Audrey Cosson

Julien Delarue

Anne-Cécile Mabille

Amandine Druon

Nicolas Descamps

Jean- Michel Roturier

Isabelle Souchon

Anne Saint-Eve
email: anne.saint-eve@inra.fr

Block protocol for conventional profiling to sensory characterize plant protein isolates AUTHORS

INTRODUCTION

Over the last decade, the food industry has been searching for alternatives to animal proteins as more consumers are making dietary choices based on health, ethical, or environmental criteria. In this context, pea (Pisum sativum L.) protein isolates are becoming increasingly common in new plant-based foods, enhancing the sustainability of food systems [START_REF] Pelzer | Pea-wheat intercrops in low-input conditions combine high economic performances and low environmental impacts[END_REF][START_REF] Siddique | Innovations in agronomy for food legumes. A review[END_REF]. Pea proteins have desirable functional properties (e.g., emulsification, foaming, and whipping) [START_REF] Gharsallaoui | Interfacial and emulsifying characteristics of acid-treated pea protein[END_REF]; they also have low allergenicity and high nutritional value [START_REF] Chel-Guerrero | Physicochemical and structural characterization of Lima Bean (Phaseolus lunatus) globulins[END_REF][START_REF] Gharsallaoui | Interfacial and emulsifying characteristics of acid-treated pea protein[END_REF]. If many articles deal about their physico-chemical characteristics, little deal about their sensory characteristics reflecting difficulties to sensory describe them [START_REF] Roland | Flavor Aspects of Pulse Ingredients[END_REF].

Like other pulse plant proteins, pea proteins have a distinct flavor. Sensory characterizations have highlighted the existence of three highly persistent notes: bitterness, astringency, and beaniness [START_REF] Bott | Sensory characteristics of combinations of chemicals potentially associated with beany flavour in foods[END_REF][START_REF] Humiski | Physicochemical and bitterness properties of enzymatic pea protein hydrolysates[END_REF]. Beaniness corresponds to a complex flavor perception that is associated with bean products: consumers perceive such diverse notes as musty/earthy, musty/dusty, sour, starchy, powdery, green pea, nutty, and brown [START_REF] Bott | Sensory characteristics of combinations of chemicals potentially associated with beany flavour in foods[END_REF]. Such notes are rooted in the complex composition of the aroma compounds found in pulses [START_REF] Murat | Characterization of odour active compounds along extraction process from pea flour to pea protein extract[END_REF]. In mouth, pea proteins are also often described as astringent, which has been defined as "the complex of sensations due to shrinking, drawing, or puckering of the epithelium as a result of exposure to substances such as alums or tannins" by the American Society for Testing Materials [START_REF]Standard terminology relating to sensory evaluation of materials and products[END_REF]. Finally, pea proteins are often perceived as bitter, which could result from the interaction of bitter compounds (e.g., amino acids, phenolics) with the TAS2R family of receptors, which are found on the apical membranes of taste receptor cells [START_REF] Maehashi | Bitter peptides activate hTAS2Rs, the human bitter receptors[END_REF][START_REF] Meyerhof | The molecular receptive ranges of human TAS2R bitter taste receptors[END_REF].

Consequently, the distinct and persistent flavor of pea proteins limits their use as food ingredients [START_REF] Lam | Pea protein isolates: Structure, extraction, and functionality[END_REF] and challenges their sensory evaluation. In sensory evaluation of foods, descriptive analysis such as classical profiling is conventionally employed [START_REF] Lawless | Sensory Evaluation of Food Science: Principles and Practices[END_REF]. However, in classical profiling, the multidimensional nature of beaniness can be difficult for panelists to precisely describe and quantify. As a result, there is an increased probability of omitting a sensory dimension [START_REF] Torres-Penaranda | Sensory descriptive analysis of soymilk[END_REF]. In addition, the prolonged persistence of bitter and astringent notes can lead to physiological adaptation and sensory fatigue [START_REF] Kallithraka | Evaluation of bitterness and astringency of (+)-catechin and (-) epicatechin in red wine and in model solution[END_REF]. These same difficulties occur when pea proteins are part of food matrices, where composition, texture, and structure play an essential role in shaping sensory properties [START_REF] Kühn | In-mouth flavour release from milk proteins[END_REF][START_REF] Guichard | Interactions between flavor compounds and food ingredients and their Influence on flavor perception[END_REF]. Perception could also be affected by cognitive interactions (i.e., neurophysiological interactions) between taste qualities (e.g., bitterness, saltiness, sweetness; [START_REF] Keast | An overview of binary taste-taste interactions[END_REF]. These cognitive interactions may act differently for individuals depending on their cultural background, food use, food consumption habits, and physiology [START_REF] Lawrence | Odour-taste interactions: a way to enhance saltiness in low-salt content solutions[END_REF]. These interactions occur when people eat products in natural context, however if we are interested in understanding the sensory mechanisms and to relate them to specific compounds in the food, it may be interesting to limit these cognitive interactions.

