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Proposing a ranking descriptive sensory method
Food Quality and Preference 21 (2010) 611–620
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Food Quality and Preference journal homepage: www.elsevier.com/locate/foodqual
Proposing a ranking descriptive sensory method Vanessa Bragato Richter a, Tereza Cristina Avancini de Almeida b, Sandra Helena Prudencio c, Marta de Toledo Benassi c,* a
Departamento de Alimentação Escolar, Prefeitura de Guaira, Av. Cel. Otávio Costa, 126, 85980-000 Guairá, PR, Brazil Secretaria Municipal de Saúde, Prefeitura de Campinas, Avenida Anchieta, 200, 13015-904 Campinas, SP, Brazil c Depto. de Ciência e Tecnologia de Alimentos, Universidade Estadual de Londrina, CP 6001, 86051-970 Londrina, PR, Brazil b
a r t i c l e
i n f o
Article history: Received 3 September 2009 Received in revised form 18 March 2010 Accepted 18 March 2010 Available online 27 March 2010 Keywords: Ranking Descriptive analysis Free-Choice Profiling Quantitative Descriptive Analysis Generalized Procrustes Analysis Pudding
a b s t r a c t The objective of this work was to propose an alternative use to ranking method, as descriptive test, here named Ranking Descriptive Analysis (RDA). RDA was compared with Free-Choice Profiling (FCP) and Quantitative Descriptive Analysis (QDA). Four chocolate puddings were used as samples. A group of assessors performed FCP. Another group of selected assessors developed a list of attributes and their corresponding definitions. These assessors were divided into two groups: a panel was trained to perform the QDA and other panel was familiarised with the RDA procedures. Sample discrimination was similar using the three techniques. The RDA panel showed better consensus than the other two ones. The QDA showed the best correlation with the instrumental analysis of color and texture. Despite the larger number of assessors, RDA has the advantage of minor costs associated with the requirement of fewer sessions and a smaller amount of product than required by other techniques. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction The objective of descriptive methods is to characterise the sensory properties of a product, using a panel that evaluates the samples qualitatively and quantitatively (Murray, Delahunty, & Baxter, 2001). A well-known descriptive method is Quantitative Descriptive Analysis (QDA), which allows the characteristics of the product to be quantified, thus enabling a statistical treatment of the data. The QDA is appropriate when an evaluation requires detailed information on the sensory profile, identification, and quantification of the attributes. It allows the comparison of similar products, correlations with instrumental measures, and can be used to define the standard for quality control (Meilgaard, Civille, & Carr, 1999). Qualified assessors who have undergone long and expensive training are used to provide reliable and consistent results (Stone & Sidel, 1998). An alternative descriptive method, the Free-Choice Profiling (FCP), offers assessors the freedom to use descriptive terms in the amount and the way that they desire, reducing analysis time because training is not necessary. The results are evaluated by Generalized Procrustes Analysis (GPA), which fit each assessor con-
* Corresponding author. Tel.: +55 43 33714987; fax: +55 43 33714565. E-mail addresses: [email protected] (V.B. Richter), tereza_al [email protected] (T.C.A. de Almeida), [email protected] (S.H. Prudencio), martatb@ uel.br (M. de Toledo Benassi). 0950-3293/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodqual.2010.03.011
figuration to a consensus configuration, transforming the results to prevent variations in the use of the scale or the interpretation of the attributes (Dijksterhuis & Gower, 1991; Gower, 1975; Williams & Langron, 1984). The GPA can be used for other descriptive analyses to verify efficiency, repeatability, and consensus of the group (Rodrigue, Guillet, Fortin, & Martin, 2000). The ranking test is traditionally used as a discriminative test to compare many samples that are presented simultaneously (Meilgaard et al., 1999). Because of the simplicity of the procedure, the ranking technique has been used in consumer studies (BarylkoPikielna et al., 2004; Hein, Jaeger, Carr, & Delahunty, 2008; Lee, Hout, & O’Mahony, 2007; Lee & O’Mahony, 2005; Liem, Mars, & Graaf, 2004; Villanueva, Petenate, & Da Silva, 2005) and in the evaluation of the efficiency of panels with different levels of training (Kim & O’Mahony, 1998; McEwan, 1999). Despite the inherent differences between the techniques, their one common objective is to determine differences between the products’ attributes. The QDA and the FCP use a scale to measure the differences in intensity for each attribute. On the other hand, the ranking test presents ordinal results of intensity of the attributes; however, the magnitude of the differences is not obtained. One of the primary difficulties in traditional descriptive methods is the use of the scale itself and the consistency/repeatability of scores attributed to the sample. Observing the assessors’ behaviour during QDA training sessions, Kim and Mahoney (1998) verified that assessors often first organize the samples by intensity order of an attribute and then distribute samples in the scale.
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Ranking seems to be a facilitating procedure. There are situations in which the interest is not in the value on the scale, but whether there are significant differences among products and if so, in which attributes and in which ‘‘ranking order”. Dairou and Sieffermann (2002) proposed a variant of FCP, named Flash Profile (FP), and compared its efficiency with a conventional profile method. FP was a combination of free-choice terms selection with a ranking method based on simultaneous presentation of the products set. Delarue and Sieffermann (2004) compared FP and a conventional descriptive method in the evaluation of flavor of two kinds of fruit dairy products: yogurts and chesses. Similar profiles were obtained by the two methods and the main benefits of FP were its rapidity and easy of use. However, the authors pointed that the semantic consensus obtained in the conventional profile allowed a more accurate description of the products. Rodrigue et al. (2000) also suggested that a ranking test could be used in the description of a product. After four training sessions, a trained panel of eight assessors evaluated 10 attributes of sweet corn using the conventional method. An untrained panel of 20 assessors performed the same task on the same samples using a ranking procedure, but only one session was conducted to familiarise assessors with samples and ranking procedures. The results from both methods were similar in terms of overall product discrimination, but slight discrepancies were found in the discriminating attributes between panels. The findings suggest that when time is insufficient to train a panel, the use of an untrained panel and ranking test should be considered. Although Rodrigue et al. (2000) and Delarue and Sieffermann (2004) have described a good performance by a ranking panel, they also considered that other studies would be necessary to verify whether ranking tests could be used fruitfully or adapted to a wider range of products and a greater number of attributes. Another consideration is that in the study described by Rodrigue et al. (2000), the attributes were pre-defined and not developed by the panels, although they were validated by the panel. For untrained and completely naive assessors, grasping the relation between attribute and perception in one session could be a difficult task. A standardisation of samples’ characteristics, assessors’ sensibility, and the development of attributes by the panels would allow a better comparison of the efficiency of the methods. Chocolate pudding was chosen as sample in the current work because it is easy to prepare, it allows definition and control of differences in appearance, aroma, flavor, and texture attributes and it has been frequently used in descriptive methods studies in the literature (Kilcast & Clegg, 2002; Lethuaut et al., 2005; Weenen, Jellema, & Wijk, 2005; Wijk, Gemert, Terpstra, & Wilkinson, 2003; Wijk, Prinz, & Janssen, 2006). The objective of this study was to propose a descriptive sensory method of simple and fast application for situations in which the magnitude of attributes or the distance between categories is less relevant. The method, here called Ranking Descriptive Analysis (RDA), was compared to two traditional descriptive methods, Quantitative Descriptive Analysis and the Free-Choice Profiling, using chocolate puddings with sugar and different sweeteners as the sample.
