on numbers)
	Gender
	Male	48.0	41.0 b	43.9 b	56.5 a	61.1 a	0.000***
	Female	52.0	59.0 a	56.1 a	43.5 b	38.9 b
	Marital status
	Single, not living with a partner	25.5	23.4 b	24.7 b	23.6 b	34.8 a	0.001***
	Cohabiting/married	60.3	61.9 a	61.8 a	61.2 a	52.0 b
	Previously married (divorced, widowed) 14.2	14.6 a	13.5 a	15.2 a	13.2 a
	Age
	18-34	32.4	30.5 b	31.3 b	30.9 b	41.5 a	0.000***
	35-54	34.2	33.2 b	33.5 b	39.0 a	30.7 b
	55?	33.4	36.2 a	35.2 a	30.1 b	27.9 b
	Child
	Yes	37.2	37.0 b	35.8 b	42.9 a	32.5 b	0.005***
	No	62.8	63.0 a	64.2 a	57.1 b	67.5 a
	Settlement type
	Urban	58.5	61.5 a	57.7 a,b	55.5 b	57.5 a,b	0.097*
	Rural	41.5	38.5 b	42.3 a,b	44.5 a	42.5 a,b
	Last finished education
	Elementary or less	32.6	31.0 a	32.6 a	35.0 a	33.1 a	0.074*
	Secondary	51.1	51.8 a	49.0 a	51.5 a	53.5 a
	Higher or university	16.3	17.2 a,b	18.4 a	13.4 c	13.5 b,c
	Declared standard of the household
	Bad	16.4	13.9 b	16.8 a,b	16.9 a,b	20.5 a	0.055*
	Moderate	50.1	51.0 a,b	51.7 a	49.5 a,b	45.6 b
	Good	33.5	35.1 a	31.5 a	33.5 a	33.9 a
	Current occupation
	Employed in a company	33.3	33.9	34.0	31.1	33.4	0.131 (ns)
	Self-employed	9.1	7.8	8.2	10.5	12.0
	Unemployed	57.7	58.4	57.8	58.4	54.7
	Income
	Low	24.3	22.2	26.5	22.9	26.4	0.251 (ns)
	Medium	30.0	29.3	31.6	31.7	25.7
	High	11.3	11.9	10.4	11.4	11.4
	NA	34.4	36.6	31.4	34.0	36.5
	Declared current state of health
	Bad	9.7	9.0	10.2	8.6	11.8	0.353 (ns)
	Moderate	30.0	30.7	30.8	27.8	29.7
	Good	60.3	60.4	59.0	63.6	58.5
	BMI
	Underweight (\18.4)	2.2	2.2	2.3	1.6	2.8	0.255 (ns)
	Normal (18.4-24.9)	46.6	44.7	48.0	48.3	45.7
	Overweight (25.0-29.9)	36.9	38.3	35.1	38.5	35.1
	Obese (C30)	12.3	12.9	12.9	9.3	13.9
	NA	2.0	2.0	1.7	2.3	2.5
	NA not available, ns non significant

*** p \ 0.01, ** p \ 0.05, * p \ 0.10 a,b,c,d Values with same letters on the same line do not differ significantly (p \ 0.05) according to ANOVA and Chi square test)

Table 3

 3 Characterisation of clusters of consumers by variables of interest related to fruits and products with health claims as well as impulsiveness (data collected in Western Balkan Countries in 2010) Values with same letters on the same line do not differ significantly (p \ 0.05) according to ANOVA and Tukey's multiple range test least motivated consumer group. In this smaller cluster, young single males are overrepresented. Fruit consumption is lower than in other groups. This low consumption might be caused by price, availability, and a lack of awareness about health benefits

		Total sample	Cluster 1	Cluster 2	Cluster 3	Cluster 4	Statistical
		mean (SD)	mean (SD)	mean (SD)	mean (SD)	mean (SD)	significance
							(ANOVA)
	Consumption frequency
	(number of portions/month)
	Fruits	35 (23)	39 a (23)	36 a (23)	32 b (23)	29 c (23)	0.000***
	Products with health claims	13 (17)	16 a (19)	12 b (15)	12 b (16)	12 b (16)	0.000***
	Intention to consume
	(number of portions/month)
	Fruits	47 (22)	50 a (21)	47 a,b (21)	44 b (22)	40 c (24)	0.000***
	Products with health claims	24 (23)	28 a (24)	23 b (22)	22 b (23)	20 b (21)	0.000***
	Knowledge
	Fruits	2.81 (1.08)	2.77 (1.07)	2.83 (1.10)	2.81 (1.10)	2.86 (1.04)	0.410 (ns)
	Products with health claims	3.33 (0.85)	3.35 a,b (0.79)	3.30 a,b (0.92)	3.40 a (0.76)	3.24 b (0.96)	0.019**
	Impulsiveness score	26.43 (5.20)	26.19 b (5.50)	26.49 b (4.81)	26.04 b (5.51)	27.39 a (4.73)	0.000***
	ns non significant
	*** p \ 0.01, ** p \ 0.05, * p \ 0.10
	a,b,c,d

Acknowledgments This article was written in the framework of the EU research project FOCUS BALKANS. The FOCUS BALKANS consortium gratefully acknowledges the financial contribution from the European Community under the Seventh Framework Programme (Grant agreement 212579). The authors thank Zaklina Stojanovic for her contribution.


A Aït-Kaddour

A Thomas

J Mardon

S Jacquot

A Ferlay

D Gruffat

Potential of fluorescence spectroscopy to predict fatty acid composition of beef

Keywords:

The present study aimed to evaluate and compare the ability of front face (FFFS) and synchronous fluorescence spectroscopy (SFS) to predict total fat and FA composition of beef LT muscles coming from 36 animals of 3 breeds (Angus, Limousin and Blond d'Aquitaine). The regression models were performed by using Partial Least Square (PLS) method. In spite of the low number of samples used, the results of this preliminary study demonstrated the ability of fluorescence spectroscopy to predict meat lipids. Nonetheless, the results suggested that the fluorescence spectroscopy is more suited to measure SFA (R 2 p ≥ 0.66; RPD ≥ 2.29) and MUFA (R 2 p ≥ 0.48; RPD ≥ 1.49) than PUFA (R 2 p ≤ 0.48; RPD ≤ 1.63). Moreover, R 2 and RPD factors obtained with FFFS were greater compared to the ones obtained with SFS suggesting that FFFS is more adapted to measure lipid composition of beef meat.