Several alternatives to the classical sensory profiling protocol have already been described in literature to deal with these issues. To limit interactions between sapid and flavor perceptions, some researchers have suggested that nose clips could be employed to allow panelists to focus on sapid perceptions [START_REF] Abegaz | Partitioning taste from flavourtic flavor notes of fresh tomato (Lycopersicon esculentum, Mill) to develop predictive models as a function of volatile and nonvolatile components[END_REF][START_REF] Murphy | Taste and olfaction: independence vs interaction I[END_REF][START_REF] O'mahony | Taste perception, food quality and consumer acceptance[END_REF][START_REF] Schoumacker | Fat perception in cottage cheese: The contribution of flavour and tasting temperature[END_REF]. In descriptive analysis, to cope with complex food and to limit the probability of omitting a dimension, some protocols allow each panelist to add descriptors to a pre-established list of attributes as with the Mixed profiling [START_REF] Coulon-Leroy | Mixed Profiling: A new tool of sensory analysis in a professional context. Application to wines[END_REF]. As food presentation schemes can impact how memory is activated, other researchers have recommended adapting how samples are presented (monadically, comparatively, or accompanied by a reference) [START_REF] Mazzucchelli | Comparative of monadic and simultaneous sample presentation modes in a descriptive analysis of milk chocolate[END_REF][START_REF] Mcbride | Hedonic rating of food: single or side-by-side sample presentation[END_REF][START_REF] Saint-Eve | Influence of Proteins on the Perception of Flavored Stirred Yogurts[END_REF]. Using a stable and known reference for product evaluation could be interesting. For example, Polarized Sensory Positioning proposes to compare and indirectly describe a set of products to three known reference products [START_REF] Teillet | Sensory methodologies and the taste of water[END_REF]. The use of references is also used by the method of Pivot Profile [START_REF] Thuillier | Pivot© profile: A new descriptive method based on free description[END_REF], which is based on a free expression of the difference between a tested product and an identified pivot product. For both methods, the choice of the reference product is critical to produce meaningful descriptions.

In addition, pronounced variability in sensitivity to bitterness has been observed in the general population and among panelists [START_REF] Hansen | Heritability and genetic covariation of sensitivity to PROP, SOA, Quinine HCl, and Caffeine[END_REF]; furthermore, past research has shown that panelists who are less sensitive to bitterness are less able to discriminate among foods [START_REF] Dinehart | Bitter taste markers explain variability in vegetable sweetness, bitterness, and intake[END_REF]. Consequently, when carrying out sensory assessments, it is necessary to characterize panelists' sensitivity to bitterness. For example, the 6-n-Propylthiouracil (PROP) test can be used. Indeed, past work has found that PROP status could be a proxy for overall oral responsiveness, which means that high PROP tasters could potentially display greater overall sensitivity to sensory properties [START_REF] Carney | Increasing flavor variety with herbs and spices improves relative vegetable intake in children who are propylthiouracil (PROP) tasters relative to nontasters[END_REF]. This hypothesis is based on research that has found links between sensitivity to PROP bitterness and sensitivity to i) other basic taste sensations [START_REF] Fischer | The Associations between 6-n-Propylthiouracil (PROP) intensity and taste intensities differ by TAS2R38 Haplotype[END_REF][START_REF] Hayes | Supertasting and PROP bitterness depends on more than the TAS2R38 gene[END_REF][START_REF] Prescott | Responses to repeated oral irritation by apsaicin, Cinnamaldehyde and Ethanol in PROP tasters and non-tasters[END_REF][START_REF] Webb | The relationships between common measurements of taste function[END_REF]; ii) chemesthetic sensations (pungency associated with capsaicin and other oral irritants) [START_REF] Prescott | Responses to repeated oral irritation by apsaicin, Cinnamaldehyde and Ethanol in PROP tasters and non-tasters[END_REF][START_REF] Yang | Phenotypic variation in oronasal perception and the relative effects of PROP and Thermal Taster Status[END_REF]; and iii) tactile sensations (astringency associated with alum) [START_REF] Bajec | Thermal taste, PROP responsiveness, and perception of oral sensations[END_REF].