2. Materials and methods 2.1. Samples Four formulations of chocolate pudding were used. Commercial powder cocoa, thickener carragena gum, sugar, and three sweeteners in different amounts were added to a basic mixture of commercial powder skimmed milk (100 g) and commercial starch of maize
(50 g) (Table 1). The formulations, based on Iop, Beleia, and Silva (1999), were developed to present differences in the characteristics of color, texture, aroma, and flavor. In preliminary tests was also considered that the formulations should reflect commercial products that were studied in previous researches (Oliveira & Benassi, 2010; Oliveira, Frasson, Almeida, & Benassi, 2004). The puddings were prepared with water (1 L) and cooked under constant agitation until boiling (12 min). Then, they were removed from heat, agitated for 2 min, and conditioned in proper containers according to the analysis. The samples were covered, to prevent drying, and stored under refrigeration (7 ± 2 °C) for approximately 20 h before analysis. 2.2. Instrumental evaluation of color and texture For texture evaluation, the puddings were conditioned in smooth plastic cups of 50 mL, 4.8 cm in diameter and 4 cm in height. The samples were analysed (15 repetitions) using a texturometer TAX-T2 (Stable Micro Systems) (Surrey, England), using cylindrical stainless probe P35, distance of penetration of 3 mm and force of 0.05 N. Hardness (N), cohesiveness (dimensionless), springiness (dimensionless), adhesiveness (Ns), and gumminess (N) were evaluated. For color analysis, samples were conditioned in Petri plates of 9 cm in diameter (three plates for sample). The readings were made (three repetitions for plate) using colorimeter Minolta CR10 (Tokyo, Japan), with a reading area of 8 mm, lighting CIE D65, and observer standard CIE 10° angle. The colorimeter directly supplied the values of L* (lightness), a* (red–green component), and b* (yellow–blue component) and the hue (H* = arc tang (b*/a*)) was calculated. A randomised design was applied and the results were submitted to the analysis of variance (ANOVA) and Tukey test (p < 0.05) (STATSOFT STATISTICA for Windows – Program manual, 1995). 2.3. Sensory analyses 2.3.1. Testing procedure and glossary development Samples (40 mL) were served cold (taken immediately from the refrigerator) in covered 100 mL transparent plastic cups, labelled with random-three digit codes. White light was used and the presentation order was randomised by session. Attribute terms for the evaluation of samples in the three methods were developed by the panel using the methodology described in Moskowitz (1983). Assessors were requested to record the similarities and differences between each sample. Two sessions were conducted. In each session, a pair of samples was presented by session, in order to obtain the highest possible number of attributes with regard to appearance, flavor, texture, and aroma. One pair was composed of samples B and C and the other
Table 1 Other ingredients of pudding formulations (in gram). Ingredients
Commercial powder cocoa (10–12% of fat) Carragena GENULACTA type LP-60 (CPKelco, USA) Sugar (commercial sucrose) Commercial sweetener with maltodextrine, sucralose and acesulfame potassium Commercial sweetener with maltodextrine, sodium saccharin, sodium cyclamate and sodium citrate Commercial sweetener with maltodextrine, stevia and silicium dioxide
Samples A (g)
B (g)
C (g)
D (g)
16 0.10 120
16 0.10
20 0.20
12 0.10
20 20 6
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pair, samples A and D. A detailed description of the glossary development for each method is presented on the next sections (2.3.2 and 2.3.3). In the three studied methods, during evaluation sessions, assessors received the developed glossary with the correspondent score sheet. For each panel, a protocol with instructions was offered reminding how to observe appearance, odour, flavor, texture, and mouthfeel after swallowing. For texture, it was emphasized that the assessor would need to cut the sample with the spoon, to compress a portion of the sample between the tongue and the palate, and ‘‘to chew” the sample until ready to swallow. The protocol was fixed in the booth during all of the evaluation sessions. For the FCP, the protocol was only used to facilitate the evaluation. For QDA and RDA, the protocol was the group consensus’ result. 2.3.2. Free-Choice Profile Fourteen assessors participated in the FCP. After the development of the attributes, individual score sheets and a specific list of each assessor’s definition of attributes were elaborated. The attributes varied in number, from 7 to 13, with an average of 10 per assessor. Attributes were evaluated using an unstructured 9 cm scale anchored in the endpoints with intensity terms. To verify the score sheets’ adequacy, the presentation for the descriptive test was simulated in two sessions. Three samples were evaluated in each session: A, B and D in the first session; B, C and A in the second session. The assessors could modify their score sheets, removing or including attributes, changing terms in the endpoints of the scales, and improving definitions. For evaluation, a balanced incomplete blocks design for four samples was used, with t = 4, k = 3, r = 3, b = 4, k = 2, E = 89. The results were analysed by Generalized Procrustes Analysis (GPA), using the Senstools program Version 2.3.28 (OP, 1998). In a preliminary analysis of the results, to verify panel’s performance, the general configuration of the assessors, the residual variances, and the individual configuration of the samples by each assessor were observed. Two assessors that presented low discrimination capacity, poor repeatability and lack of consensus with the group (residual variance superior to 1.5%, and individual configuration of samples different from the consensus plot and higher distances between samples repetition), had their results removed and the data analysis was repeated. The criterion adopted for consensual description of samples was to consider the attributes with highest correlation (minimum |0.5|) in each dimension for each assessor. 2.3.3. Quantitative Descriptive Analysis and Ranking Descriptive Analysis 2.3.3.1. Pre-selection. Initially 47 assessors have participated in a series of preliminary tests to evaluate their performance for aroma and basic tastes recognition, discrimination of color and hardness intensity. For aroma recognition, the minimum criterion was 70% correct answers. Approved assessors were then submitted to a test of recognition of basic tastes, while assessors who did not identify at least one of the standard solutions to each basic taste were removed from the panel (Penna, 1980). To evaluate the discrimination capacity for hardness and color intensities, ranking tests were applied, as in Meilgaard et al. (1999). The criterion adopted was 100% correct answers. For hardness, four samples of puddings were used, formulated as described for the sample A (Table 1) but with different starch concentrations: 40, 50, 70, and 90 g. The samples were evaluated by Instrumental Texture Profile and presented the following values of hardness: 0.69, 1.32, 4.2, and 6.7 N, similar to observed in commercial products (Oliveira & Benassi, 2010). For color, the samples were stand-