Introduction

Fat and fatty acids (FAs) are major contributors of meat nutritional value. Indeed, numerous studies have highlighted the link between lipid intake and potential health effects in humans [START_REF] Hooper | Effect of reducing total fat intake on body weight: Systematic review and meta-analysis of randomised controlled trials and cohort studies[END_REF][START_REF] Riccardi | Dietary fat, insulin sensitivity and the metabolic syndrome[END_REF][START_REF] Wahrburg | What are the health effects of fat[END_REF]. Even if the issue remains controversial [START_REF] Mcafee | Red meat consumption: An overview of the risks and benefits[END_REF], most of the dietary recommendations advise to lower the contribution of fat and saturated FA (SFA) to daily energy intakes while increasing the intake of omega-3 polyunsaturated FA (PUFA) to reduce the risk of cardiovascular disease [START_REF] Gidding | Dietary recommendations for children and adolescents: A guide for practitioners: Consensus statement from the American heart association[END_REF]Siri-tarino, Sun, Hu, & Krauss, 2010a, 2010b). Furthermore, some trans-fatty acids (C18:1 trans-11) present within meat were shown to have no cholesterol-raising effect [START_REF] Chardigny | Do trans fatty acids from industrially produced sources and from natural sources have the same effect on cardiovascular disease risk factors in healthy subjects? Results of the trans fatty acids collaboration (TRANSFACT) study[END_REF]. There is also emerging evidence that conjugated linoleic acid (CLA) isomer cis-9 trans-11 is associated with prevailing beneficial health effects for humans [START_REF] Benjamin | Conjugated linoleic acids as functional food: An insight into their health benefits[END_REF].

Thus, several strategies, mainly modulating genetics and dietary factors, are currently investigated to enhance meat FA composition [START_REF] Scollan | Enhancing the nutritional and health value of beef lipids and their relationship with meat quality[END_REF][START_REF] Wood | Fat deposition, fatty acid composition and meat quality: A review[END_REF]. To support this effort, fluorescence analysis was suggested to be a rapid and efficient tool useful in the quality estimation of meat products [START_REF] Karoui | Fluorescence spectroscopy measurement for quality assessment of food systems-A review[END_REF][START_REF] Swatland | Autofluorescence of adipose tissue measured with fibre optics[END_REF]. Front-face fluorescence spectroscopy (FFFS) notably allows the investigation of solid food samples in the wavelength range of 200-750 nm. In this case, excitation and emission spectra are acquired at fixed wavelength determined as a function of the fluorophores present in the analyzed food matrix, namely tryptophan, vitamin A, riboflavin, lipid oxidation products and NADH in meat products. In a variant acquisition mode, synchronous fluorescence spectroscopy (SFS), spectra results from simultaneously scanning both the excitation and emission monochromators keeping a fixed wavelength interval between them [START_REF] Karoui | Fluorescence spectroscopy measurement for quality assessment of food systems-A review[END_REF]. Firstly initiated by [START_REF] Lloyd | Synchronized excitation of fluorescence emission spectra[END_REF], SFS allows the consideration of the whole fluorescence landscape meaning it retains information related to several fluorophores of the food matrix. Therefore, it could be more suitable for the analysis of complex multi-component samples as compared to conventional FFFS. Fluorescence spectroscopy has been mainly used to characterize quality traits of meat products, such as sensory properties [START_REF] Dufour | Les spectres de fluorescence frontale: une empreinte digitale de la viande[END_REF][START_REF] Olsen | Analysis of the early stages of lipid oxidation in freeze-stored pork back fat and mechanically recovered poultry meat[END_REF][START_REF] Swatland | UV fiber-optic probe measurements of connective tissue in beef correlated with taste panel scores for chewiness[END_REF]. Extensive works using fluorescence spectroscopy associated with chemometrics were also carried out to authenticate and classify meat products as a function of their rearing conditions [START_REF] Gatellier | Use of a fluorescence front face technique for measurement of lipid oxidation during refrigerated storage of chicken meat[END_REF], manufacturing process, storage and cooking conditions [START_REF] Gatellier | Use of meat fluorescence emission as a marker of oxidation promoted by cooking[END_REF][START_REF] Møller | Monitoring chemical changes of dry-cured Parma ham during processing by surface autofluorescence spectroscopy[END_REF] or microbial spoilage (Aït-Kaddour, [START_REF] Aït-Kaddour | Development of a portable spectrofluorimeter for measuring the microbial spoilage of minced beef[END_REF]. Quantitative evaluations of some meat components, namely collagen and heterocyclic aromatic amine contents, were developed using multivariate regression techniques in meat products [START_REF] Egelandsdal | Autofluorescence quantifies collagen in sausage batters with a large variation in myoglobin content[END_REF][START_REF] Sahar | Potential of synchronous fluorescence spectroscopy coupled with chemometrics to determine the heterocyclic aromatic amines in grilled meat[END_REF][START_REF] Wold | Quantification of intramuscular fat content in beef by combining autofluorescence spectra and autofluorescence images[END_REF]. However, very few data is currently available regarding the feasibility of using fluorescence spectroscopy to evaluate total fat content in meat. Wold and collaborators investigated the ability of FFFS to determine fat content in beef and exhibited fair models of cross validated prediction (RMSECV = 1.89%, R 2 = 0.71 and RMSECV = 1.10%, R 2 = 0.71, respectively) [START_REF] Wold | Quantification of intramuscular fat content in beef by combining autofluorescence spectra and autofluorescence images[END_REF][START_REF] Wold | Quantification of connective tissue (hydroxyproline) in ground beef by autofluorescence spectroscopy[END_REF]. Another work reported poor prediction models (R 2 = 0.57) when testing the potential of fluorescence spectroscopy to evaluate intramuscular fat content in porcine meat [START_REF] Brondum | Prediction of water-holding capacity and composition of porcine meat by comparative spectroscopy[END_REF]. Surprisingly, to our knowledge, no work investigating the potential interest of fluorescence spectroscopy in assessing fatty acid composition of meat was reported so far. Yet, an advantage of fluorescence detection compared to absorption measurements (e.g., IR) is the greater sensitivity (100-1000 times) achievable, meaning that fluorescent compounds can be investigated at very low concentration levels (in the parts per billion range).

It would be therefore interesting to determine in what extent fluorescence spectroscopy would be useful to predict the specific FA composition of meat. Similar approaches were carried out using near infrared reflectance spectroscopy. Globally, this tool appeared to accurately predict the concentration of some prominent FA and FA groups such as saturated, branched and monounsaturated FA in beef, poultry and lamb meat [START_REF] Guy | Prediction of lamb meat fatty acid composition using near-infrared reflectance spectroscopy (NIRS)[END_REF][START_REF] Prieto | Use of near infrared spectroscopy for estimating meat chemical composition, quality traits and fatty acid content from cattle fed sunflower or flaxseed[END_REF][START_REF] Sierra | Prediction of the fatty acid composition of beef by near infrared transmittance spectroscopy[END_REF].

Thus, the purpose of this study is to evaluate and compare the ability of front face and synchronous fluorescence spectroscopy to predict total fat and FA composition of beef.

Materials and methods

Animals and meat samples

The muscle samples came from 36 bulls of 3 genotypes with varying lipogenesis capacities (Angus N Limousin N Blond d'Aquitaine). All the details on the experimental design and diets for the experiment were previously described by [START_REF] Gruffat | Breed and dietary linseed affect gene expression of enzymes and transcription factors involved in n-3 long chain polyunsaturated fatty acids synthesis in longissimus thoracis muscle of bulls[END_REF]. At the end of the experimental period, the bulls were slaughtered in the experimental abattoir of INRA (Saint-Genès-Champanelle, France). Samples (∼100 g) of the Longissimus thoracis (LT) muscle of the 36 animals were collected at 24 h post mortem from the 10th thoracic rib on the right side of the ribbed carcass. Muscle samples were cut into small cubes (1 cm 3 ), immediately frozen in liquid nitrogen and stored at -80 °C. Just before analysis, the frozen samples of the LT muscle were ground into fine and homogeneous powders in liquid nitrogen with a mixer mill (Retch MM 301, Hann Germany).

