Use of different panellists (experienced, trained, consumers and experts) and the projective mapping task to evaluate white wine

Food Quality and Preference 83 (2020) 103900

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Use of different panellists (experienced, trained, consumers and experts) and the projective mapping task to evaluate white wine Alanah Barton, Lydia Hayward, Connor D. Richardson, Matthew B. McSweeney

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School of Nutrition and Dietetics, Acadia University, 15 University Ave, Wolfville, NS B4P 2R6, Canada

ARTICLE INFO

ABSTRACT

Keywords: Experienced panellists Wine Projective mapping Ultra-flash profiling Consumers Trained panellists

A panellist’s experience with a specific sensory method can influence product differentiation during a sensory task. Projective mapping (PM) and ultra-flash profiling (UFP) have been used extensively to create a description of food products. The objective of this study was to identify how familiarization with a specific sensory method can influence the results. As such this study compares the results of a PM and UFP task completed by experienced panellists (n = 17) to the results from trained panellists (n = 11), naïve consumers (n = 82) and individuals who are employed in the wine industry (experts; n = 12). All panellists evaluated eight single varietal white wines produced in Nova Scotia, Canada. The experienced panellists in this study had experience with the PM task; however, they did not have experience or knowledge about the products being tested (wine). There was no significant correlation between naïve consumers and the experienced panellists (RV = 0.431). However, there was a high similarity between the results of the experienced panellists and the trained panellists (RV = 0.768). The experts' results were significantly different from the other participants (trained, experienced and consumers). The results of this study indicate that knowledge of the sensory method effects the panellists’ evaluations and that experienced panellists may be a viable option to evaluate food products when time, resources or samples are limited. More studies need to be done to explore the use of experienced panellists to evaluate different food products.

1. Introduction Generally, panellists involved in sensory panels can be selected from two categories based on the purpose of the study, either consumer panellists or descriptive (trained) panellists (Sáenz-Navajas et al., 2016). Trained panellists have been well established in sensory science and are recognized as being the standard when trying to establish the sensory properties of a given food item (Ares & Varela, 2017). Trained panellists are selected based on their sensory acuity for basic characteristics (tastes, odours and textures) and their ability to discriminate among products (Ares & Varela, 2017; Stone, Bleibaum, & Thomas, 2012). After selection, the panellists receive extensive training to gain a knowledgeable background of the food products being tested, as well as the method of evaluation (Castura, Findlay, & Lesschaeve, 2005). Trained panellists can provide researchers with precise and reliable information to be used during food product development (Ares & Varela, 2017). Specifically, trained panellists can distinguish small differences in food products (Ares & Varela, 2017). Conversely, consumers or untrained panellists are usually used to complete hedonic tests where they are asked to indicate their

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preferences (Lawless & Heymann, 2010). It is unlikely that the untrained panellists are able to differentiate the small differences between the products, but they can indicate what products are acceptable and provide the consumer perspective (Morin, Hayward, & McSweeney, 2018). An issue with consumer panels is that the panellists may not understand the method, or the questions being asked of them, and this can lead to unreliable results. However, studies involving trained panellists are time consuming and expensive due to the long training period (Sáenz-Navajas et al., 2016). Another group of panellists that can be considered for sensory panels is experienced panellists. Experienced panellists are individuals who are highly experienced with the food product being tested, the method being used or have received extensive training before the testing (Michon & McDonnell, 2008). Previous research has investigated experienced panellists who do not have extensive knowledge of the food product being tested, but rather those who have a significant understanding of and experience with the sensory method being used (Morin et al., 2018). This study focused on panellists who participated in projective mapping and ultra-flash profile trials previously and understood the method. It asked the panellists to evaluate seven cookies

Corresponding author. E-mail address: [email protected] (M.B. McSweeney).

https://doi.org/10.1016/j.foodqual.2020.103900 Received 6 November 2019; Received in revised form 31 January 2020; Accepted 3 February 2020 Available online 04 February 2020 0950-3293/ © 2020 Elsevier Ltd. All rights reserved.