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ardised with skimmed milk and brown food colourants. After the tests, the group was composed of 33 selected assessors. 2.3.3.2. Definition of the descriptive terminology. After attributes development, assessors selected the most frequent terms to compose the score sheet. In a second session, assessors defined attributes and developed a consensus evaluation procedure using the suggested samples and market products as qualitative references (Table 2). A final session was conducted to verify the consensus on the list of attributes, glossary, and protocol of instructions. After this phase, the assessors were randomly divided into two panels, one panel with 21 people for the RDA and the other with 12 people for the QDA. Training and samples evaluation of each panel were conducted separately. 2.3.3.3. Quantitative Descriptive Analysis (QDA). Three sessions were necessary for assessors to reach consensus on reference samples that represented the extremities of the scale for each attribute and form elaboration. After this stage, four sessions were conducted for training in the use of the scales (Table 2). The score sheet was composed of unstructured 9 cm line scales anchored in the end points with intensity terms. At the final selection of assessors, four samples were used. To avoid an extreme familiarisation with samples that would be later evaluated, only sample C was used. The other two products were formulated in a manner similar to sample A, but with different starch concentrations (40 and 70 g) (Section 2.3.3.1). A commercial pudding characterised with regard to color (L* = 31 and H* = 38) and hardness (0.43 N) was also used. The presentation order of the four samples followed the statistical design of the FCP. To evaluate the discrimination and repeatability abilities of the assessors, a two factor (samples and repetitions) ANOVA and an F test for each attribute were conducted. To select assessors by discrimination ability, they had to present values of Fsamples with a maximum level of significance of 50% (p < 0.50). For repeatability, assessors with values of Frepetitions with a minimum level of significance of 5% were selected (p < 0.05). For evaluation of the agreement of the assessor with the panel, the average individual scores were compared to panel scores. Samples were evaluated in four sessions using the design of the FCP. Results were analysed using analysis of variance (ANOVA) for two sources of variation (samples and assessors) and an interaction of sample assessor, as well as a multiple comparison Tukey test (p < 0.05). In cases of significant Finteraction sample assessor, the main effects (samples) were tested against interaction mean square data. The results were also analysed by GPA, as described for the FCP. 2.3.3.4. Ranking Descriptive Analysis (RDA). One training session was conducted to demonstrate the score sheet used in the RDA. Two posterior sessions were used to define a more specific protocol for RDA. Samples were served at a table and the assessors were requested to evaluate all of the attributes so that the panel arrived at a consensus on the procedure and amount of sample to be served. Due to the number of attributes, the panel opted to evaluate the samples in two phases sequentially: one for analysis of appearance and aroma attributes and another for texture and flavor attributes. To familiarise assessors with the score sheet and attributes and to verify the efficiency of the protocol, a test with four samples was conducted. The same products used in the final selection of the assessors in QDA were used. The assessors had considered the procedure to be adequate (score sheet, attributes, and protocol) and the observed results showed that the panel was able to rank the products. The evaluation of the samples by RDA was completed in one session in which, for each attribute, samples were ordered by
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Table 2 Attributes, definitions and references used by QDAa,b and RDAb panels to describe the sensory properties of chocolate puddings. Attributea
Homogeneousness
Smooth, appearance with absence of lumps or bubbles Intensity of light reflection in the product, brightness intensity, opposite of opaque
Semi-sweet dark chocolate bar (Garoto ); milk chocolate (GarotoÒ) Creamy yogurt and consistent yogurt Sample A cover with oil; chocolate cream desert (DanetteÒ) and sample A with different starch content (20 and 35 g) Semi-sweet dark chocolate bar (GarotoÒ), milk chocolate (GarotoÒ)
Chocolate aroma
Chocolate aroma intensity
Chocolate flavor
Chocolate flavor intensity, characteristic of chocolate
Semi-sweet dark chocolate bar (GarotoÒ), milk chocolate (GarotoÒ)
Sweet taste
Sweet taste intensity, sugar taste
Water solutions (1 L) with different content of sugar: 200, 100, 50 and 10 g
Residual bitter taste
Residual bitter taste intensity; persistence of bitter taste in the mouth after swallowing the sample; off-taste of sweetener
Water solutions (1 L) with different sweeteners: sugar (10 g), sucralose and acesufame-k (10 g), saccharin and cyclamate (10 g) and stevia (10 g)
Consistency
Force required to obtain a given deformation, observed in the mouth or cutting with spoon Capacity to dissolve homogeneously in the mouth
Sample A with different starch content: 20, 25, 30 and 35 g Chocolate cream desert (DanetteÒ)
3. Results 3.1. Instrumental characterisation of texture and color Pudding samples showed different texture and color profiles (Table 3). Sample D was characterised as more firm and gummy than puddings B and C and it was also identified as more clear and yellowish than the others. Pudding C was differentiated as the darkest sample, less hard and gummy, and more elastic than samples A and B. Sample B was characterised as the least adhesive and presented intermediate values of hardness, lightness, and hue. Sample A was the most reddish sample.
Table 3 Instrumental characterisation of textureA and color.B,C Attributes
Samples A
Hardness (N) Adhesiveness (Ns) Springiness Cohesiveness Gummines (N) Lightness Hue B C
Light: milk chocolate (GarotoÒ) Dark: semi-sweet dark chocolate bar (GarotoÒ) Low: creamy yogurt Much: consistent yogurt Low: sample A Much: chocolate cream desert (DanetteÒ) Weak: milk chocolate (GarotoÒ) Intense: semi-sweet dark chocolate bar (GarotoÒ) Weak: milk chocolate (GarotoÒ) Intense: semi-sweet dark chocolate bar (GarotoÒ) Weak: milk dispersion (0.5 L) with unsweetened cocoa powder (15 g) and sugar (30 g) Intense: milk dispersion (0.5 L) with unsweetened cocoa powder (15 g) and sugar (30 g) None: milk dispersion (0.5 L) with unsweetened cocoa powder (15 g) and sugar (50 g) Intense: milk dispersion (0.5 L) with unsweetened cocoa powder (15 g) and commercial sweetener with saccharin and cyclamate (15 g) Low: sample A with 40 g of starch Much: sample A with 70 g of starch Low: sample A with 40 g of starch Much: chocolate cream desert (DanetteÒ)
Applied for QDA and RDA. Applied for QDA.
increasing intensity. The results were evaluated using Friedman test (Newell & MacFarlane, 1987) to evaluate sample differences for each attribute. In order to compare the configuration obtained with those observed in the two other methods, data was also evaluated by GPA, as described for the FCP.
A
Quantitative referenceb Ò
Brown color intensity
Creaminess a
Qualitative referencea
Brown color
Brightness
b
Definitiona
B ab
1.7 ± 0.2 0.4 ± 0.1a 0.86 ± 0.05b 0.57 ± 0.02a 0.99 ± 0.12ab 36.5 ± 0.2b 46.4 ± 0.4c
C b
1.6 ± 0.2 0.2 ± 0.2b 0.86 ± 0.05b 0.57 ± 0.01a 0.93 ± 0.09b 36.5 ± 1.2b 47.9 ± 0.4b
D c
1.4 ± 0.1 0.4 ± 0.5a 0.89 ± 0.0a 0.57 ± 0.02a 0.81 ± 0.07c 35.2 ± 0.6c 47.9 ± 0.6b
1.8 ± 0.1a 0.4 ± 0.1a 0.90 ± 0.01a 0.57 ± 0.12a 1.04 ± 0.06a 38.5 ± 0.4a 49.5 ± 2.1a
Average of 15 analyses ± standard deviation. Average of nine analyses ± standard deviation. Different letters in the same line indicates significant differences (p < 0.05).