Fatty acid analysis by gas liquid chromatography

Total lipids were extracted according to the method of [START_REF] Folch | A simple method for the isolation and purification of total lipids from animal tissues[END_REF] by mixing the LT muscle powder with a 2/1 chloroform/methanol mixture (vol/vol) and quantified by gravimetry. Fatty acid extraction and transmethylation into fatty acid methyl esters (FAME) were subsequently performed according to the method of [START_REF] Bauchart | Effects of diets supplemented with oil seeds and vitamin E on specific fatty acids of rectus abdominis muscle in Charolais fattening bulls[END_REF]. Fatty acid methyl ester analysis was performed with Gas Liquid Chromatography (GLC) using a Peri 2100-chromatography system (Perichrom Society, Saulx-les-Chartreux, France) fitted with a CP-Sil 88 glass capillary column (Varian, Palo Alto, CA; length = 100 m; diam. = 0.25 mm). The carrier gas was H 2 and the oven and flame ionization detector temperatures described by [START_REF] Scislowski | Dietary linoleic acid-induced hypercholesterolemia and accumulation of very light HDL in steers[END_REF] were used. Total FAs were quantified using C19:0 as an internal standard. The identification of each individual FAME and the calculation of the response coefficients for each individual FAME were performed using the quantitative mix C4-C24 FAME (Supelco, Bellafonte, PA).

Fluorescence spectroscopy

Samples fluorescence came from the same powder as that used for fatty acid analysis (i.e. LT muscle collected at 24 h post mortem, immediately frozen in liquid nitrogen and stored at -80 °C). Frozen samples were ground into fine and homogeneous powders in liquid nitrogen and stored at -80 °C until analyses. Before acquisitions, the homogeneous powder of ground LT muscles was thawed during 1 h00 at 20 °C. Then a proportion 3 g of meat powder was placed between a powder sample holder and a quartz cell and mounted in a solid sample holder. Before fluorescence acquisition the sample was visually controlled to ensure that all the measurement window was totally covered with meat sample. Fluorescence spectra were recorded in two excitation modes successively on the same sample, classical excitation mode (FFFS: Front Face Fluorescence Spectra) and synchronous excitation mode (SFS: Synchronous Fluorescence Spectra) by using a FluoroMax-4 spectrofluorometer (Jobin-Yvon, Longjumeau, France) equipped with a solid sample holder with an incidence angle of the excitation radiation set at 60°in order to minimize reflected light, scattered radiation and depolarization phenomena. For SFS, the excitation wavelength (λ ex ) and emission wavelength (λ em ) are scanned simultaneously (synchronously), usually maintaining a constant wavelength interval, named offset or Δ λ, between λ ex and λ em . In this study, six Δλ (20, 40, 60, 80, 100, and 120 nm) were used. All the excitation fluorescence spectra were recorded between 250 and 550 nm.

For FFFS, the emission spectra were recorded at 305-400, 340-540, 360-570, 400-650 and 410-700 nm after excitation at 290, 322, 335, 350 and 382 nm respectively. Those excitation spectra mainly addressed the fluorescence of tryptophan, collagen/pyridinolin/riboflavin, NADH, vitamin A, and vitamin E, respectively. The two first fluorophores were chosen because they are clearly liposoluble while the other fluorophores were chosen because their fluorescence properties can be influenced by their environment [START_REF] Mazerolles | Chemometric methods for the coupling of spectroscopic techniques and for the extraction of the relevant information contained in the spectral data tables[END_REF]. SFS and FFFS were recorded in duplicates for each sample giving a total of 792 spectra recorded.

Data pre-processing

The proper choice of pre-processing is difficult to assess prior to model validation but pre-processing should maintain or decrease the effective model complexity. So, in order to improve the calibration models different pre-processing methods were investigated. Firstly, noise of the spectral datasets was reduced by using the Savitzky-Golay method with a polynomial order and a filter width of 2 and 12 respectively. Secondly, the scattering effects were minimized by using Standard Normal Variate (SNV), Multiple Scattering Correction (MSC) or by area normalization (reducing the area under each spectrum to a value of 1: AREA). These pre-processing techniques are desired to reduce the (physical) variability between samples due to scatter. Data preprocessing were performed by using the PLS-Toolbox v.7.5 (Eigenvector Research) for MATLAB R2013b.

Partial least square regression

Partial Least Square Regression (PLS-R) models were validated by independent datasets. The initial data sets were divided into two sets, 70% of the samples were used for the calibration and, 30% for the validation by using the Nearest Neighbor Thinning method proposed in the PLS-Toolbox v.7.5 (Eigenvector Research) that permits to select validation samples which best fill out all covariance space. The models performance can be evaluated by different factors. [START_REF] Williams | Near-infrared technology: Getting the best out of light[END_REF] defines 7 levels of model accuracy based on the R 2 values obtained for prediction. Nonetheless, considering the R 2 can conduct to over estimation of the model accuracy. In order to have a clear idea of model performance other statistics such as the Ratio of Performance to Deviation (RPD = SD/RMSEP) factor was considered. Five levels of model accuracy based on the RPD values obtained for prediction were considered (Table 1).

PLS-R analyses were performed by using the PLS-Toolbox v.7.5 (Eigenvector Research) for MATLAB R2013b.

Results

Fatty acid composition measured using the GLC reference method

The means, ranges, SD (standard deviation) and CV (coefficient of variation) of LT intramuscular lipid and FA concentrations are presented in Table 2. Mean SFA and MUFA were the most abundant FA groups in LT muscle and their contents were similar (600.05 mg/100 g and 571.25 mg/100 g respectively). On the other hand, mean PUFA contents were 3 times lower (202.95 mg/100 g). Palmitic (16:0) and oleic (18:1 Δ9 cis) acids were on average the most abundant individual FAs (299.68 mg/100 g of fresh muscle, ranging from 50.64 to 1215.19 mg/100 g and 388.47 mg/100 g of fresh muscle, ranging from 41.26 to 1512.13 mg/100 g, respectively). Stearic acid (18:0) averaged 216.1 mg/100 g, ranging from 41.26 to 631.1 mg/100 g. Among PUFA, n-6 PUFA were the most represented in LT muscle (159.95 mg/100 g) with linoleic acid (18:2 n-6) as major FA (101.84 mg/100 g). In contrast, n-3 PUFA contents were low (35.2 mg/100 g), mainly represented by linolenic acid (18:3 n-3, 20.6 mg/100 g). All total PUFAs categories (except trans PUFA n-6 andtotal CLA) andindividual 20:3 n-3, 20:4 n-3 and22:6 n-3 The fluorescence spectra obtained after excitation at 290 nm exhibited a peak at 334 nm. Emission fluorescence spectra obtained after excitation at 322 nm presented peaks at 353, 379, 527 nm and a large band from 418 to 524 nm with a maximum at 466 nm. The emission spectra obtained after excitation at 335 nm presented different bands, a high one at 369 nm, a large one between 420 and 543 nm with a maximum at 458 nm and small ones at 378 and 551 nm. Excitation of meat samples at 350 nm resulted in an emission fluorescence spectrum presenting a broad band between 420 and 543 nm with a maximum at 466 nm and two small peaks at 573 and 596 nm. Finally, the fluorescence spectra obtained after excitation at 382 nm exhibited peaks at 419, 596, 628 nm and a broad band between 428 and 546 nm with a maximum at 468 nm.