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made with different cereal grains (Morin et al., 2018). The researchers found that the experienced panellists similarly evaluated the cookies to the trained panellists. However, there was limitations to the study, including the samples involved in the trial, as the cookie is a simple (not very complex) food product (Morin et al., 2018). Building on the results, this study will instead investigate wine. Specifically, this study will investigate single varietal white wines produced in Nova Scotia, Canada. Wine is a complex product based on both grape composition and technology used during the winemaking process (Sáenz-Navajas et al., 2016). When evaluating wine, panellists need to consider appearance, odour, taste, and mouthfeel (Rahman & Reynolds, 2015). Studies investigating the differences among trained and consumers when evaluating wine in sensory trials have resulted in inconsistencies. Some studies have demonstrated that trained panellists are more accurately able to characterize wine and identify subtler differences between products (Torri et al., 2013). On the other hand, negligible differences have been found between the descriptors provided by the trained and untrained participants when evaluating wine (Liu, Grønbeck, Di Monaco, Giacalone, & Bredie, 2016). Instead of trained or untrained panellists, many studies have consisted of sommeliers, winemakers and others involved in the wine and grape industry, as they have extensive knowledge of products being tested (Coulon-Leroy, Symoneaux, Lawrence, Mehinagic, & Maitre, 2017; Franco-Luesma, HonoréChedozeau, Ballester, & Valentin, 2019; Grohmann, Peña, & Joy, 2018; Lehrer & Lehrer, 2016; Sáenz-Navajas et al., 2015). These individuals can be considered to be experienced panellists; however, they have extensive knowledge of the products being tested instead of the sensory method to be used. These panellists are usually referred to as experts in the literature (Franco-Luesma et al., 2019; Grohmann et al., 2018; Sáenz-Navajas et al., 2015). As stated above, trained panellists and trained panels are timeconsuming, and need lots of resources and money. As such, sensory scientists have developed less time-consuming and flexible methodologies that allow panellists to identify differences in the food items (Valentin, Chollet, Lelièvre, & Abdi, 2012; Varela & Ares, 2012). These methodologies can be adapted to panels with different degrees of training (Ares & Varela, 2017; Liu et al., 2016). One of these methods is projective mapping (PM). Projective mapping asks consumers to represent similarities and differences between products (Jervis & Drake, 2014) and leads to a graphical representation of a product to understand relationships between products (Risvik, McEwan, Colwill, Rogers, & Lyon, 1994). Projective mapping is considered to be a holistic approach that allows participants to express similarities and differences, as well as group samples by positioning them on a two-dimensional plane using either a piece of paper or a computer screen (Santos et al., 2013). Projective mapping has been used to evaluate a number of different food products including apples (Pickup, Bremer, & Peng, 2018), chocolate (Kennedy & Heymann, 2009), powdered drinks (Ares, Varela, Rado, & Giménez, 2011), granola bars (Kennedy, 2010), sausages (Horita et al., 2017), strawberries (Vicente, Varela, Saldamando, & Ares, 2014), sweet potatoes (Vicente et al., 2017), teas (Moelich et al., 2017), and yogurts (Cruz et al., 2013; Esmerino et al., 2017). As well, numerous studies have used projective mapping to evaluate wine (Fariña et al., 2015; Heymann, Hopfer, & Bershaw, 2014; Hopfer & Heymann, 2013; Perrin & Pagès, 2009; Ross, Weller, & Alldredge, 2012; Smith & McSweeney, 2019; Torri et al., 2013). Projective mapping is usually paired with ultra-flash profiling (UFP). In the UFP method, participants are asked to use their vocabulary to list adjectives next to each sample after they complete their projections (Dehlholm, Brockhoff, Meiner, Aaslyng, & Bredie, 2012). Projective mapping and UFP has been used to evaluate numerous different food products including apples (Pickup et al., 2018), brandy (Louw et al., 2015), caramel corn (Mayhew, Schmidt, & Lee, 2016), cheese (Santos et al., 2013; Smith & McSweeney, 2019), hops (Barry, Muggah, McSweeney, & Walker, 2018), thickened liquids for those living with dysphagia (Ong,

Table 1 Description of wines assessed in the PM and UFP sessions. All wines were manufactured in Nova Scotia, Canada. Wine

Grape Variety

Vintage

Price

Alcohol (% v/v)