Dinehart and colleagues found that people with greater PROP sensitivity gave higher bitterness scores to vegetables [START_REF] Dinehart | Bitter taste markers explain variability in vegetable sweetness, bitterness, and intake[END_REF]. As a result, differences among panelists in bitterness sensitivity could greatly influence sensory perceptions of foods containing pea proteins. In summary, the complex sensory properties of pea proteins require an improved sensory profiling protocol if we wish to better and more extensively characterize food products containing pea proteins and understand the mechanisms that lead to sensory perceptions.

The aim of this study was to propose a relevant protocol for the evaluation and the characterization of pea protein-based samples. Based on this past research, we adapted a classical protocol to evaluate pea-protein-based foods. To decrease the effects of sensation saturation and sensory fatigue, we limited sample number and employed a strict palate-cleansing protocol. To deal with the complex and multidimensional perception of beaniness, we asked panelists to evaluate attributes by blocks. In addition, panelists were extensively trained to recognize different attributes, and we employed a reference sample to evoke these sensory memories during the experiment. Finally, to limit cognitive interactions between taste and flavor, and to dissociate cognitive interactions from chemical interactions, panelists wore nose clips during the evaluation of sapid perceptions. In this study, we compared how panelists evaluated solutions of pea protein isolates using both the classical protocol and the adapted protocol called here block protocol.

MATERIALS AND METHODS

Samples preparation

Six commercial pea protein isolates (representing different batches and suppliers) were used in this study.

They were presented in the form of solutions (P1 to P6). No further information on the isolates will be given for reasons of confidentiality.

We prepared 4% (w/w) pea protein solutions: the isolates were mixed in water (Evian, France) for 1 min and then allowed to soak for 30 min. The solutions were then mixed by hand before being served to the panelists.

The samples were stored at 4°C before serving. They were served at 20°C (room temperature) in transparent cups (29.5 mL) bearing three-digit codes.

Experimental conditions

We recruited 17 panelists (13 women and 4 men) based on their interest in participating in a long-term study.

They were grouped on two sub-groups: 9 panelists (8 women and 1 man, mean age = 20 years) and 8 panelists (5 women and 3 men, mean age = 26 years). These groups were formed according to the availability of panelists. They had no prior experience with pea products or sensory evaluation methods. They were not informed of the precise aim of the experiment. They gave their free and informed consent to participate in the study and received compensation for their participation. They were asked not to eat, drink, or smoke for at least 1 h prior to any experiments or training sessions. All sensory profiling was carried out in individual booths under white light (samples had similar color) in an air-conditioned room (20°C).

Palate-cleansing protocol

To reduce sensation build-up, three main strategies have been used: limiting sample number, increasing waiting time between samples, and more effectively cleansing the oral mucosa between samples [START_REF] Courregelongue | Using repeated ingestion to determine the effect of sweetness, viscosity and oiliness on temporal perception of soymilk astringency[END_REF]. Among these possibilities, the following palate-cleansing protocol was used between samples during our formal experiments: panelists had to consume an apple slice, drink water, and wait 40 seconds.

Characterization of panelist PROP status

We assessed the PROP taster status of the panelists using the three-solution test, with sodium chloride as the standard [START_REF] Tepper | Development of brief methods to classify individuals by PROP taster status[END_REF]. Taste intensity ratings were collected for three solutions of suprathreshold 6-n-Propylthiouracil (Sigma-Aldrich, USA) (0.032, 0.32, and 3.2 mM) and of sodium chloride (NaCl, Sigma-Aldrich, USA) (0.01, 0.1, 1.0 M); the solvent was water (Evian, France). The panelists first assessed the three sodium chloride solutions and then the three PROP solutions; the order of presentation in three-solution tests does not produce systematic differences in taste ratings. The Labeled Magnitude Scale (LMS) was used for evaluating the samples [START_REF] Tepper | Development of brief methods to classify individuals by PROP taster status[END_REF]. For each solution type, the order in which different solutions were presented was balanced across panelists. Between solutions, panelists rinsed their mouths with the palatecleansing. The panelists assessed each solution twice.