Fig. 2 presented average front face synchronous spectra recorded at the different Δλ (20, 40, 60, 80, 100 and 120 nm). The spectra recorded with an offset of 20 nm presented one prominent peak at 327 nm (emission 347 nm) and smaller one between 449 and 525 nm (emission between 469 and 545 nm) with a maximum at 491 nm (511 nm). This band shifted to lower wavelength 294, and 293 nm when the spectra are recorded respectively with offsets 80 and 60 nm. The synchronous spectra of 100 nm offset presents one large band cantered at 279 nm. The offset of 120 nm permits to obtain spectra with bands at 271 nm (emission 391 nm), 325 (emission 445 nm) and 356 nm (emission 476 nm). Here, the band around 290 nm, which was prominent with the previous offsets (20 to 80 nm), disappears. The band located between 450 and 525 nm seems to disappear when the offset value increases.

Prediction of lipid contents with FFFS

The PLS regression statistics corresponding to the selected equations of the best models developed from FFFS are summarized in Table 3.

With FFFS, the best predictive models were obtained for total lipid content (RPD = 2.67), total FA (RPD = 2.61), SFA (RPD = 2.65), linear SFA (RPD = 2.65), MUFA (RPD = 2.62), cis MUFA (RPD = 2.77) and for individual unsaturated and mono-unsaturated FA, C14:0 (RPD = 2.88), C16:0 (RPD = 2.61), C18:0 (RPD = 2.50), Δ9 cis C18:1 (RPD = 2.80), and Δ 9 trans C18:1 (RPD = 2.51). All these models were obtained with the 322 nm excitation band after applying the normalization preprocessing method except for the C14:0 and C16:0 models that were obtained without preprocessing (i.e. RAW).

Seven other models, that can be used to distinguish between high and low values and applied for rough screening [START_REF] Williams | Near-infrared technology: Getting the best out of light[END_REF], were identified for the total trans MUFA (λex: 335 nm, RPD = 1.54), PUFA (λex: 350 nm, RPD = 1.62), n-3 PUFA (λex: 322 nm, RPD = 1.54), LC n-3 PUFA (λex: 322 nm, RPD = 1.57), n-6 trans PUFA (λex: 322 nm, RPD = 1.50), and CLA (λex: 350 nm, RPD = 1.53). These models were calculated after spectral data normalization except for the sum of n-3 PUFA model that was calculated after applying MSC preprocessing to spectra. For the other components, the model accuracies were poor and cannot be used for prediction (0.89 b RPD b 1.49; R 2 p b 0.35).

Prediction of lipid contents with SFS

The PLS regression statistics of models developed with SFS are summarized in Table 4. Considering SFS, the best predictive models were obtained for the total lipid content (RPD = 2.76), MUFA (RPD = 2.55), cis MUFA (RPD = 2.77), C14:0 (RPD = 2.76), Δ 9 cis C18:1, (RPD = 2.63), and Δ 9 trans C18:1 (RPD = 2.68). Based on the RPD and R 2 p , those models can be used for process control [START_REF] Williams | Near-infrared technology: Getting the best out of light[END_REF]. The different models were calculated with an offset value of 60 nm (maximum emission 353 nm) except for the PLS-R model of Δ 9 trans C18:1 obtained with an offset value of 120 nm (maximum emission 391 nm). All these predictive models were obtained with the data corrected by SNV preprocessing except for the predictive model of the C14:0 that was calculated after correcting spectra with the AREA preprocessing method.

The regression models of total amount of FA (RPD = 2.46), SFA (RPD = 2.40), linear SFA (RPD = 2.39), CLA (RPD = 2.25), C16:0 (RPD = 2.29), C18:0 (RPD = 2.39) can be used for approximate quantitative prediction. Those models were obtained with an offset of 120 nm except for total amount of FA and C16:0 calculated with two offsets 60 and 80 nm respectively. These models were obtained after applying the SNV preprocessing method to spectral data except for total FA and CLA that were calculated after performing the normalization preprocessing method.

Seven other models, that can be used to distinguish between high and low values and applied for rough screening [START_REF] Williams | Near-infrared technology: Getting the best out of light[END_REF], were identified for the sum of MUFA trans (RPD = 1.95), PUFA (RPD = 1.76), n-3 PUFA (RPD = 1.85), n-6 trans PUFA (RPD = 1.82), Δ 10-11 trans C18:1 (RPD = 1.71), n-3 C18:3 (RPD = 1.83), and n-3 C20:4 (RPD = 1.64). These different predictive models were calculated with the normalization preprocessing method except for the sum of n-3 PUFA predictive model that was obtained after applying the MSC method to spectral data.

The accuracy of the other predictive models was low based on their RPD values (1.06 b RPD b 1.47 and R 2 p b 0.58).

Discussion

Total lipids and fatty acids prediction in meat

A few studies have attempted to confirm the ability of fluorescence spectroscopy to predict total lipids of meat [START_REF] Brondum | Prediction of water-holding capacity and composition of porcine meat by comparative spectroscopy[END_REF][START_REF] Wold | Quantification of intramuscular fat content in beef by combining autofluorescence spectra and autofluorescence images[END_REF][START_REF] Wold | Quantification of connective tissue (hydroxyproline) in ground beef by autofluorescence spectroscopy[END_REF]. [START_REF] Wold | Quantification of connective tissue (hydroxyproline) in ground beef by autofluorescence spectroscopy[END_REF] reported a R 2 of 0.71 and a RPD of 1.86 for intact beef Longissimus dorsi muscle and [START_REF] Brondum | Prediction of water-holding capacity and composition of porcine meat by comparative spectroscopy[END_REF] found lower R 2 (0.57) and RPD (1.52) on pork intact muscles. The higher predictive accuracy of the models reported in the present work (FFFS: R 2 = 0.68 and RPD = 2.61; SFS: R 2 = 0.80, RPD = 2.46) are probably associated with two factors: (i) a higher variability in the data base: increasing variability by the use of several breeds fed, several diets, and muscle type in sampling had a positive effect on beef FA prediction [START_REF] Brondum | Prediction of water-holding capacity and composition of porcine meat by comparative spectroscopy[END_REF][START_REF] Mourot | Breeds and muscle types modulate performance of near-infrared reflectance spectroscopy to predict the fatty acid composition of bovine meat[END_REF]; (ii) the preparation of the samples: in this study, meat samples were transformed into fine powder under liquid nitrogen whereas in other studies, samples were intact. The fluorescence measurement can be considered as a surface measurement and the grinding of the sample can permit to record a fluorescence signal that is more representative of the sample. NIR spectroscopy is undoubtedly the most used method for predicting total lipids in beef, some studies after cross-validation reported a R 2 cv and RPD higher than 0.9 and 3.5 respectively [START_REF] De Marchi | Use of near infrared spectroscopy for assessment of beef quality traits[END_REF][START_REF] Mourot | Breeds and muscle types modulate performance of near-infrared reflectance spectroscopy to predict the fatty acid composition of bovine meat[END_REF][START_REF] Tøgersen | On-line prediction of chemical composition of semi-frozen ground beef by non-invasive NIR spectroscopy[END_REF] suggesting that this spectral method presented probably a higher accuracy to predict total lipids in beef.