Closure of Wine

W1 W2 W3 W4 W5 W6 W7

L’Acadie L’Acadie L’Acadie Seyval Blanc Seyval Blanc Riesling Riesling

2016 2016 2016 2016 2016 2016 2016

$19.99 $21.99 $24.99 $16.99 $20.99 $17.99 $23.99

11.0% 11.0% 9.0% 12,0% 11.0% 12.0% 13.0%

Cork Cork Screw Top Cork Screw Top Cork Cork

Steele, & Duizer, 2018), and wine (Wilson, Brand, du Toit, & Buica, 2018). In this context, the objective of the present study is to compare the results of a projective mapping and ultra-flash profile task on single varietal white wines completed by experienced panellists to trained panellists, naïve consumers, and individuals employed in the wine industry (experts). For this study, experienced panellists will be defined as those who have a significant understanding of the projective mapping method and do not have experience with the products being tested. 2. Materials and methods 2.1. Samples Each session evaluated seven single varietal white wines produced in Nova Scotia (NS), Canada. One sample was presented twice (W5), so each participant received eight wine samples in all of the PM and UFP sessions. The wines selected are a representative sample of the single varietal white wines sold in NS; based on a focus group (n = 11) with individuals involved in the wine industry. Table 1 describes the different single varietal white wines involved in this study. All wines were purchased at the local liquor store in Wolfville, NS and are available across the province. In all sessions, samples were prepared and presented following the same procedure. All wines were assessed by a wine professional to confirm the absence of cork taint. All wine bottles were opened 10 min before testing and were served at 4 °C. The samples (30 mL) were presented in a clean standard ISO wine glass. The samples were all labelled with random three-digit codes, both on the wine glass and a placemat. Each sample was placed on the placemat on a white tray on top of their designated three-digit code. Each participant received the samples in a randomized order. The three-digit codes differed between the sessions. Along with the eight samples, each tray contained a glass of filtered water to allow the participants to cleanse their palates. 2.2. Participants 2.2.1. Experienced panellists The experienced panellists were composed of 17 panellists who had participated in PM and UFP trials previously and understood the method (7 males and 10 females; mean age of 33.8 +/− 6.4). The participants had completed an average of 4.6 PM and UFP sessions (Supplementary Table 1). Also, the panellists were screened to ensure that they had not participated in a trained panel or wine sensory trial previously. 2.2.2. Trained panellists The trained panel was composed of 11 participants (4 males and 7 females; mean age of 26.1 +/− 5.4). Panellists had previously participated in two trained panels aimed at describing white wines produced in NS and five trained panels aimed at describing wine (red, white and sparkling) made in NS. A brief description of the training the panellists 2

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underwent is included below. The trained panel procedure is based on the procedure by Lawless and Heymann (2010). The panellists were trained for 12 sessions of approximately 1 h each. Aroma, taste and mouthfeel attributes were generated by the panellists, as well as reference standards for these attributes. In total, 22 attributes were generated by the panel and used in the wine evaluation. The trained panel evaluated 12 different white wines produced in NS. During the training session, the definition and the procedure for evaluating each attribute were discussed in detail. The references and reference values were selected through discussion with the panellists. During the final stages of training, the intensity of each attribute was evaluated on 15 cm unstructured line scales in relation to the values assigned to the references. The panellists’ results on the 15 cm line scales were evaluated to ensure they were consistent. After the training, evaluations of the white wines by the trained panel were completed (testing phase) following standard sensory testing protocols, and the data was entered onto paper ballots (three replicates). When the trained panellists completed the PM and UFP task, they followed the procedure outlined below (Section 2.4 Projective Mapping and Ultra-Flash Profiling).

2.2.4. Experts Twelve individuals (sommeliers, winemakers, wine professionals, grape growers) from the NS wine community were recruited (38.2 +/ − 5.6). All of the individuals had at least three years of experience working in the wine industry, had not participated in a PM and UFP trial before and were considered experts based on the criteria by Parr, White, and Heatherbell (2004). 2.3. Projective mapping and ultra-flash profiling The Acadia University Research Ethics Board approved this study (REB 17-33). All four sessions took place on separate days, and new wine bottles were opened on the day of the testing. All sessions followed the same procedure. Participants received a consent form containing information about the study and its purpose before the start of the session. Instructions were given verbally to participants before the trial on how to use the projective mapping technique. The moderator demonstrated the procedure (PM and UFP) for the panellists by placing sandwich cookies samples on the screen created by the Compusense Cloud software. The eight samples were presented simultaneously to the participants. The participants were informed that they could evaluate the samples in any order they preferred. Participants were asked to assess their overall perception of the wines. They were informed to perform the task according to their own assessment criteria, and there were no right or wrong answers. Participants were asked to evaluate a sample and then place the sample on the 2-dimensional plan on the computer screen. The participants were also informed that there is available space for them to physically move the wines around the booth before placing them on the computer screen. They were instructed to place those wines they perceived to be similar closer together and those that are different further away from each other (Risvik et al., 1994). The PM task was paired with ultra-flash profiling, which asks panellists to provide descriptive words for each of the samples (Perrin et al., 2008; Varela & Ares, 2012). The participants were presented with a list of terms, as well as an option to add their own terms. Panellists could either select attributes from the provided list (30 words based on previous work), as well as add their own terms to describe the wine sample. The 30 descriptors (sweet, crisp, tree fruit, pear, light flavor, sour, citrus, tropical fruit, apple, aftertaste, pineapple, dried fruit, astringent, phenolic, lactic, floral, earthy, berry, strong, apricot, minerallike, burned, bitter, dry, caramel, acidic, wood, dried vegetables, oak, petroleum) were taken from an ongoing study by the researchers. The ongoing study has involved creating a consumer language to describe wines made in Nova Scotia, Canada and specifically five trials, using PM and UFP, have been completed to describe white wines. Participants were asked to avoid using descriptions that compared two or more wines to each other (e.g. Sample 473 was sweeter than sample 901). Participants were encouraged to take as many sips or sniffs as necessary to evaluate the wine. In between evaluating samples, participants were instructed to take a drink of water. When the participants had completed the task, participants were asked a variety of questions about demographics and wine consumption habits.