Attribute selection and panelist training

Panelists were asked to fill out a check-all-that-apply (CATA) survey. It contained 30 attributes, and it was possible for panelists to add more. For our final list, we selected attributes that were cited more than 20% of the time and that allowed significant discrimination among sample types. We also wished to limit attribute number to avoid panelist fatigue. The eleven attributes that we retained were salty, bitter, astringent, mouthfeel, granularity, pea, broth, nuts, almond, potato, and grains. For the evaluation of textured food matrices, the granularity attribute was not relevant to be used.

We then trained all the panelists to assess these attributes using external references and an unstructured scale ranging from 0 and 10 (Table 1). Training took place over 8 sessions that lasted 45 min. Afterward, panelist performance was evaluated and confirmed. The panel performance was assessed for the entire panel (subgroup 1 and subgroup 2) using an ANOVA model with three factors (sample, panelist, repetition) and their interactions. The sample effect indicates the discrimination of products by panelists (p < 0.05). Interactions were also used to determine whether panelists used the intensity scale between repetitions in a similar way (panelist * repetition), whether there was agreement among panelists (product * panelist) and whether panelists generated similar values between repetitions (product * repetition). Panelist performance was also evaluated for panelist discrimination, agreement and repeatability.

General experimental set-up

Nine panelists (9/25) were taught to use classical protocol, and eight others (8/25) were taught to use block protocol. They were then asked to evaluate the solutions P1 to P6. The panelists were unaware of sample identity, and they were given two replicates of each sample type. After this first round of evaluation, the panelists were taught the profiling protocol they had not yet used, and they were asked to repeat the evaluation process. So in total 17 panelists (17/25) evaluated products with both block and classical protocol. This repeated measures approach to sensory profiling allowed us to account for potential order and learning effects.

Sensory profiling methods

Two protocols were used: classical protocol and block protocol. In both cases, panelists were asked to evaluate the intensity of a sample's sensory attributes using an unstructured scale ranging from 0 to 10. To account for order and carry-over effects, sample order was balanced across panelists using a Latin square (Williams design). Panelists were given two replicates of each sample. The palate-cleansing protocol described above was used between samples.

When panelists employed the classical protocol, samples were presented monadically in sequence (Fig. 1): panelists evaluated all eleven attributes (printed on the same survey page) at once for each sample.

When panelists employed the block protocol, attributes were evaluated in blocks (Fig. 1). Prior to the experiment, the attribute blocks were defined and discussed with the panelists. The first attribute block focused on taste perception and mouthfeel (attributes of block 1: salty, bitter, astringent, mouthfeel, and granularity), for which panelists wore nose clips. The next two attribute blocks focused on olfactory perception (attributes of block 2: pea, broth, nuts, almond; attributes of block 3: potato, grains and other), and panelists did not wear nose clips during the evaluation. For each block, samples were presented monadically in sequence: panelists evaluated all the attributes in the block (printed on the same survey page) for each sample. The samples order was the same for the three blocks for one subject (but different between subjects). In addition, the first sample (P1) in each session, for the three blocks, was also the reference used to limit drift between sessions. This reference was available in large quantities and was stored under highly stable conditions.

Data analysis

All the analyses were performed using XLStat (Addinsoft, 2017, Paris, France) and R (R Core Team, 2017).

For analyses of an inferential nature, we used α = 0.05 to determine statistical significance.

Based on the results of the PROP test, we defined three groups of panelists. In the low PROP taster (LPT) group were panelists who had rated sodium chloride as having a more intense taste than PROP. Panelists who gave similar ratings to sodium chloride and PROP were in the medium PROP taster (MPT) group. Those who had rated PROP as having a more intense taste than sodium chloride were in the high PROP taster (HPT) group. Differences among groups were analyzed using repeated-measures analysis of variance (ANOVA) and post-hoc comparisons (Newman-Keuls method).