As far as we know, this study brings new insight regarding the ability of FFFS to predict FA composition of beef because no study reported equivalent investigation. According to [START_REF] Williams | Near-infrared technology: Getting the best out of light[END_REF], the regression models calculated for total SFA, linear SFA, MUFA, cis MUFA and for some individual unsaturated and mono-unsaturated FA (C14:0, C16:0, C18:0, Δ 9 cis C18:1, and Δ 9 trans C18:1) could be used for process control (R 2 p ≥ 0.63 and RPD p ≥ 2.50) and the one obtained for nonlinear SFA would be suited for approximate quantitative prediction.

The poor predictive models calculated for the other FAs (i.e. total or individual MUFA and PUFA) except the oleic acid Δ9 cis C18:1, could be attributed either to a narrow variation range of the reference values (giving small CV). Nonetheless, this hypothesis was not verified for total CLA, total trans MUFA, 18:1 Δ 10-11 trans, 20:3 n-3, 20:4 n-3, and 22:6 n-3 that presented high CV and low RPD values. We suggested that those components are not well predicted due both to a large RMSEP compared to SD of the reference values [START_REF] Prevolnik | Application of near infrared spectroscopy to predict chemical composition of meat and meat products[END_REF] and to their lower concentration in meat (Tables 2 and3). Indeed, the concentration of MUFA and PUFA are lower compared to the other FA except for the oleic acid. Moreover, we assumed the presence of a different minimum threshold of detection between the SFA and MUFA. Indeed, the total MUFA and cis MUFA are correctly predicted compared to individual MUFA except for oleic acid which is the most abundant FA.

The best predictive models discussed above were obtained by using three excitation wavelengths (322, 335 or 350 nm) that were previously reported as being sensitive to meat fat. The 322 nm was assigned to vitamin A [START_REF] Skjervold | Development of intrinsic fluorescent multispectral imagery specific for fat, connective tissue, and myofibers in meat[END_REF]. The 335 nm excitation wavelength was used to predict intramuscular fat content on beef [START_REF] Wold | Quantification of intramuscular fat content in beef by combining autofluorescence spectra and autofluorescence images[END_REF][START_REF] Wold | Quantification of connective tissue (hydroxyproline) in ground beef by autofluorescence spectroscopy[END_REF]. Nonetheless, this excitation band was also reported as sensitive to NADH [START_REF] Karoui | Utilisation of a rapid technique based on frontface fluorescence spectroscopy for differentiating between fresh and frozen-thawed fish fillets[END_REF]. Even if its concentration is small in meat sample [START_REF] Chan | Dietary vitamin E effect on color stability and sensory assessment of spoilage in three beef muscles[END_REF][START_REF] Gatellier | Effect of a dietary vitamin E supplementation on colour stability and lipid oxidation of air and modified atmospherepackaged beef[END_REF], we considered the 350 nm excitation wavelength associated to the fluorescence of vitamin E. Nonetheless, some inconsistencies concerning the assignment of this band to vitamin E have to be pointed out in such a complex product [START_REF] Sikorska | Analysis of olive oils by fluorescence spectroscopy: Methods and applications[END_REF].

No interesting models were obtained with the 290 and 382 nm excitation bands probably due to their low relation with meat fat. Indeed, the 290 nm band was mainly assigned to tryptophan residues in proteins and usually applied as a fingerprint to discriminate muscles [START_REF] Dufour | Les spectres de fluorescence frontale: une empreinte digitale de la viande[END_REF][START_REF] Sahar | Synchronous front-face fluorescence spectroscopy coupled with parallel factors (PARAFAC) analysis to study the effects of cooking time on meat[END_REF], to predict microbial spoilage of meat [START_REF] Aït-Kaddour | Development of a portable spectrofluorimeter for measuring the microbial spoilage of minced beef[END_REF] and chicken breast filet [START_REF] Sahar | Synchronous front-face fluorescence spectroscopy as a promising tool for the rapid determination of spoilage bacteria on chicken breast fillet[END_REF]. For the 382 nm band, [START_REF] Wold | Quantification of intramuscular fat content in beef by combining autofluorescence spectra and autofluorescence images[END_REF], [START_REF] Egelandsdal | On attempts to measure the tenderness of Longissimus Dorsi muscles using fluorescence emission spectra[END_REF] and [START_REF] Skjervold | Development of intrinsic fluorescent multispectral imagery specific for fat, connective tissue, and myofibers in meat[END_REF] have shown that this excitation wavelength is more adapted to extract information related to connective tissue. This was confirmed by [START_REF] Sahar | Synchronous front-face fluorescence spectroscopy coupled with parallel factors (PARAFAC) analysis to study the effects of cooking time on meat[END_REF], nonetheless they reported that riboflavin could also be excited.

The SFS mode used in this study demonstrates that spectra recorded on meat samples exhibit peaks corresponding to different fluorophores as reported by [START_REF] Sahar | Synchronous front-face fluorescence spectroscopy coupled with parallel factors (PARAFAC) analysis to study the effects of cooking time on meat[END_REF]. The results demonstrated that the influence of Δ λ can be substantial on the shape, location, and signal intensity of the fluorescence peaks. Indeed, depending of the Δ λ value, synchronous fluorescence spectra are substantially modified. As previously noted [START_REF] Boubellouta | Effects of mild heating and acidification on the molecular structure of milk components as investigated by synchronous front-face fluorescence spectroscopy coupled with parallel factor analysis[END_REF], SFS makes it possible to narrow the spectral bands and, compared to emission or excitation spectra, to have information on the fluorescence properties of several intrinsic fluorophores on a given spectrum. This suggests the potential of synchronous fluorescence spectra to be suited for the prediction of different lipid components in beef.

As far as we know, this study brings also new insight regarding the ability of SFS to predict total FA and other lipids of beef because no study reported equivalent investigation. As discussed for FFFS, the regression models calculated for total lipids, MUFA, cis MUFA and individual unsaturated and mono-unsaturated FA (C14:0, C18:1, and Δ9 trans C18:1) could be used for process control and the one obtained for total FA, linear SFA, C16:0, and C18:0 would be suited for approximate quantitative prediction [START_REF] Williams | Near-infrared technology: Getting the best out of light[END_REF]. The lower accuracy of predictive models calculated for the other FAs can be attributed, as for FFFS, either to a narrow variation range of the reference values, to large RMSEP compared to SD of the reference values and to their lower concentration in meat. Moreover, a higher ability of SFS to predict SFA can be noted compared to FFFS.