2.2.3. Consumers Eighty-two consumers from the Annapolis Valley, NS community (38 males and 44 females; mean age 33.9 +/− 12.1) participated in the study (Table 2). They were screened to ensure they had not participated in a PM and UFP trial before, did not have extensive knowledge of sensory evaluation, wines or work in a sensitive industry (wine, grape, sensory analysis), had consumed wine in the last two weeks and considered themselves to be regular wine drinkers.

Table 2 Demographics of the consumer panellists (mean age 33.9 +/−12.1). Consumers (n = 82) Gender Education

Income

Frequency of white wine consumption

What is your preferred alcoholic beverage?

What term best describes your interest in wine? What term best describes your knowledge of wine? How much do you typically spend on a bottle of wine (CAD)?

Male Female High School Post-Secondary Certificate or Diploma Bachelor or Above Less than $25,000 $25,000- $44,999 $45,000- $64,999 $65,000- $99,999 $100,000- $149,999 $150,000+ Prefer not to answer Several times a week Approximately once a week 1–3 times a month Once every 3 months Wine Beer Spirits Cider Other No interest Limited interest Interested Highly interested No knowledge Limited knowledge Knowledge Highly knowledgeable Up to $20.99 $21.00–30.99 $31.00–50.99 $51.00 and higher

46% 54% 25% 33% 42% 16% 10% 15% 33% 8% 6% 12% 7% 32% 58% 3% 59% 20% 11% 9% 1% 0% 15% 64% 21% 4% 75% 20% 1% 70% 27% 3% 0

2.4. Environment All PM and UFP sessions were conducted in the Centre for Sensory Research of Food at Acadia University. The participants performed the testing within white individual sensory booths, under ambient lighting and controlled temperature. 2.5. Statistical analysis The results from each PM and UFP session were analyzed separately. The Compusense Cloud Software recorded the location of the coordinates from each participants’ product map (x and y coordinates). Frequency counts were completed on the attributes identified by each 3

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participant to describe the wines. All intensities of attributes (ex. slightly sweet, sweet and very sweet) were considered to be separate attributes. Multiple Factor Analysis (MFA) was then used to analyze the results for each session. MFA first normalizes each set of variables followed by principal component analysis (Abdi, Williams, & Valentin, 2013). The PCA functions to analyze the variance that can occur from the various observations made by the participants (Abdi et al., 2013) and is achieved by using the x and y coordinates for each sample, from each participant, and rotating them until a common trend is found (Abdi et al., 2013). These factor scores are then used to create a plot that is representative of similarities between participants’ observations found during the testing (Abdi et al., 2013). MFA also calculates the total number of times each attribute was cited for each product. The analysis included only descriptors identified by the participants four or more times in each session (Kemp, Pickering, Willwerth, & Inglis, 2018). RV coefficients and their significance were calculated to determine the degree of similarity between the PCA bi plot (only the first two dimensions) resulting from the different panellists (trained, consumers, experts and experienced). The RV coefficient is a multivariate similarity coefficient used to measure the extent to which two different configurations are similar (Louw et al., 2013). RV coefficients range between 0 and 1, with values closer to 1 indicating a higher degree of similarity (Abdi, Valentin, Chollet, & Chrea, 2007). All analysis was done on the computer using XLSTAT® software (Version 2018.2, New York, N.Y., U.S.A.) in Microsoft ExcelTM.