3.2. Sensory characterisation 3.2.1. Evaluation of assessor’s performance: comparison among panel’s behaviour Analysis of the general configuration and residual variance of assessors of each panel demonstrated that there was consensus in the three panels, without behaviour discrepancies among members (Figs. 1 and 2). The QDA panel (Figs. 1B and 2B), which received quantitative training, and the FCP panel (Figs. 1A and 2A), which did not receive training, showed a greater dispersion of the assessors in the general configuration and in the residual variance (maximum of 1.2% and 1.5%, respectively) than the RDA panel; which showed less dispersion and a low residual variance (0.5%) (Figs. 1C and 2C).
3.2.2. Samples evaluation Fig. 3A and B show configuration of samples consensus in the FCP and the QDA; the products are represented by triangles that indicate the repeatability (the higher the distance between the vertices, the less repeatability). Both panels presented good repeatability for all samples. The attributes better correlated with the first two dimensions for each assessor were represented for FCP (Table 4), QDA (Table 5) and RDA (Table 6) panels. To facilitate the evaluation and comparison between methods, there were considered the attributes that presented higher correlation (minimum |0.5|) in each dimension for each assessor. Considering the consensus configuration, 38% of the observed variability was explained for the FCP. Dimension 1, responsible for 25% of the variance, could be explained (in the negative direction) for the attributes brown color, sweet taste, and chocolate flavor. These terms had high correlation for most of the panel members (nine assessors). Pudding D, located at the right side,
V.B. Richter et al. / Food Quality and Preference 21 (2010) 611–620
Fig. 1. Graphic representation of the assessors dispersion along the two first dimensions: general configuration of assessors in FCP (A), QDA (B) and RDA (C) panels. Numbers (1–21) indicates assessors.
was considered the clearest sample, with less sweet taste and chocolate flavor. The samples configured at the left side, B, A, and C (in this sequence), were characterised as presenting more accented color and flavor of chocolate, and also more sweet taste. Dimension 2 (13% of the variance) separated samples primarily by bitter taste and residual bitter taste. Puddings C and D, located in the superior part of the graph, were characterised by higher intensity of bitter taste (Fig. 3A, Table 4). In the QDA method, the first two dimensions were responsible for 46% of the variance (32% for dimension 1 and 14% for dimension 2). Dimension 1 was explained, in the negative direction, by attri-
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Fig. 2. Residual variance (%) associate with each assessor in FCP (A), QDA (B) and RDA (C) panels.
butes of brown color (12 assessors), chocolate flavor (10), chocolate aroma (9), creaminess (8), and brightness (6), and, in the positive direction, by attribute consistency (5). Dimension 2 was correlated negatively to sweet taste and positively to residual bitter taste. Therefore, samples located at the upper side were characterised by a higher intensity of residual bitter taste and lower sweet taste. Samples configured at the left side were described as more characteristic of chocolate (brown color, aroma and flavor of chocolate) and those located towards the right side were characterised as more consistent (Fig. 3B, Table 5).
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samples C and D, but sample B presented a less intense chocolate flavor as compared to sample A (Tables 5 and 7, Fig. 3B). In RDA method, the first two dimensions were responsible for 75% of the variance. Dimension 1 (51% of the variance) could be explained (in the negative direction) for the attributes brown color, chocolate flavor, sweet taste, and chocolate aroma. Dimension 2 (24% of the variance) separated the samples, primarily for the attribute bitter taste. The texture attributes correlated with two dimensions, positive for creaminess and negative for consistency, in dimension 1, with an inverse relationship in dimension 2 (Fig. 3C, Tables 6 and 8). Table 8 shows the results of the RDA panel by the attributes analysed. Pudding D was characterised as presenting the lightest brown color and with lowest sweet taste, while pudding C had the most intense chocolate aroma and flavor. These samples presented a residual bitter taste that was more intense than puddings A and B. Samples A and B were significantly different in relation to texture attributes, sample B was characterised as creamier and less consistent as compared to sample A. Sample B and D presented similar characteristics of texture.
3.3. Comparison of the applicability of the sensory methods A comparison of the number of required assessors, number of samples each assessor had to taste and time to run the tests for each stage of the methods is detailed in Table 9.
4. Discussion
Fig. 3. Two-dimensional consensus plot for chocolate puddings (A, B, C and D) obtained by FCP (A), QDA (B) and RDA (C) methods. Triangle vertices represent one of the three replicates analyses for each sample.
The mean values of the attributes’ intensity analysed by the QDA panel are represented in Table 7. The analysis of the results by ANOVA demonstrated that the formulations differed significantly with relation to the attributes of appearance, aroma, flavor, and texture. Sample D was characterised as being the most clear, less creamy, more consistent, with highest residual bitter taste and less sweet taste than the other samples. Pudding C presented brown color, high aroma, and chocolate flavor and was the second in intensity of residual bitter taste, when compared with the other samples. Samples A and the B were not differentiated in intensity of brown color and presented less residual bitter taste than
Comparing the panel’s performance on each method, it was observed consensus among assessors in the three panels (Figs. 1 and 2). The greater consensus observed in the RDA panel was likely due to the facility of the use of ordinal scales as compared with the use of interval scales in the traditional descriptive techniques. FCP and RDA panel’s presented good repeatability. Like others ranking tests, RDA does not allow checking panel repeatability because just one measure is taken for each sample (Fig. 3). The comparison of the samples configuration of consensus obtained by the three methods demonstrated that a higher amount of variance (75%) was observed for the RDA compared with FCP (38%) and ADQ (46%). This behaviour was already expected since the results of RDA consisted in less complex data than the obtained for FCP and QDA, allowing to show the main information in two dimensions. Comparing the samples distribution in RDA with FCP and QDA (Fig. 3), similarities were observed in configuration (primarily in dimension 1) and in the attributes more important to discrimination (Tables 4–6). Delarue and Sieffermann (2004) also obtained similar results comparing the profiles obtained by conventional descriptive method and a ranking procedure (FP). The largest difference in the samples distribution was observed in dimension 2, in which samples A and B appeared more discriminated. The attributes chocolate flavor and brown color in dimension 1 and residual bitter taste in dimension 2 presented high correlation in the three sensory methods. Both QDA and RDA assessors used aroma and texture descriptor more consensually than the FCP panel (Fig. C, Tables 4–6). In general, consensus among the methods was observed. All of the results were coherent for the discrimination of the products and similar for the three panels. The same attributes were important for the characterisation in the three sensory methods and all samples were discriminated. It should be noted that the use of GPA allowed the comparison of the performance of the three panels.