In the present study, three offsets can be identified as the most suited for meat lipid prediction, 60, 80, and 120 nm. The synchronous fluorescence excitation spectra obtained with those three offsets presented different peaks that can be related to specific fluorophores. The spectral band identified around 296 nm seems to originate from the fluorescence of the protein tryptophan residues [START_REF] Boubellouta | Effects of mild heating and acidification on the molecular structure of milk components as investigated by synchronous front-face fluorescence spectroscopy coupled with parallel factor analysis[END_REF][START_REF] Sahar | Synchronous front-face fluorescence spectroscopy coupled with parallel factors (PARAFAC) analysis to study the effects of cooking time on meat[END_REF]. While, the assignment of the band, between 450 and 525 nm, to a specific fluorophore remains unclear. For example, it was previously assigned to riboflavin and to Maillard reaction products [START_REF] Sahar | Synchronous front-face fluorescence spectroscopy coupled with parallel factors (PARAFAC) analysis to study the effects of cooking time on meat[END_REF][START_REF] Yaacoub | Rapid assessment of neoformed compounds in nuts and sesame seeds by front-face fluorescence[END_REF].

As previously mentioned, no studies have investigated the potential of fluorescence spectroscopy to predict individual or total FA composition of ground LT muscles. Nonetheless, few studies have used NIR spectroscopy to predict those components in ground LT muscles [START_REF] Cecchinato | Genetic analysis of beef fatty acid composition predicted by near-infrared spectroscopy[END_REF][START_REF] Mourot | Breeds and muscle types modulate performance of near-infrared reflectance spectroscopy to predict the fatty acid composition of bovine meat[END_REF][START_REF] Sierra | Prediction of the fatty acid composition of beef by near infrared transmittance spectroscopy[END_REF]. The models reported in those studies for 14:0, 16:0, 18:0, 18:1 Δ9, 20:3 n-3, 22:6 n-3 were more accurate than those reported in the present study. Nonetheless, the models for 18:1 Δ 9 trans, 20:4 n-6, 20:5 n-3, and 22:5 n-3 presented higher RPD values whatever the fluorescence spectroscopy method (SFS or FFFS) compared to the NIR spectroscopy (0.40 in average).

For total SFA, linear SFS, MUFA, cis MUFA higher predictive statistics were reported for NIR spectroscopy. In the study of [START_REF] Mourot | Breeds and muscle types modulate performance of near-infrared reflectance spectroscopy to predict the fatty acid composition of bovine meat[END_REF], that reported the best statistics for predictive models, the RPD cv was improved by an average value of 0.98. Total PUFA, n-3 PUFA, n-3 LC PUFA, n-6 PUFA, n-6 trans PUFA, n-6 LC PUFA were more accurately predicted by fluorescence spectroscopy (SFF and FFFS) compared to NIR spectroscopy (the RPD is improved in our study by a value of 0.32). For total trans MUFA, total CLA, 22:6 n-3, and 18:3 n-3 higher RPD value (+ 0.12 in average) was obtained only by SFS excitation method compared to NIR spectroscopy. Those observations could suggest that fluorescence spectroscopy is more suited for predicting the PUFA than NIR spectroscopy. Nonetheless, it seems whatever the method used that the major FAs (representing over 1% of the total FAs identified such as 14:0, 16:0, 18:0, 18:1 Δ9,…) were more accurately predicted than minor FAs [START_REF] Mourot | Breeds and muscle types modulate performance of near-infrared reflectance spectroscopy to predict the fatty acid composition of bovine meat[END_REF]. Also, total and many individual SFAs and MUFAs were better predicted than total and individual PUFAs.

The best regression models for total fat, lipids and each FAs were obtained using different mathematical preprocessing. In the present study, nearly all-preprocessed models are simpler or more parsimonious (i.e. use fewer PLS factors) than the non-preprocessed models (i.e. RAW models), independent of the fluorescence spectra used, FFFS or SFS. The best preprocessing method differs between components considered suggesting that it is impossible to know beforehand which preprocessing would lead to the most accurate model previously reported [START_REF] Fernández-Cabanás | Optimisation of the spectral pre-treatments used for Iberian pig fat NIR calibrations[END_REF]. Nonetheless, it is interesting to note both that the models created from normalized (i.e. AREA) spectra are generally the simplest models and the best preprocessing method. This is more obvious for the models calculated with FFFS that gave 20 components among 33 compared to the models calculated with SFS that presented 16 components among 33 presenting the normalization as the best preprocessing method (Tables 3 and4). Those results suggested that the normalization approach appears as a good preprocessing strategy for the prediction of fat and fatty acid contents from fluorescence spectra. This study shows, and recalled, that a normalization is not at all a neutral operation and has to be carefully chosen.

Comparison of the ability of FFFS and SFS to predict fatty acid composition

The comparison between the two fluorescence excitation modes was conducted whenever a predictive model was usable for process control (RPD ≥ 2.50). Compared to the SFS, the models obtained with FFFS presented generally lower values of R 2 p , loading vectors, RMSEP and higher values of RPD factors. The b-coefficient obtained with the SFS presented a noisy shape (results not shown) indicating an over fitting of models calculated. This implies that the models obtained with FFFS are generally more accurate and simplest or more parsimonious than the models obtained with SFS data. Considering that meat is a complex and anisotropic matrix presenting different physicochemical properties, the use of only some excitation or emission wavelengths for the excitation of its intrinsic fluorophores would have limited the ability of the FFFS to predict efficiently some lipids in meat compared to the SFS excitation mode. Nonetheless, the present study clearly shows that the FFFS is the best method for prediction of fat and fatty acid contents in meat.

Those results can be assigned to the fact that a synchronous fluorescence spectrum exhibits peaks corresponding to several different fluorophores. These compounds are present at the sample surface resulting in an increase of spectral loss and interferences. This induces instability in models compared to the FFFS that allows exciting a fluorophore more specifically. This observation is in agreement with the observation of [START_REF] Patra | Recent developments in multi-component synchronous fluorescence scan analysis[END_REF] who reported that in spite of the sensitivity, selectivity, for multi-component analysis, SFS fails for very complex mixtures.

Conclusion

This study can be considered as a first step to demonstrate the ability of fluorescence spectroscopy to predict FA contents of the LT muscle coming from three breeds of cattles. This method globally appears as a promising tool to support the current effort of research carried out to improve the nutritional quality of meat products. The results suggested that the fluorescence spectroscopy is more suited to measure SFA and MUFA because this method failed when the prediction of PUFA was investigated. It seems that a higher minimum threshold was necessary to predict correctly the PUFA compared to the SFA. Indeed, the total MUFA and cis MUFA are correctly predicted compared to individual MUFA except for oleic acid which is the most abundant FA. However more effort should be done to validate those hypotheses. Taking into account the number of samples used in the present study and experimental conditions, the models obtained are not ready to be used in practical conditions. A more complete calibration and validation phases, with different muscle categories, feeding regimes and higher number of breeds will be necessary to build robust predictive models and to ensure that fluorescence spectroscopy is adapted for prediction of meat fat composition and for industrial applications.

  had a lower CV (under 40%) than individual and total SFAs and MUFAs. 3.2. Description of fluorescence spectra of meat samples Average spectra recorded in the two modes (FFFS and SFS) are presented in Figs. 1 and 2. The Fig. 1 presented fluorescence emission spectra obtained at 5 excitation wavelengths 290, 322, 335, 350, and 382 nm.