experienced panellists (56.0%). Once again, the duplicated sample was grouped together (W5.1 and W5.2), as well as the Riesling wines (W6 and W7). The trained panellists evaluated the L’Acadie wines similar to the experienced panellists, as W1 and W2 were grouped, while W3 (L’Acadie) was further away. Lastly, the Seyval Blanc wines, were again, separated by the second dimension, with W4 on the positive side and W5 on the negative side of the second dimension. The trained panellists used fewer words, 41, compared to 68 descriptors used by the experienced panellists to describe the wines (before removing those attributes used three times or less). However, there were less trained panellists (n = 12) than experienced panellists and this may explain why less terms were used. As stated above only the terms that were used four or more times were included in the analysis. The L’Acadie wines (W1 and W2) were again associated with bitter, wood, honey and petroleum and the Riesling wines (W6 and W7) with berry, apples, dried/cooked fruit and grassy. Overall, the results lead to clear trends being presented in the product map, the first dimension had wood, bitter, and berry on the positive side and pineapple, earthy and strong on the negative. The second dimension separated the wines with oak, yeasty and petroleum on the positive side and sweet, grassy and dried/cooked fruit on the negative. The descriptions of the wines were similar to what was observed with the experienced panellists. 3.3. Consumers The same wines, following the same procedure were evaluated by consumers (n = 82; Table 2). The product map obtained from the MFA is illustrated in Fig. 1c. The first two dimensions of the MFA explained 38.9%, the lowest amount of the four different panellist groups. The consumers used 79 different terms to describe the wines, however only 40 terms were used four or more times and were included in the analysis. Additionally, the researchers observed that the consumers were more likely to add intensities to the attributes, such as slightly sweet. The two L’Acadie wines (W1 and W2) were again grouped tightly together and were associated with smooth, buttery, and berry attributes. In agreement with the other groups of panellists, W3 was separated from the other L’Acadie wines and described as watery, dried fruit and fresh vegetables. However, the Riesling wines (W6 and W7) were much further apart and differed in the descriptors used when compared to the results from the trained and experienced panellists. The duplicate samples (W5.1 and W5.2) were much further apart on the product map as well, indicating that the consumers may not have known these samples were the same wine. Overall, the consumers did not seem to be able to separate the wines based on the grape variety or the winery, used many terms to describe the wines, and did not seem to agree with each other.

3. Results 3.1. Experienced panellists The first and second dimensions of the MFA explained 56.0% of the variance of the experimental data (Fig. 1a). The panellists grouped the W5 (W5.1 and W5.2) sample, which was presented twice, together. The placement of these two identical wines demonstrates that the participants were able to distinguish their similarity and supports their ability to reproduce these results. On the first dimension of the bi plot, the Seyval Blanc wines (W4, W5.1, W5.2; negative) are separated from the Riesling wine (W6 and W7) and two of the L’Acadie wines (W1 and W2). The Seyval Blanc wines were separated on the second dimension with the duplicated W5 (W5.1 and W5.2) being on the negative side, while the W4 is on the positive side. The sensory attributes generated by the participants were examined to determine how they described white wines. The experienced panellists used a total of 68 terms to describe the white wines, however only those mentioned four or more times were included in the analysis. The Riesling wines (W6 and W7) were associated with watery, grassy, apples, berry, dry, sulphur and dried/cooked fruit. The L’Acadie wines (W1 and W2), opposite the Riesling wines on the second dimension, were associated with petroleum, mineral-like, burned, honey, bitter, dried vegetables, wood, mouldy, buttery, and oxidized. The Seyval Blanc wines (W4, W5.1 and W5.2) were all located on the negative dimension of F1 but were differentiated by the second dimension. W5.1 and W5.2 were associated with caramel, floral, tree fruit, pungent, apricot, sweet, pear, crisp, earthy and light flavour, while W4 was associated with strong, oak, spicy, resinous, canned/cooked vegetables, tropical fruit, lactic, astringent, nutty, pineapple, citrus, fresh vegetables, yeasty and aftertaste (positive) and was grouped with W3 (L’Acadie). The same winery produced both of these wines (W3 and W4) and similar processing techniques may have led to these wines being grouped.