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Table 4 Attributes better correlated (r) with the first two dimensions for each assessora on FCP. Aa
Dimension 1
Dimension 2
1 2 3
Brown color ( 0.78); sweet taste ( 0.95); chocolate flavor ( 0.78) and brightness ( 0.61) Brown color ( 0.85); sweet taste ( 0.92); chocolate flavor ( 0.70) and brightness ( 0.69) Brown color ( 0.65) and sweet taste ( 0.50)
4 6
Chocolate color ( 0.57); sweet taste ( 0.57); chocolate flavor ( 0.50); consistency (appearance) ( 0.56) and firmness (cut) (0.74) Sweet taste ( 0.82); chocolate flavor ( 0.81) and bubbles ( 0.63)
Bitter taste (0.72); crust thickness (0.50) and crust firmness (0.51) Bitter taste (0.77) and chocolate aroma ( 0.54) Bitter taste (0.52); residual bitter taste (0.50); chocolate aroma (0.50) and syneresis (0.57) Bitter taste (0.83); sour taste (0.86) and consistency ( 0.53)
7
Brown color ( 0.76); sweet taste ( 0.50) and chocolate flavor ( 0.64)
8
Brown color ( 0.59) and sweet taste ( 0.88)
9 10 11
Sweet taste ( 0.74); chocolate flavor ( 0.72) and consistency (cut) (0.69) Chocolate flavor ( 0.73); chocolate aroma (0.59) and homogeneous surface ( 0.69) Chocolate color ( 0.87); sweet taste ( 0.83); chocolate flavor ( 0.77); chocolate aroma ( 0.79) and homogeneousness ( 0.91) Brown color (0.64); chocolate flavor ( 0.79); chocolate aroma ( 0.60) and consistency (cut) (0.76) Brown color (0.60) and smooth surface (0.57)
13 14 a
Bitter taste (0.69); residual bitter taste (0.71) and chocolate aroma (0.51) Bitter taste (0.59); chocolate aroma (0.52) and creamy texture ( 0.61) Smooth surface (0.57); consistency (cut) (0.53) and homogeneousness (0.53) Surface crust (0.59) Sweet taste ( 0.69) Consistency (mouth) ( 0.50) Sweet taste ( 0.65) Bitter taste (0.69); sweet taste ( 0.71); chocolate flavor ( 0.58) and viscosity (0.51)
Assessors were identified by numbers 1–14.
Table 5 Attributes better correlated (r) with the first two dimensions for each assessora on QDA. A
Dimension 1
Dimension 2
1
Brown color ( 0.88); chocolate aroma ( 0.78); chocolate flavor ( 0.67) and residual bitter taste (0.67) Brown color ( 0.76); chocolate aroma ( 0.61) and chocolate flavor ( 0.71)) Brown color ( 0.73); chocolate aroma ( 0.75); creaminess ( 0.87); consistency (0.77) and residual bitter taste (0.84) Brown color ( 0.76); brightness ( 0.80); chocolate aroma ( 0.52); creaminess ( 0.64); consistency (0.77) and chocolate flavor ( 0.50) Brown color ( 0.87); brightness ( 0.51); chocolate aroma ( 0.69); creaminess ( 0.70); consistency (0.51); chocolate flavor ( 0.50) and residual bitter taste ( 0.68) Brown color ( 0.85); brightness ( 0.82); homogeneousness ( 0.74); chocolate aroma ( 0.64) and chocolate flavor ( 0.82) Brown color ( 0.83); brightness ( 0.58); chocolate aroma ( 0.69); creaminess ( 0.85); consistency (0.64); chocolate flavor ( 0.68); sweet taste ( 0.83) and residual bitter taste (0.67) Brown color ( 0.71); brightness ( 0.75); chocolate aroma ( 0.51); creaminess ( 0.60) and chocolate flavor ( 0.71) Brown color ( 0.72) and brightness ( 0.61) Brown color ( 0.78); creaminess ( 0.79); consistency (0.77) and chocolate flavor ( 0.72) Brown color ( 0.81); creaminess ( 0.67); chocolate flavor ( 0.88) and sweet taste ( 0.79) Brown color ( 0.56); chocolate aroma ( 0.57) and creaminess ( 0.82)
Creaminess ( 0.63); consistency (0.65) and sweet taste ( 0.55)
2 3 4 5 6
7
8 9 10 11 12 a
Homogeneousness (0.51) and sweet taste ( 0.73) Chocolate flavor (0.73) and sweet taste ( 0.64) Sweet taste ( 0.74) and residual bitter taste (0.87) Sweet taste ( 0.51) Sweet taste ( 0.52); consistency ( 0.51) and residual bitter taste ( 0.50)
Sweet taste ( 0.67) and residual bitter taste (0.51) Residual bitter taste (0.53) Sweet taste ( 0.65) and residual bitter taste (0.94) Residual bitter taste (0.64) Sweet taste ( 0.81) and residual bitter taste (0.61)
Assessors were identified by numbers 1–12.
These results agreed with those described by Rodrigue et al. (2000) in a study with two panels: one trained for descriptive analysis and another not trained for the ranking test. Results from the study showed similarities between the trained and untrained panels. These authors suggest that when time is insufficient to train a panel, the use of an untrained panel and a ranking test should be considered. However, we observed it was important to train a panel in order to obtain good descriptor conceptualisation and greater panel consensus. Consensus across the characterisations of the samples obtained the descriptive analysis methods (Fig. 3, Tables 4–6) and the instrumental evaluation of color and texture (Table 3) was observed. With regard to appearance, instrumental measure of color described sample D as the clearest sample and C as the darkest sample (Table 3). It agreed with the observed in all sensorial methods
that considered D as presenting the least intensity and C as the most intensity of brown color (Fig. 3 and Tables 4–6). Samples A and B, which were configured close in sensory methods with regard to dimension 1 (high correlation with attribute brown color) (Fig. 3 and Tables 4–6), did not present a difference in L* value (Table 3). In a general approach, similar profiles were observed in the samples configuration by GPA for the three methods (Fig. 3, Tables 4–6). The three studied methods described similar intensity of sweet taste and chocolate flavor in samples A and B, the highest intensity of these attributes in sample C, and the lowest in sample D. Samples C and D were described as presenting a higher residual bitter taste than samples A and B. The QDA and RDA panels presented similar results for aroma attribute, both characterised sample C with intense aroma of chocolate (Tables 7 and 8).