  Abbreviations: SD: standard deviation; Min: minimum; Max: maximum.

Fig. 2 .

 2 Fig. 2. Average Synchronous Fluorescence spectra recorded at Δλ = 60, 80 and 120 on meat samples (a.u. arbitrary unit).

Table 1

 1 Interpretation of RPD (Ratio of Performance to Deviation) value[START_REF] Williams | Near-infrared technology: Getting the best out of light[END_REF].

	RPD value	Classification			Application
	0.00-1.50	Not recommended			No application
	1.50 and 2.00	Distinguish between high and low values	Rough screening
	2.00 and 2.50	Approximate quantitative prediction	Quality control
	2.50 and 3.00	Good				Process control
	N3.0	Excellent			Any application
	Table 2
	Total lipid and total fatty acid (g/100 g fresh meat) contents and fatty acid composition
	(mg/100 g fresh meat) of LT muscle samples.
	Component		Mean	Min	Max	SD	CV
	Total lipids		2.07	0.78	6.06	1.22	59%
	Total FA		1.39	0.36	4.35	0.98	71%
	Total FA/total lipids	0.63	0.38	0.90	0.11	17%
	Fatty acid families
	Total SFA		600.05	106.53	2121.08	471.60	79%
	Total linear SFA		578.06	100.15	2067.75	457.83	79%
	Total non-linear SFA	21.99	6.37	58.48	14.18	64%
	Total MUFA		571.25	96.20	1975.96	466.32	82%
	Total cis MUFA		483.09	77.27	1796.27	406.62	84%
	Total trans MUFA		88.16	15.33	326.59	70.39	80%
	Total PUFA		202.95	128.38	358.24	49.27	24%
	Total n-3 PUFA		35.20	17.88	65.45	9.94	28%
	Total n-3 LC PUFA		14.60	7.41	26.31	3.53	24%
	Total n-6 PUFA		159.95	99.86	274.96	37.91	24%
	Total n-6 cis PUFA		135.01	80.38	230.58	28.41	21%
	Total n-6 trans PUFA	24.94	2.89	94.58	21.00	84%
	Total n-6 LC PUFA		30.74	17.09	57.62	8.05	26%
	Total CLA		6.64	0.51	23.03	5.84	88%
	Individual fatty acids
	14:0		30.26	2.38	114.69	29.88	99%
	16:0		299.68	50.64	1215.99	256.94	86%
	18:0		216.10	41.26	631.10	149.60	69%
	18:1 Δ 9 cis		388.47	41.72	1512.13	343.22	88%
	18:1 Δ 9 trans		5.43	0.57	22.29	4.63	85%
	18:1 Δ 10-11 trans	66.37	8.85	268.60	55.75	84%
	18:2 n-6 cis cis		101.84	61.97	165.82	21.25	21%
	18:3 n-3		20.60	6.31	46.39	7.80	38%
	20:3 n-3		0.14	0.01	1.19	0.26	186%
	20:4 n-3		0.80	0.01	2.75	0.62	78%
	20:4 n-6		21.73	11.92	39.31	5.52	25%
	20:5 n-3		3.90	2.00	6.10	1.03	26%
	22:5 n-3		9.26	5.15	16.47	2.23	24%
	22:6 n-3		0.49	0.01	1.67	0.45	92%

SFA, saturated FA; Total SFA: linear + non-linear SFA; Linear SFA: 12:0 up to 24:0; Non-linear SFA: iso [14 up to 18] + anteiso [15 + 17], FA mainly synthesized by rumen microorganisms; MUFA, monounsaturated FA; Total MUFA: cis + trans MUFA; Total cis MUFA: 14:1 Δ9cis + 15:1 Δ9cis + 16:1 Δ9cis + 17:1 Δ8 and Δ9cis + 18:1 Δ6cis up to Δ15cis

Table 3

 3 Performance of the best PLS regression models to predict lipids and fatty acid contents of minced beef meat obtained with classical Front Face Fluorescence excitation method a .

	Component	λex (nm)	PPS	LV	R 2	C	R 2	CV	R 2	P	RMSEC	RMSEP	RPD p
	Total lipids	322	AREA	5	0.67	0.60	0.68	0.78	0.46	2.67
	Total FA	322	AREA	6	0.72	0.63	0.68	0.58	0.37	2.61
	Total FA/total lipids	322	AREA	5	0.44	0.17	0.42	0.09	0.08	1.42
	Fatty acid families
	Total SFA	322	AREA	5	0.69	0.63	0.66	291.72	178.19	2.65
	Total linear SFA	322	AREA	5	0.69	0.62	0.66	283.93	172.57	2.65
	Total non-linear SFA	322	AREA	8	0.75	0.61	0.68	7.76	6.04	2.35
	Total MUFA	322	AREA	6	0.71	0.61	0.68	279.80	177.76	2.62
	Total cis MUFA	322	AREA	5	0.69	0.61	0.67	256.07	146.68	2.77
	Total trans MUFA	335	AREA	9	0.65	0.43	0.51	41.78	46.07	1.54
	Total PUFA	350	AREA	7	0.40	0.06	0.45	40.33	32.37	1.62
	Total n-3 PUFA	322	MSC	2	0.13	0.04	0.23	9.90	6.95	1.54
	Total n-3 LC PUFA	322	AREA	3	0.14	0.01	0.18	3.41	2.34	1.57
	Total n-6 PUFA	350	MSC	7	0.47	0.07	0.44	29.04	24.61	1.63
	Total n-6 cis PUFA	335	SNV	1	0.00	0.01	0.06	31.44	24.21	1.22
	Total n-6 trans PUFA	322	AREA	6	0.46	0.34	0.33	16.22	14.80	1.50
	Total n-6 LC PUFA	335	SNV	5	0.05	0.05	0.14	8.27	6.21	1.37
	Total CLA	350	AREA	10	0.79	0.44	0.58	2.75	3.90	1.53
	Individual fatty acids
	14:0	322	RAW	7	0.77	0.66	0.75	15.27	11.23	2.88
	16:0	322	RAW	10	0.83	0.71	0.65	119.98	98.54	2.61
	18:0	322	AREA	5	0.73	0.67	0.66	85.50	59.91	2.50
	18:1 Δ 9 cis	322	AREA	5	0.69	0.61	0.68	214.88	122.79	2.80
	18:1 Δ 9 trans	322	AREA	6	0.64	0.50	0.63	3.12	1.85	2.51
	18:1 Δ 10-11 trans	335	AREA	9	0.60	0.35	0.48	34.813	37.244	1.49
	18:2 n-6 cis cis	350	SNV	1	0.01	0.06	0.09	23.34	18.11	1.26
	18:3 n-3	322	MSC	2	0.13	0.05	0.17	7.67	6.05	1.37
	20:3 n-3	290	MSC	10	0.15	0.00	0.33	0.43	0.36	0.89
	20:4 n-3	322	AREA	6	0.12	0.00	0.16	0.62	0.48	1.39
	20:4 n-6	290	AREA	3	0.05	0.00	0.00	5.65	4.02	1.45
	20:5 n-3	322	MSC	2	0.07	0.00	0.10	1.02	0.88	1.21
	22:5 n-3	322	AREA	4	0.21	0.05	0.15	2.00	1.65	1.37
	22:6 n-3	290	MSC	3	0.09	0.04	0.28	0.43	0.37	1.24