3.4. Experts Lastly, the wines were evaluated by experts from the NS wine industry (n = 12; sommeliers, winemakers, wine professionals, grape growers), and the results were analyzed using MFA (Fig. 1d). The first two dimensions of the MFA explained 43.2% of the variance. The experts grouped the duplicated wine samples together (W5.1 and W5.2). However, the other wines are not grouped together based on the grape variety used in production. W2, W4 and W6 are placed together, and they are made from three different grapes, L’Acadie, Seyval Blanc and Riesling, respectively. W1 (L’Acadie) is separated from all of the other wines. The sensory attributes generated by the expert participants were examined to determine how they described the wines. The experts used 57 terms to describe the wines, and the majority of terms (44) were used more than four times and were included in the analysis. Unlike the consumers, the experts seemed to use a common language to describe the wines. The first dimension was defined by cooked vegetables, dried vegetables, sour candy and caramel (positive values) and aftertaste,

3.2. Trained panellists Fig. 1b shows the product map and descriptors used to describe the samples by the trained panellists. The first and second dimensions of the MFA explained 61.4% of the variation, slightly more than the 4

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Fig. 1. Representations of the eight white wine samples (one duplicate sample) and the terms used to describe the samples for the first two dimensions of the multiple factor analysis of the data from the projective mapping task and ultra-flash profile task with (a) experienced panelists, (b) trained panelists, (c) consumers, and (d) experts.

tropical fruit, grassy and crisp on the negative side of the dimension. The second dimension was characterized by oak, watery and wood on the positive side, with astringent and sweet on the negative side. Overall, the results from the expert participants uniquely separated the wines, which is quite different from the experienced panellists, trained panellists and consumers.

Table 3 Comparison of the results from bi plots created from the projective mapping tasks (based on the first two dimensions).

3.5. Comparison of the different panellists RV coefficients provide a numerical value for the degree of similarity between the different groups of panellists’ (experienced, trained, consumers, and experts) bi plots (first two dimensions obtained from the projective mapping sessions). The RV is interpreted similarly to the Pearson correlation coefficient and can be applied to multivariate data (Josse, Pagès, & Husson, 2008). The RV coefficients comparing the results from the different groups of panellists are listed in Table 3. The RV coefficient between the experienced panellists and the trained panellists was 0.768 (p-value = 0.004), implying that the results are similar. The RV coefficients when comparing the experienced panellists to consumers and experts was 0.431 (p-value = 0.098) and 0.260 (p-

Panelist Comparison

RV coefficient

p-value

Experienced vs. Trained Experienced vs. Consumers Experienced vs. Experts Trained vs. Consumers Trained vs. Experts Consumers vs. Experts

0.768 0.431 0.260 0.275 0.156 0.308

0.004 0.098 0.434 0.338 0.786 0.261

value = 0.434), respectively, indicating there is not a significant correlation in the results. The experienced panellists did not evaluate the wines similar to consumers, and also, their results were different from the experts. Additionally, the trained panellists’ results were not correlated to the evaluations by the consumers or experts. Finally, the experts' evaluations were not correlated to the consumers. Overall, the experienced panellists' understanding of the PM and UFP method led to results that were similar to that of the trained panellists (trained for 5

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12 h) and led to them evaluating them differently than consumers. The experts evaluated the wine in a significantly different method than all other groups of participants.