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Table 6 Attributes better correlated (r) with the first two dimensions for each assessora on RDA. A
Dimension 1
Dimension 2
1
Brown color ( 0.99); homogeneousness ( 0.86) and creaminess ( 0.86)
2
Brown color ( 0.75); brightness ( 0.99); creaminess (0.76); consistency ( 0.76) and chocolate flavor ( 0.99) Brown color ( 0.99); brightness ( 0.99); homogeneousness ( 0.99); consistency (0.86) and chocolate flavor ( 0.52) Homogeneousness ( 0.86); brightness ( 0.76); chocolate aroma ( 0.76); creaminess ( 0.86); sweet taste ( 0.99); chocolate flavor ( 0.86) and residual bitter taste ( 0.99) Brown color ( 0.75); homogeneousness ( 0.86); creaminess (0.76) and consistency ( 0.76) Brown color ( 0.75); brightness (0.99); creaminess ( 0.76); sweet taste ( 0.86) and residual bitter taste (0.86) Brown color ( 0.99); brightness (0.99); chocolate flavor ( 0.76) and sweet taste ( 0.86) Brown color ( 0.86); homogeneousness (0.86); chocolate aroma ( 0.99); creaminess (0.99); consistency ( 0.99); chocolate flavor ( 0.76) and sweet taste ( 0.99) Brown color ( 0.99); chocolate aroma ( 0.99); creaminess (0.75); consistency ( 0.75); chocolate flavor ( 0.99); sweet taste ( 0.99) and residual bitter taste (0.99) Brown color ( 0.86); brightness (0.76); homogeneousness (0.86); chocolate aroma ( 0.86); creaminess (0.75); consistency ( 0.76); chocolate flavor ( 0.99) and sweet taste ( 0.86) Brown color ( 0.99); chocolate aroma ( 0.76); creaminess (0.99); consistency ( 0.99); chocolate flavor ( 0.99) and sweet taste ( 0.52) Brown color ( 0.86); brightness (0.86); creaminess (0.75); consistency ( 0.75) and sweet taste ( 0.86) Chocolate flavor ( 0.99) and sweet taste ( 0.86)
Brightness ( 0.84); consistency (0.92); chocolate flavor ( 0.82) and residual bitter taste (0.82) Chocolate aroma ( 0.82); residual bitter taste (0.51); creaminess ( 0.65) and consistency (0.65) Creaminess ( 0.84) and residual bitter taste (0.51)
3 4 5 6 7 8 9 10
11 12 13 14 15 16 17 18 19 20
21 a
Brown color ( 0.99); homogeneousness ( 0.75); chocolate aroma ( 0.99) and chocolate flavor ( 0.76) Brown color ( 0.99); brightness ( 0.99); homogeneousness ( 0.86); chocolate aroma ( 0.99); chocolate flavor ( 0.99); sweet taste ( 0.99) and residual bitter taste (0.99) Brown color ( 0.99); brightness ( 0.86); homogeneousness ( 0.75); chocolate aroma ( 0.99); chocolate flavor ( 0.86) and sweet taste ( 0.86) Brown color ( 0.99); chocolate flavor ( 0.86) and sweet taste ( 0.99) Brown color ( 0.99); chocolate aroma ( 0.86); consistency ( 0.99) and chocolate flavor ( 0.76) Homogeneousness ( 0.99); chocolate aroma ( 0.86); creaminess (0.75); consistency ( 0.75) and sweet taste ( 0.86) Brown color ( 0.99); brightness ( 0.99); homogeneousness (0.86); chocolate aroma ( 0.99); creaminess (0.76); consistency ( 0.76); chocolate flavor ( 0.86) and sweet taste ( 0.86) Brown color ( 0.99); chocolate aroma ( 0.76); creaminess (0.99); consistency ( 0.99); chocolate flavor ( 0.99) and sweet taste ( 0.75)
Brown color ( 0.84) and consistency (0.67) Chocolate aroma ( 0.92); sweet taste ( 0.92) and residual bitter taste (0.92) Consistency (0.67) Chocolate aroma ( 0.92); creaminess ( 0.57) and consistency (0.57) Brightness ( 0.92) and residual bitter taste (0.82) Brightness ( 0.57) Residual bitter taste (0.92)
Brightness ( 0.67) and residual bitter taste ( 0.57) Homogeneousness ( 0.92); chocolate aroma (0.57); chocolate flavor (0.82) and residual bitter taste (0.51) Brown color ( 0.84); brightness (0.84); chocolate aroma ( 0.84); creaminess (0.51); consistency ( 0.51) and residual bitter taste (0.51) Brightness (0.92); sweet taste ( 0.92); residual bitter taste (0.82); creaminess ( 0.57) and consistency (0.57) Creaminess (0.84) and consistency (0.84) Creaminess ( 0.67) Homogeneousness ( 0.82); brightness (0.82); chocolate aroma (0.82); creaminess ( 0.82); consistency (0.82) and residual bitter taste (0.92) Brightness ( 0.82); creaminess ( 0.67); sweet taste ( 0.84) and residual bitter taste (0.82) Brown color ( 0.92); brightness ( 0.92); chocolate flavor (0.82) and residual bitter taste (0.82) Residual bitter taste (0.92)
Homogeneousness ( 0.92) and residual bitter taste (0.92)
Assessors were identified by numbers 1–21.
Table 7 Values of sensory attribute intensities analyzed by QDA.A,B Attributes
Samples A
Brown color Homogeneousness Brightness Chocolate aroma Chocolate flavor Sweet taste Residual bitter taste Consistency Creaminess
Table 8 Characterization of samples by RDA.A,B Attributes
B b
4.3 4.8a 4.2ab 4.5b 4.7b 5.1a 0.4c 3.4b 5.5b
C b
3.9 3.6a 4.2ab 3.4bc 3.0c 5.1a 1.4bc 3.0b 6.4ab
D a
7.5 3.4a 5.3a 6.0a 7.0a 3.9a 3.5ab 3.4b 6.7a
Samples A
c
1.6 4.0a 3.0b 2.4c 2.4c 1.8b 5.3a 5.5a 3.3c
A
Brown color Homogeneousness Bright Aroma Chocolate flavor Sweet taste Residual bitter taste Consistency Creaminess
B ab
61 65a 48a 53ab 54b 56a 37b 70a 36c
C b
51 55ab 58a 50b 43b 58a 39b 32c 67a
D a
77 54ab 56a 74a 80a 72a 66a 64ab 45bc
21c 36b 48a 33b 33b 24b 69a 44bc 62ab
A
Mean of 12 assessors. Different letters in the same line indicates significant differences. Tukey test (p < 0.05).
Rank sums values of 21 assessors. Different letters in the same line indicates significant differences (p 6 0.05) according to Newell and MacFarlane (1987).
The greatest differentiation between the methods’ performance was observed in texture evaluation. The QDA demonstrated better correlation with the instrumental results, considering sample B less consistent than D (Table 7). The RDA panel considered sample A more consistent than B and D (Table 8). In the instrumental profile of texture, sample B presented
hardness and gumminess less intense than sample D (Table 3). These parameters cold be associated with the sensory attribute of consistency (Table 2). In this way, it seems that the qualitative training (use of reference standards and glossary) was not sufficient for the consensual use of texture attributes by QDA and RDA panels.
B
B
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V.B. Richter et al. / Food Quality and Preference 21 (2010) 611–620 Table 9 Comparison of the requirements in the descriptive sensory methods studied.
Stages
a
Pre-selection Attributes developmenta Definition of attributesa Methods familiarisation Training Selection Samples evaluation
Total of sessions Number of assessors Amount of sample (pudding units) Analysis of results a
FCP
QDA
RDA
– Two sessions 56 samples One individual session Two sessions 84 samples – –
Four sessions Two sessions 48 samples Four sessions –
Four sessions Two sessions 84 samples Four sessions One session 84 samples – –
Four sessions 168 samples 7 14 308 GPA
Seven sessions Four sessions 144 samples Four sessions 144 samples 26 12 336 GPA e ANOVA
One session 84 samples 12 21 252 GPA e Friedman
Products, apart of samples, used in the selections and as standards had not been considered.