To analyze the sensory profiling results, in a classical way, we carried out three-way ANOVAs. There were two sets of models: models using the classical protocol data and models using the block protocol data. Within each set, there was one model focused on the results for a single attribute. For all the models, sample type, replicate, and panelist identity were the fixed effects, and all first-order interactions were included. When the fixed effects were significant, we carried out post-hoc comparisons (Newman-Keuls method). We also performed additional three-way ANOVAs and Newman-Keuls tests to assess the effect of PROP status on the intensity scores for the 11 attributes: sample type, replicate, PROP status and panelist identity (nested factor with PROP status) were the fixed effects, and all first-order interactions were included. To visually explore differences in the results obtained using classical versus block protocol, we carried out multiple factor analysis (MFA) (centered by group; Pearson type); the data from both protocols were averaged across replicates and panelists.

RESULTS

Assessing panelist performance

We examined how well panelists performed (reproducibility and homogeneity) when using classical versus block protocol to evaluate attribute intensity. Table 2 presents the results from the three-way ANOVAs that used the scoring data for each attribute from each of the protocols. The interaction between replicate and sample type were not significant (except in the case of granularity as evaluated with classical protocol).

Replicate was significant for both protocols for all the attributes and the interaction between panelist and replicate was not significant for potato, nuts, broth, grains and bitter for the classical protocol and, granularity for the block protocol. But the F values for these factors are low compared to the F values for the effects produced. Nevertheless, these effects indicated a low level of repeatability of the panel and ask to be circumspect for the following analysis. The interaction between panelist and sample type was significant for seven attributes and one attribute for the block and the classical protocols, respectively. Such interactions are common when sensory attributes are evaluated using unstructural scales and are difficult to control even when panelists have undergone extensive training [START_REF] Lawless | Comparison of rating scales: sensitivity, replicates and relative measurement[END_REF][START_REF] Jourjon | Comparison of different scaling techniques for sensory analysis of wine[END_REF]. Nevertheless, the block protocol seems to enhance differences between products and panelists. These results nonetheless suggest that the panelists generally came up with repeatable and homogeneous scores, even if there was some disagreement for some attributes.

Comparison of the classical protocol and block protocol results

In the three-way ANOVAs, sample type was significant, revealing that there were differences in how the different pea protein solutions were perceived (Table 2). When the block protocol was used, panelists discriminated among the six sample types for all the attributes; for the classical protocol, the same was true for eight of the attributes (except pea, grains and astringent). For the block protocol, the greatest degree of discrimination was observed with the attributes nuts and mouthfeel (F = 21.61 and F=21.14); for the classical protocol, it was with granularity (F=10.04). For the block protocol, there was some degree of discrimination displayed in granularity, pea, and almond (resp. F=8.67, F=9.19, F=8.26) as well with grains and astringent (resp. F=4.54, F=4.10), although F-values were weaker in the latter case. In contrast, with classical protocol, panelists did not discriminate among the sample types for the attributes astringent, grains, and pea.

Consequently, the block protocol allowed a greater degree of discrimination among a larger number of attributes. Table 3 presents the results of the Newman-Keuls groups associated for the evaluation of pea protein isolates for significant attributes with the classical and the block protocols. Overall products are ranked in the same way. Nevertheless there is a higher number of different groups with the block protocol for the attributes bitter, astringent, mouthfeel, pea, broth and grains (respectively from 2 to 4, and 1 to 3 groups).

The results of the MFA are shown in Figure 2. The RV coefficient was 0.778, which means that there There was a good correlation between the results obtained with the two protocols (RV coefficient = 0.778) [START_REF] Josse | Testing the significance of the RV coefficient[END_REF]. The first two axes accounted for a large proportion of the variance (81.01%) in the sensory profiles.