found in projective mapping trials when the task is completed by consumers or untrained panellists, and trained panellists (Barcenas, Elortondo, & Albisu, 2004) and studies have stated that there is not a superiority of trained panellists over consumers (Ares & Varela, 2017). However, this was not the case in this study. These results may be due to wine being a complex product (Sáenz-Navajas et al., 2016) and the consumers may have difficulty describing what they perceive in the samples. Trained panellists can describe the perception of sensory attributes and can usually identify differences between samples (Chollet, Lelièvre, Abdi, & Valentin, 2011). Additionally, the differences between the trained panellists' results and that of the experts are well documented as Zamora and Guirao (2004), concluded that trained panellists could reach a higher level of consensus. Conversely, experts are very sensitive to many characteristics of a product, in this case wine, and are able make rapid judgements about the samples (Ares & Varela, 2017). Experts had also been found in past studies to evaluate wine differently than consumers (Honore-Chedozeau, Lelievre-Desmas, Ballester, Chollet, & Valentin, 2017, Parr, Heatherbell, & White, 2002; Torri et al., 2013). There is also usually a mismatch between consumers' hedonic scores and experts' quality ratings (Ares & Varela, 2017). Experts are able to sort wines based on their grape variety (Ballester, Patris, Symoneaux, & Valentin, 2008; Pagès, 2005; Parr, Green, White, & Sherlock, 2007), however this was not found in the study and as stated above W2, W4 and W6 were placed together, and they are made from three different grapes, L’Acadie, Seyval Blanc and Riesling, respectively. These results may be due to the experts having no information about the wines or about the aim of the tasting (they were instructed to describe the wines). Honoré-Chedozeau, Chollet, Lelièvre-Desmas, Ballester, & Valentin, 2020 found that when experts do not have information about the wines or are not identifying faults in the wine, they use an analytical approach to evaluate the wine and experts in their study indicated they tried to guess the purpose of this study. This may have affected this study as well, as experts, as well as all other panellists, were only asked to assess their overall perception of the wines. Overall, the experienced panellists similarly evaluated the wines to the trained panellists, and if wineries (or food companies) have a pool of experienced panellists (familiar with the method), they may be able to save money during product development. The use of experienced panellists may allow for quick evaluation of new products. Additionally, panel training is expensive, and time-intensive (Ares et al., 2013) and the use of experienced panellists by food companies may save money and time. Building on the past study (Morin et al., 2018), experienced panellists were able to evaluate a more complex product, wine, compared to the previous study which investigated cookies. However, there are limitations to this study. Firstly, this study did not compare the descriptors used by the different groups of panellists as 30 words were provided to all participants to be used during the PM and UFP task. Participants were allowed to and were instructed that they can add their own descriptors but when analyzing the results, the majority of the descriptors used (4 or more times) were the attributes that were provided. The experienced panellists used 27 of the 30 attributes, as did the trained panellists. The consumers used 28 of the 30 attributes and the experts used all of them. Additionally, all groups of panellists added their own descriptors (experienced = 14, trained = 9, consumers = 13, experts = 17). Future trials should ask the participants to provide their own descriptors and investigate if they differ based on the participant's experience with food products or training with the sensory method. Additionally, it has been recommended to replicate sessions when using the PM and UFP task, as this study only used one session per group of participants, future studies should investigate the reproducibility of experienced panellists. Lastly, a small number of samples (n = 8) were presented in this study; for the use of experienced panellists and PM/UFP task to be more useful to food product developers it will have to be investigated if experienced panellists can evaluate more samples during a single sensory task. Overall, more studies need to be completed to investigate the use of

4. Discussion The present study aimed to compare the results of a projective mapping and ultra-flash profile task on single varietal white wines completed by experienced panellists to trained panellists, consumers, and individuals employed in the wine industry (experts). Experienced panellists in this study were individuals who had experience with projective mapping and ultra-flash profile but did not have extensive knowledge of wine. When comparing the results of the sensory task completed by the different groups of panellists, it is clear that the experience with the sensory method has a drastic effect on the results. On the first two dimensions of the MFA, the trained panellists explained the highest amount of variation (61.4%), followed by the experienced panellists (56.0%) and experts (43.1%). The results from the consumers (had no experience with the sensory method) explained the least amount of variation 38.9%. The results of the experienced panellists were similar to that of the trained panellists (RV = 0.768); however, it was not similar to the consumers (RV = 0.431) or experts (RV = 0.260). These results are in agreement with other studies that have found that familiarization steps improve panellists' performance during a sensory task (Hopfer & Heymann, 2013; Jaeger et al., 2017; Liu et al., 2016). Training or familiarization steps can be categorized into two categories, those that inform participants about the method or those that describe the products to be tested (Liu et al., 2016; Perrin et al., 2008). Liu et al. (2016) found that both method and product training had a positive effect on the discriminability, with product testing having a more significant effect. However, in this study, the experienced panellists did not know about wine but rather had extensive knowledge about the PM and UFP task and the results agree with those of Hopfer and Heymann (2013), as they found that a training step increased the reliability of the results. The participants in the study by Hopfer and Heymann (2013) were trained using paper shapes differing in colour. In this study, participants in all sessions were given a demonstration of the method using sandwich cookies, but they were not formally trained. Instead, it was assumed that the experienced panellists were very familiar with the method, and this familiarity would allow them to be more discriminative when evaluating the products. The results agree with the findings of Ishii, Kawaguchi, O’Mahony, and Rousseau (2007) that participants’ performance is related to their familiarity with the experimental procedure. Additionally, the results of the study agree with a past study on cookies and experienced panellists (Morin et al., 2018). The researchers found that the experienced panellists evaluated the cookies in the study similar to the trained panellists (Morin et al., 2018), as was also found in this study. Furthermore, the results indicate that experienced panellists are able to evaluate complex products (wine) similar to trained panellists. Other studies (Liu et al., 2016; Moussaoui & Varela, 2010; Nestrud & Lawless, 2008; Veinand, Godefroy, Adam, & Delarue, 2011) have evaluated the reliability of rapid methodologies by comparing the results of the blind repeated sample on the product map. These authors reported that if blinded samples were located close to each other on the product map, the PM and UFP task is reliable. In this study, the experienced panellists (Fig. 1a), trained panellists (Fig. 1b) and experts (Fig. 1d) placed the blinded duplicated samples (W5.1 and W5.2) very close together on the product map, while the consumers (Fig. 1c) had the samples placed further apart. However, as a caveat, when participants are evaluating very similar samples, the blinded duplicated samples could be placed further apart (Ares & Varela, 2014). When comparing the results from the trained panellists to other groups of participants (consumers and experts), results clearly showed that the panellists evaluated the wine in different manners (RV = 0.275 and 0.156, respectively). Good agreement in the results can usually be 6