Comparing instrumental data with sensory methods, texture attributes analysed by QDA showed good agreement. The assessors of QDA and RDA were submitted to the same pre-selection tests and they all participated in the attributes development, and they had a ‘‘qualitative training” with the samples of reference. Therefore, all of the assessors were considered to have the same perception for each attribute. In this work, the better performance for texture attributes of QDA panel could be attributed to the training with scale endpoints. Although the three methods studied show efficient samples discrimination and consensus among assessors, the QDA training facilitated the understanding of the attributes definitions. A comparison of the applicability of the sensory methods (number of assessors, number of samples and time required) could be observed in Table 9. The data, however, do not consider the additional difficulty in the definition and preparation of the products used in the stages of pre-selection and standards for training (QDA and RDA). The FCP was the fastest method (seven sessions), since some stages conducted in the QDA and the RDA were eliminated from the FCP (pre-selection, consensus for attributes, training, and selection), implying an economy of samples in relation to the QDA. Due to the procedure of evaluation (three repetitions), even with fewer assessors, the FCP required a larger amount of samples when compared with the RDA. The initial procedures for the QDA and RDA were similar. In these two methods, the assessors underwent a pre-selection test and qualitative training sessions to improve their perception and to consensually define the attributes; this required the analyst to have experience in conducting these initial procedures. In RDA, due to the facility of application technique in the final evaluation of the samples, a reduction in the number of sessions and, consequently, the number of samples required compared with the QDA, was observed. However, it should be considered that since evaluation using RDA method was made only once for each sample, it did not allow to check repeatability of panel performance. All results were analysed using GPA; however, ANOVA can also be used for the QDA and the Test of Friedman for the RDA. This represents an advantage in relation to the FCP in view of the small availability GPA programmes. The requirement, in quantitative terms, of assessors, sessions, and samples will no doubt vary in relation to specific situations (e.g., the requirement for assessor retraining or new assessor selection in QDA). 5. Conclusions The method of Ranking Descriptive Analysis (RDA), using the pre-selection of assessors, attributes development and definition
(qualitative aspect) and final analysis using the ranking procedure for each attribute, allowed the discrimination of the samples with efficiency similar to that displayed by the descriptive methods of the QDA and FCP. The facility of the procedure for ranking samples (ordinal scale), in comparison with the use of interval scales used in the traditional descriptive techniques, showed that the RDA panel presented greater consensus. However, for some attributes, the training of the QDA panel facilitated the agreement of the definitions. A more intense qualitative training for RDA would allow more consistent results, primarily for complex attributes of texture. Despite the larger number of assessors required, RDA has the advantage of minor costs associated with the requirement of fewer sessions than QDA and a smaller amount of product than required by other sensory techniques. References Barylko-Pikielna, N., Matuszewska, I., Jeruszka, M., Kozlowska, K., Brzozowska, A., & Roszkowski, W. (2004). Discriminability and appropriateness of category scaling versus ranking methods to study sensory preferences in elderly. Food Quality and Preference, 15, 167–175. Dairou, V., & Sieffermann, J.-M. (2002). A comparison of 14 jams characterized by conventional profile and a quick original method, the flash profile. Journal of Food Science, 67, 826–834. Delarue, J., & Sieffermann, J.-M. (2004). Sensory mapping using flash profile. Comparison with a conventional descriptive method for the evaluation of the flavour of fruit dairy products. Food Quality and Preference, 15, 383–392. Dijksterhuis, G. B., & Gower, J. C. (1991). The interpretation of generalized procrustes analysis and allied methods. Food Quality and Preference, 3, 67–87. Gower, J. C. (1975). Generalized procrustes analysis. Psychometrika, 40, 33–51. Hein, K. A., Jaeger, S. R., Carr, T. B., & Delahunty, C. M. (2008). Comparison of five common acceptance and preference methods. Food Quality and Preference, 19, 651–661. Iop, S. C. F., Beleia, A. P., & Silva, R. S. F. (1999). Formulation and evaluation of dry dessert mix containing sweetener combinations using mixture response methodology. Food Chemistry, 66, 167–171. Kilcast, D., & Clegg, S. (2002). Sensory perception of creaminess and its relationship with food structure. Food Quality and Preference, 13, 609–623. Kim, K., & O’Mahony, M. A. (1998). New approach to category scales of intensity: Traditional vs. rank-rating. Journal of Sensory Studies, 13, 241–249. Lee, H. S., Hout, D., & O’Mahony, M. (2007). Sensory difference tests for margarine: A comparison of R-indices derived from ranking and A–Not A methods considering response bias and cognitive strategies. Food Quality and Preference, 18, 675–680. Lee, H. S., & O’Mahony, M. (2005). Sensory evaluation and marketing: Measurement of a consumer concept. Food Quality and Preference, 16, 227–235. Lethuaut, L., Brossard, C., Meynier, A., Rousseau, F., Llamas, G., Bousseau, B., et al. (2005). Sweetness and aroma perceptions in dairy desserts varying in sucrose and aroma levels and in textural agent. International Dairy Journal, 15, 485–493. Liem, D. G., Mars, M., & Graaf, C. (2004). Consistency of sensory testing with 4- and 5-year-old children. Food Quality and Preference, 15, 541–548. McEwan, J. A. (1999). Comparison of sensory panels: A ring trial. Food Quality and Preference, 42, 161–171. Meilgaard, M. C., Civille, G. V., & Carr, B. T. (1999). Sensory evaluation techniques. Boca Raton: CRC Press.
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Moskowitz, H. R. (1983). Product testing and sensory evaluation of foods – Marketing and R&D approaches. Westport: Food and Nutrition Press Inc. Murray, J. M., Delahunty, C. M., & Baxter, I. A. (2001). Descriptive sensory analysis: Past, present and future. Food Research International, 34, 461–471. Newell, G. J., & MacFarlane, J. D. (1987). Expanded tables for multiple comparison procedures in the analysis of ranked data. Journal of Food Science, 52, 1721–1725. Oliveira, A. P. V., & Benassi, M. T. (2010). Avaliação sensorial de pudins de chocolate com açúcar e dietéticos por Perfil Livre. Ciência e Agrotecnologia, 34, 146–154. Oliveira, A. P. V., Frasson, K., Almeida, T. C. A., & Benassi, M. T. (2004). Aceitação de sobremesas lácteas dietéticas e formuladas com açúcar: teste afetivo e mapa de preferência interno. Ciência e Tecnologia de Alimentos, 24, 627–633. OP & P Product Research (1998). Senstools Versão 2.3. Utrecht: OP & P Product Research. Penna, E. W. (1980). Evaluacion sensorial: una metodologia actual para tecnología de alimentos. Santiago: Universidade do Chile. Rodrigue, N., Guillet, M., Fortin, J., & Martin, J. F. (2000). Comparing information obtained from ranking and descriptive tests of four sweet corn products. Food Quality and Preference, 11, 47–54.