The loadings of maps for the attributes showed that evaluations of granularity were well correlated between the two protocols, as were evaluations of broth, salty, mouthfeel, bitter, nuts, and almond. The only attribute for which this was not the case, was potato. This is because of higher potato intensity scores when panelists used block protocol, especially for P1 (5.31 for block profiling vs. 2.68 for classical profiling). This result means that scores of potato intensity were different when panelists used classical versus block protocol. It may be that, with the classical protocol, it was easier to confuse the potato attribute with other attributes; in contrast, block protocol might have limited such confusion. Along the first axis (52.95% of variance explained), sample types were mainly differentiated based on the attributes nuts, mouthfeel, almond, and granularity. Along the second axis (28.05% of variance explained), sample types were mainly differentiated based on the attributes salty and potato. The sample types evaluated with classical protocol are mostly distributed along the first axis, which means that panelists mainly used a single dimension when differentiating among sample types. In contrast, the sample types evaluated with block protocol are distributed along axes 1 and 2, which means that panelists used two dimensions in the discrimination process. Overall, this finding indicates that there was a greater degree of discrimination among sample types when block protocol was employed.

For both protocols, panelists had the possibility to complete their descriptions with another attribute in 'Other'.

However, with both protocols, this attribute has been used only rarely and never in a consensual way. These results do not allow us to conclude on the usefulness of such an attribute.

PROP status and its relationship with the block protocol results

Among the 17 individuals who evaluated pea protein isolates in solution with both protocols, 7 were assigned to the HPT group (PROP intensity > sodium chloride intensity) and 5 were assigned to the LPT group (PROP intensity < sodium chloride intensity). The 5 panelists who did not perceive a difference between PROP and sodium chloride were assigned to MPT group. We used the block protocol data and the classical protocol data for the P1-P6 solutions to examine whether panelist PROP status influenced attribute evaluation (Table 4). The ANOVA results show that PROP status affected the panelists' evaluation of 9 of the 11 attributes (all except mouthfeel and granularity) for block protocol and 9 of the 11 attributes (all except mouthfeel and pea) for classical. Table 5 presents the results from the Newman-Keuls post-hoc comparisons. With block protocol, in contrast to the HPT and MPT groups, the LPT group perceived all the attributes as less intense (all except mouthfeel and granularity which are not significant). With classical protocol, LPT group perceived the products as less intense in astringent, granularity and aroma attributes (except pea attribute). The differences between the two protocols therefore depend on the attributes. Nevertheless, for bitter attribute, block protocol allow the expression of a greater difference between PROP status populations.

DISCUSSION

We found that classical and block protocols yielded correlated results. However, the block protocol allowed better discrimination among sample types. These differences were likely due to the differences in the presentation schemes, which activated different cognitive processes. In past research, monadic presentation has been found to be faster and less tiring than comparative presentation [START_REF] Mazzucchelli | Comparative of monadic and simultaneous sample presentation modes in a descriptive analysis of milk chocolate[END_REF]; however, comparative presentation allows panelists to detect smaller differences among foods and to make more accurate decisions about these relative differences [START_REF] Mcbride | Hedonic rating of food: single or side-by-side sample presentation[END_REF][START_REF] Saint-Eve | Influence of Proteins on the Perception of Flavored Stirred Yogurts[END_REF]. Here, our classical protocol employed a monadic presentation scheme, which means that panelists based their attribute evaluations on their memory of the references from the training period. They thus had to activate both their long-term and short-term memory. In contrast, in our block protocol, the presentation scheme combined both monadic and comparative elements. Consequently, the panelists could base their attribute evaluations on both their memories from the training period as well as on the reference sample, which was the first sample in the sequence [START_REF] Hastie | The relationship between memory and judgment depends on whether the judgment task is memory-based or on-line[END_REF]. As a result, they might have needed to rely less on their long term-memory than when using the classical protocol.

The differences in the results obtained with the two protocols could also be rooted in bias. For example, during sensory evaluation, one of a food's attributes can strongly influence or bias the perception of the food's other attributes (halo effect) [START_REF] Thorndike | A constant error in psychological ratings[END_REF], or, in contrast, the absence of a potentially salient attribute from a food can affect how the food's other attributes are perceived (dumping effect) [START_REF] Clark | Limiting response alternatives in time-intensity scaling: an examination of the halo-dumping effect[END_REF]. By having panelists evaluate attributes in groups, block protocol may reduce correlations among attributes and thus limit halo and dumping effects. Such factors could explain the distribution patterns along the first and second axes in the MFA (Fig. 2).