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experienced panellists and if the results they produce are reliable and reproducible.

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5. Conclusion The experienced panellists' familiarity with the PM and UFP task lead to results that were similar to that of the trained panellists. The experienced panellists' results differed from that of the consumers and the experts. The use of experienced panellists could lead to cost-saving and quick, reliable, results during the production of new wines and other food products. More studies need to be completed to examine the use of experienced panellists further and to overcome the limitations of this study (a small number of samples, panellists were given a list of descriptors). The use of experienced panellists should not be considered as a replacement of trained panellists, but rather another method in the toolbox that can help with food development as it requires fewer resources, time and samples. The experts evaluated the wine samples differently than the trained panellists and consumers. Winemakers and others working in the wine industry should be mindful that experts' evaluations of wines do not also correlate to that of consumers and ensure they market their wine with descriptors that apply to consumers. In conclusion, the participants with a high level of familiarity or experience with a sensory method had a drastic effect on the results of the sensory task and can allow the participants to be more discriminative when completing a sensory task. CRediT authorship contribution statement Alanah Barton: Conceptualization, Data curation, Formal analysis, Investigation. Lydia Hayward: Conceptualization, Data curation, Formal analysis, Investigation. Connor D. Richardson: Formal analysis, Visualization. Matthew B. McSweeney: Conceptualization, Resources, Methodology, Software, Data curation, Formal analysis, Investigation, Visualization, Supervision, Funding acquisition, Project administration. Acknowledgments The Nova Scotia Department of Agriculture funded this research. Thank you to all the volunteers who participated in this project. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.foodqual.2020.103900. References Abdi, H., Valentin, D., Chollet, S., & Chrea, C. (2007). Analyzing assessors and products in sorting tasks: DISTATIS, theory and applications. Food Quality and Preference, 18(4), 627–640. https://doi.org/10.1016/j.foodqual.2006.09.003. Abdi, H., Williams, L. J., & Valentin, D. (2013). Multiple factor analysis: Principal component analysis for multitable and multiblock data sets. Wiley Interdisciplinary Reviews: Computational Statistics, 5(2), 149–179. https://doi.org/10.1002/wics.1246. Ares, G., de Saldamando, L., Vidal, L., Antúnez, L., Giménez, A., & Varela, P. (2013). Polarized projective mapping: Comparison with polarized sensory positioning approaches. Food Quality and Preference, 28(2), 510–518. https://doi.org/10.1016/j. foodqual.2013.01.003. Ares, G., & Varela, P. (2014). Comparison of novel methodologies for sensory characterization. In G. Ares, & P. Varela (Eds.). Novel techniques in sensory characterization and consumer profiling. Boca Raton: CRC Press. Ares, G., & Varela, P. (2017). Trained vs. consumer panels for analytical testing: Fueling a long lasting debate in the field. Food Quality and Preference, 61, 79–86. https://doi. org/10.1016/j.foodqual.2016.10.006. Ares, G., Varela, P., Rado, G., & Giménez, A. (2011). Are consumer profiling techniques equivalent for some product categories? The case of orange-flavoured powdered drinks. International Journal of Food Science & Technology, 46(8), 1600–1608. https:// doi.org/10.1111/j.1365-2621.2011.02657.x. Ballester, J., Patris, B., Symoneaux, R., & Valentin, D. (2008). Conceptual vs. Perceptual wine spaces: Does expertise matter? Food Quality and Preference, 19(3), 267–276. https://doi.org/10.1016/j.foodqual.2007.08.001.

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