SFA, saturated FA; Total SFA: linear + non-linear SFA; Linear SFA: 12:0 up to 24:0; Non-linear SFA: iso [14 up to 18] + anteiso

[15 + 17]

; MUFA, monounsaturated FA; Total MUFA: cis + trans MUFA; Total cis MUFA: 14:1 Δ9cis + 15:1 Δ9cis + 16:1 Δ9cis + 17:1 Δ8 and Δ9cis + 18:1 Δ6cis up to Δ15cis + 20:1 Δ9cis + 22:1 Δ9cis; Total trans MUFA: 16:1 Δ9trans + 18:1Δ6trans up to Δ16trans; PUFA, polyunsaturated FA; Total PUFA: n-6 PUFA + n-3PUFA + CLA; Total n-3 PUFA: 18:3 n-3 + 20:3 n-3 + 20:4 n-3 + 20:5 n-3 + 22:3 n-3 + 22:4 n-3 + 22:5 n-3 + 22:6 n-3; Total n-6 PUFA: 18:2 n-6 + 18:3 n-6 + 20:2 n-6 + 20:3 n-6 + 20:4 n-6 + 22:2 n-6 + 22:4 n-6 + 22:5 n-6; LC, long chain; CLA, conjugated linoleic acid; Total CLA: 9cis,11trans-CLA + 11cis,13trans-CLA + Total cis,cis CLA + Total trans,trans CLA. a Abbreviations: λex: excitation wavelength; PPS: preprocess; RAW: without preprocessing; SNV: Standard Normal Variate; AREA: normalization; LV: loading vector; MSC: Multiple Scattering Correction; R 2 C : coefficient of determination for calibration; R 2 p : coefficient of determination for prediction; RMSEC: Root mean square error of calibration; RMSEP: Root mean square error of prediction; RPDp: Ratio of Performance to Deviation for prediction.

Table 4

 4 Performance of the best PLS regression models to predict lipids and fatty acid contents of minced beef obtained with Synchronous Fluorescence excitation method a . RMSEC: root mean square error of calibration; RMSEP: root mean square error of prediction; RPDp: residual predictive deviation for prediction.

	Component	Δλ (nm) PPS	LV R 2 C	R 2	P	RMSEC RMSEP RPD p
	Total lipids	60	AREA	8 0.87 0.77	0.46	0.46 2.76
	Total FA	60	AREA	8 0.87 0.80	0.35	0.40 2.46
	Total FA/total lipids	80	AREA	4 0.26 0.58	0.10	0.08 1.47
	Fatty acid families
	Total SFA	120	SNV	7 0.93 0.80 122.32 195.24 2.40
	Total linear SFA	120	SNV	7 0.93 0.80 118.82 189.68 2.39
	Total non-linear SFA	60	AREA	7 0.84 0.76	5.80	6.09 2.37
	Total MUFA	60	SNV	7 0.86 0.79 175.83 182.27 2.55
	Total cis MUFA	60	SNV	7 0.87 0.81 145.76 153.48 2.62
	Total trans MUFA	60	AREA	5 0.52 0.54	51.39	38.17 1.95
	Total PUFA	120	AREA	4 0.48 0.48	36.93	29.36 1.76
	Total n-3 PUFA	40	MSC	6 0.46 0.37	7.80	5.78 1.85
	Total n-3 LC PUFA	60	SNV	3 0.25 0.03	3.29	2.88 1.32
	Total n-6 PUFA	120	AREA	4 0.44 0.39	29.12	26.45 1.47
	Total n-6 cis PUFA	20	MSC	5 0.17 0.13	25.33	24.78 1.17
	Total n-6 trans PUFA	60	AREA	6 0.56 0.43	15.08	12.51 1.82
	Total n-6 LC PUFA	40	AREA 10 0.25 0.16	6.65	8.02 1.07
	Total CLA	120	AREA	5 0.72 0.71	3.01	2.52 2.25
	Individual fatty acids
	14:0	60	AREA	8 0.86 0.83	11.38	10.95 2.76
	16:0	80	SNV	8 0.92 0.77	73.36 115.14 2.29
	18:0	120	SNV	5 0.81 0.78	64.60	62.16 2.39
	18:1 Δ 9 cis	60	SNV	7 0.87 0.81 121.59 128.85 2.63
	18:1 Δ 9 trans	120	SNV	4 0.70 0.74	2.37	1.63 2.68
	18:1 Δ 10-11 trans	20	AREA	7 0.69 0.50	30.75	32.46 1.71
	18:2 n-6 cis cis	20	MSC	5 0.16 0.09	19.33	18.50 1.20
	18:3 n-3	40	AREA	6 0.38 0.39	6.59	4.61 1.83
	20:3 n-3	80	MSC 10 0.15 0.00	0.24	0.26 1.09
	20:4 n-3	60	AREA	2 0.08 0.11	0.66	0.42 1.64
	20:4 n-6	100	AREA 10 0.29 0.05	4.48	5.89 1.06
	20:5 n-3	40	SNV	5 0.29 0.11	0.92	0.76 1.44
	22:5 n-3	120	AREA	4 0.33 0.16	1.86	1.76 1.30
	22:6 n-3	120	RAW	6 0.51 0.34	0.32	0.32 1.41
	SFA, saturated FA; Total SFA: linear + non-linear SFA; Linear SFA: 12:0 up to 24:0; Non-linear
	SFA: iso [14 up to 18] + anteiso [15 + 17]; MUFA, monounsaturated FA; Total MUFA: cis +
	trans MUFA; Total cis MUFA: 14:1 Δ9cis + 15:1 Δ9cis + 16:1 Δ9cis + 17:1 Δ8 and Δ9cis +
	18:1 Δ6cis up to Δ15cis + 20:1 Δ9cis + 22:1 Δ9cis; Total trans MUFA: 16:1 Δ9trans +
	18:1Δ6transup to Δ16trans; PUFA, polyunsaturated FA; Total PUFA: n-6 PUFA + n-
	3PUFA + CLA; Total n-3 PUFA: 18:3 n-3 + 20:3 n-3 + 20:4 n-3 + 20:5 n-3 + 22:3
	n-3 + 22:4 n-3 + 22:5 n-3 + 22:6 n-3; Total n-6 PUFA: 18:2 n-6 + 18:3 n-6 +
							Scattering
	Correction; R 2 C : coefficient of determination for calibration; R 2 p : coefficient of determination
	for prediction;

20:2 n-6 + 20:3 n-6 + 20:4 n-6 + 22:2 n-6 + 22:4 n-6 + 22:5 n-6; LC, long chain; CLA, conjugated linoleic acid; Total CLA: 9cis,11trans-CLA + 11cis,13trans-CLA + Total cis,cis CLA + Total trans,trans CLA.

a Abbreviations: Δλ: offset in nm; PPS: preprocess; RAW: without preprocessing; SNV:

Standard Normal Variate; AREA: normalization; LV: loading vector; MSC: Multiple