Here, we asked panelists to evaluate sapid and texture attributes with nose clips. If nose-clip evaluation does not mimic what happens when people taste the products in real life, it allows to dissociate chemical interactions from cognitive interactions in order to study the relationships between sensory data and instrumental data to move towards an understanding of the mechanisms of sensory perceptions [START_REF] Abegaz | Partitioning taste from flavourtic flavor notes of fresh tomato (Lycopersicon esculentum, Mill) to develop predictive models as a function of volatile and nonvolatile components[END_REF].

In term of practicality, the block protocol takes more time than the classical protocol for products evaluation (about 20 min versus 15 min). Although using the nose clip for only one of the attributes blocks requires to panelists to put on and remove it during the evaluation, panelists found it easier to evaluate perceptions than classical protocol in particular due to the focus on a smaller number of attributes. In addition, the use of the reference, which makes sample preparation more cumbersome, was greatly appreciated by the panelists to help them to rate the products. In summary, with this explorative study these products, block protocol is more efficient (better repeatability and discrimination) than classical protocol, and its use when evaluating foods with complex, multidimensional sensory properties, such as foods incorporating pea protein isolates would be interesting to consider.

Nevertheless, due to the high presence of the replicate effect, particularly in the classical protocol, and to the low number of evaluated products, as well as the limited number of subjects used in the panels, results have yet to be confirmed by other studies. This research has also improved our knowledge of how pea proteins are perceived. Plant proteins are often described as "beany," a multidimensional and ambiguous descriptor. In our study, we decided against using the term beany. Instead, we parsed out its multiple components and expressed them via other terms. Indeed, in the literature, the definition of beany varies across consumers and food types. For example, lupin flour has been described as having beany/green, mushroom/soil, floral, meaty, nutty, woody/green, sweet, and baked attributes [START_REF] Kaczmarska | Flavour characteristics of lupin and soybean after germination and effect of fermentation on lupin flavour[END_REF], and pea and soybean have been described as having beany, green, fresh, and grassy attributes [START_REF] Troszyńska | The effects of germination on the sensory quality and immunoreactive properties of pea (Pisum Sativum L.) and Soybean (Glycine max)[END_REF]. As a result, the use of beany as a descriptor is sometimes avoided [START_REF] Lawrence | Preference mapping of soymilk with different U.S. consumers: preference mapping of soymilk[END_REF]. For example, one study allowed cooked peas to only be described with the attributes pea, cooked vegetable, earthy, brothy, grainy, and hay like [START_REF] Malcolmson | Flavour and Flavour Properties of Saskatchewan Grown Field Peas ( Pisum Sativum L.)[END_REF].

In our study, pea protein solutions could be described using attributes such as potato, pea, grains, broth, almond, and nuts. This definition of pea flavor (in particular the following attributes: green/pea, grain/starchy, nuts, and broth) is consistent with the definition developed by Vara-Ubol, Bott, and Chambers [START_REF] Bott | Sensory characteristics of combinations of chemicals potentially associated with beany flavour in foods[END_REF][START_REF] Vara-Ubol | Sensory characteristics of chemical compounds potentially associated with beany flavour in foods[END_REF], where beany is a combination of musty/earthy, musty/dusty, and one or more of the characteristic attributes green pea pod, nutty, brown, sour, starchy, and powdery. However, there are some differences in the beaniness of pea proteins (as illustrated in our results) and the beaniness of proteins in soybeans, green peas, or lupin flour. More specifically, the attributes musty/dusty and earthy/mushroomy are not associated with pea proteins.

Another aspect of our study is that we found a relationship between panelist PROP status and evaluations of pea protein solutions. The percentage of HPTs and LPTs that we identified among our panelists generally concurs with what has been reported in the literature. However, it is important to note that there is pronounced variability among populations worldwide (India: 26% HPT and 42% LPT; West Africa: 3% LPT; China: 6%-23% LPT; North America [Caucasians]: 30% LPT [START_REF] Deshaware | Genetic variation in bitter taste receptor gene TAS2R38 , PROP taster status and their association with body mass index and food preferences in Indian population[END_REF][START_REF] Guo | The genetics of phenylthiocarbamide perception[END_REF].

Here, we found that PROP status affected evaluations of bitterness (as also shown by [START_REF] Dinehart | Bitter taste markers explain variability in vegetable sweetness, bitterness, and intake[END_REF] as well as evaluations of other sapid and flavor attributes. In particular, LPTs seemed to have a harder time discriminating among sample types for a number of attributes; the only attribute for which there was evidence of discrimination was a texture attribute, which suggests that mechanisms underlying texture perception are very different from those underlying chemical perception. However, some researchers have been questioning whether PROP status is a global indicator of taste function given that the PROP test only measures sensitivity to one bitter compound [START_REF] Lim | Measures of individual differences in taste and creaminess perception[END_REF][START_REF] Webb | The relationships between common measurements of taste function[END_REF]. That said, certain studies have shown that, compared to LPTs, MPTs and HPTs do perceive a variety of compounds with greater intensity [START_REF] Fischer | The Associations between 6-n-Propylthiouracil (PROP) intensity and taste intensities differ by TAS2R38 Haplotype[END_REF][START_REF] Hayes | Supertasting and PROP bitterness depends on more than the TAS2R38 gene[END_REF]. Therefore, to improve the quality of food descriptions and discrimination, sensory studies might want to strictly employ panelists who are HPT or MPT. Alternatively, researchers could control for panelist PROP status when carrying out the analyses.

CONCLUSIONS

In this study, we drew upon classical sensory protocol to develop a block protocol in which attributes are evaluated in groups. Panelists evaluated samples that were presented monadically and sequentially; for each evaluation sequence, the first sample in the series was the reference. This protocol made it easier to evaluate samples with strong persistent attributes, such as bitterness or astringency, and multidimensional attributes, such as beaniness. The use of a reference sample helped limit drift between sessions. The block protocol made it possible for panelists to provide a more complete sensory characterization of the samples, which included a large number of attributes. In this study, the use of nose clips limited interactions between taste and flavor with a view to dissociate cognitive interactions from chemical interactions. We also assessed the PROP status of our panelists, which allowed us to better understand each panelist's evaluations. Furthermore, our results suggest that PROP status could help researchers select panelists based on sensory sensitivity. In closing, our overall findings should improve the characterization and formulation of innovative products based on pea proteins. Nevertheless, these results have yet to be confirmed for its use when evaluating other types of products. In particular, foods with complex, multidimensional sensory properties, such as foods incorporating pea protein isolates would be interesting to consider.

ACKNOWLEDGMENTS

This work was supported by Roquette (Lestrem, France). The authors thank David Forest for his technical support and the panelists for their participation in this research.

Table 1: Definition of the sensory attributes evaluated by the panelists for the pea-protein-based sample types.

Attributes

Attributes in French Definition

Salty

Fig. 1 :Fig. 2 :

 12 Fig. 1: Schematic representation of the profiling protocols used in this study. (a) Classical protocol. (b) Block

Table 2 :

 2 Results of the three-way ANOVAs examining panelist performance and comparing the two protocols during the experiment where attribute intensity for pea protein solutions was evaluated using the classical protocol (CP) and the block protocol (BP) (degrees of freedom: 118; residual degrees of freedom: 85).

		Salé	A fundamental taste -sodium chloride is a typical example
	Bitter	Amer	The fundamental taste associated with a caffeine solution

Significant p-values are in bold (α = 0.05).

Table 3 :

 3 Results from the Newman-Keuls post-hoc comparisons for the evaluation of pea protein isolates in water solutions by different protocol. (CP) Classical protocol. (BP) Block protocol. Nb_group number of Newman-Keuls group. Significant differences between groups are indicated by differences in letters.

Table 4 :

 4 Effects of panelist PROP status on the evaluation of the attribute intensities of pea protein solutions from the results from the ANOVAs (degrees of freedom: 34; residual degrees of freedom: 169). Significant pvalues are in bold (α = 0.05). (CP) Classical protocol. (BP) Block protocol.

	HPT -BP	a	a	a	a	a	a	b	a	b	a	a
	HPT -CP	a	a	a	a	a	a	a	a	a	a	a
	MPT -BP	a	a	a	a	a	a	a	a	a	a	a
	MPT -CP	a	a	b	a	a	a	ab	ab	ab	a	ab
	LPT -

Nb_group number of Newman-Keuls group. Significant differences between groups are indicated by differences in letters.

Salty Bitter Astringent Mouthfeel Granularity Pea Broth Nuts Almond Potato Grains