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Contents lists available at ScienceDirect
Food Quality and Preference journal homepage: www.elsevier.com/locate/foodqual
Proposing a ranking descriptive sensory method Vanessa Bragato Richter a, Tereza Cristina Avancini de Almeida b, Sandra Helena Prudencio c, Marta de Toledo Benassi c,* a
Departamento de Alimentação Escolar, Prefeitura de Guaira, Av. Cel. Otávio Costa, 126, 85980-000 Guairá, PR, Brazil Secretaria Municipal de Saúde, Prefeitura de Campinas, Avenida Anchieta, 200, 13015-904 Campinas, SP, Brazil c Depto. de Ciência e Tecnologia de Alimentos, Universidade Estadual de Londrina, CP 6001, 86051-970 Londrina, PR, Brazil b
a r t i c l e
i n f o
Article history: Received 3 September 2009 Received in revised form 18 March 2010 Accepted 18 March 2010 Available online 27 March 2010 Keywords: Ranking Descriptive analysis Free-Choice Profiling Quantitative Descriptive Analysis Generalized Procrustes Analysis Pudding
a b s t r a c t The objective of this work was to propose an alternative use to ranking method, as descriptive test, here named Ranking Descriptive Analysis (RDA). RDA was compared with Free-Choice Profiling (FCP) and Quantitative Descriptive Analysis (QDA). Four chocolate puddings were used as samples. A group of assessors performed FCP. Another group of selected assessors developed a list of attributes and their corresponding definitions. These assessors were divided into two groups: a panel was trained to perform the QDA and other panel was familiarised with the RDA procedures. Sample discrimination was similar using the three techniques. The RDA panel showed better consensus than the other two ones. The QDA showed the best correlation with the instrumental analysis of color and texture. Despite the larger number of assessors, RDA has the advantage of minor costs associated with the requirement of fewer sessions and a smaller amount of product than required by other techniques. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction The objective of descriptive methods is to characterise the sensory properties of a product, using a panel that evaluates the samples qualitatively and quantitatively (Murray, Delahunty, & Baxter, 2001). A well-known descriptive method is Quantitative Descriptive Analysis (QDA), which allows the characteristics of the product to be quantified, thus enabling a statistical treatment of the data. The QDA is appropriate when an evaluation requires detailed information on the sensory profile, identification, and quantification of the attributes. It allows the comparison of similar products, correlations with instrumental measures, and can be used to define the standard for quality control (Meilgaard, Civille, & Carr, 1999). Qualified assessors who have undergone long and expensive training are used to provide reliable and consistent results (Stone & Sidel, 1998). An alternative descriptive method, the Free-Choice Profiling (FCP), offers assessors the freedom to use descriptive terms in the amount and the way that they desire, reducing analysis time because training is not necessary. The results are evaluated by Generalized Procrustes Analysis (GPA), which fit each assessor con-
* Corresponding author. Tel.: +55 43 33714987; fax: +55 43 33714565. E-mail addresses: [email protected] (V.B. Richter), tereza_al [email protected] (T.C.A. de Almeida), [email protected] (S.H. Prudencio), martatb@ uel.br (M. de Toledo Benassi). 0950-3293/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodqual.2010.03.011
figuration to a consensus configuration, transforming the results to prevent variations in the use of the scale or the interpretation of the attributes (Dijksterhuis & Gower, 1991; Gower, 1975; Williams & Langron, 1984). The GPA can be used for other descriptive analyses to verify efficiency, repeatability, and consensus of the group (Rodrigue, Guillet, Fortin, & Martin, 2000). The ranking test is traditionally used as a discriminative test to compare many samples that are presented simultaneously (Meilgaard et al., 1999). Because of the simplicity of the procedure, the ranking technique has been used in consumer studies (BarylkoPikielna et al., 2004; Hein, Jaeger, Carr, & Delahunty, 2008; Lee, Hout, & O’Mahony, 2007; Lee & O’Mahony, 2005; Liem, Mars, & Graaf, 2004; Villanueva, Petenate, & Da Silva, 2005) and in the evaluation of the efficiency of panels with different levels of training (Kim & O’Mahony, 1998; McEwan, 1999). Despite the inherent differences between the techniques, their one common objective is to determine differences between the products’ attributes. The QDA and the FCP use a scale to measure the differences in intensity for each attribute. On the other hand, the ranking test presents ordinal results of intensity of the attributes; however, the magnitude of the differences is not obtained. One of the primary difficulties in traditional descriptive methods is the use of the scale itself and the consistency/repeatability of scores attributed to the sample. Observing the assessors’ behaviour during QDA training sessions, Kim and Mahoney (1998) verified that assessors often first organize the samples by intensity order of an attribute and then distribute samples in the scale.
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Ranking seems to be a facilitating procedure. There are situations in which the interest is not in the value on the scale, but whether there are significant differences among products and if so, in which attributes and in which ‘‘ranking order”. Dairou and Sieffermann (2002) proposed a variant of FCP, named Flash Profile (FP), and compared its efficiency with a conventional profile method. FP was a combination of free-choice terms selection with a ranking method based on simultaneous presentation of the products set. Delarue and Sieffermann (2004) compared FP and a conventional descriptive method in the evaluation of flavor of two kinds of fruit dairy products: yogurts and chesses. Similar profiles were obtained by the two methods and the main benefits of FP were its rapidity and easy of use. However, the authors pointed that the semantic consensus obtained in the conventional profile allowed a more accurate description of the products. Rodrigue et al. (2000) also suggested that a ranking test could be used in the description of a product. After four training sessions, a trained panel of eight assessors evaluated 10 attributes of sweet corn using the conventional method. An untrained panel of 20 assessors performed the same task on the same samples using a ranking procedure, but only one session was conducted to familiarise assessors with samples and ranking procedures. The results from both methods were similar in terms of overall product discrimination, but slight discrepancies were found in the discriminating attributes between panels. The findings suggest that when time is insufficient to train a panel, the use of an untrained panel and ranking test should be considered. Although Rodrigue et al. (2000) and Delarue and Sieffermann (2004) have described a good performance by a ranking panel, they also considered that other studies would be necessary to verify whether ranking tests could be used fruitfully or adapted to a wider range of products and a greater number of attributes. Another consideration is that in the study described by Rodrigue et al. (2000), the attributes were pre-defined and not developed by the panels, although they were validated by the panel. For untrained and completely naive assessors, grasping the relation between attribute and perception in one session could be a difficult task. A standardisation of samples’ characteristics, assessors’ sensibility, and the development of attributes by the panels would allow a better comparison of the efficiency of the methods. Chocolate pudding was chosen as sample in the current work because it is easy to prepare, it allows definition and control of differences in appearance, aroma, flavor, and texture attributes and it has been frequently used in descriptive methods studies in the literature (Kilcast & Clegg, 2002; Lethuaut et al., 2005; Weenen, Jellema, & Wijk, 2005; Wijk, Gemert, Terpstra, & Wilkinson, 2003; Wijk, Prinz, & Janssen, 2006). The objective of this study was to propose a descriptive sensory method of simple and fast application for situations in which the magnitude of attributes or the distance between categories is less relevant. The method, here called Ranking Descriptive Analysis (RDA), was compared to two traditional descriptive methods, Quantitative Descriptive Analysis and the Free-Choice Profiling, using chocolate puddings with sugar and different sweeteners as the sample.
2. Materials and methods 2.1. Samples Four formulations of chocolate pudding were used. Commercial powder cocoa, thickener carragena gum, sugar, and three sweeteners in different amounts were added to a basic mixture of commercial powder skimmed milk (100 g) and commercial starch of maize
(50 g) (Table 1). The formulations, based on Iop, Beleia, and Silva (1999), were developed to present differences in the characteristics of color, texture, aroma, and flavor. In preliminary tests was also considered that the formulations should reflect commercial products that were studied in previous researches (Oliveira & Benassi, 2010; Oliveira, Frasson, Almeida, & Benassi, 2004). The puddings were prepared with water (1 L) and cooked under constant agitation until boiling (12 min). Then, they were removed from heat, agitated for 2 min, and conditioned in proper containers according to the analysis. The samples were covered, to prevent drying, and stored under refrigeration (7 ± 2 °C) for approximately 20 h before analysis. 2.2. Instrumental evaluation of color and texture For texture evaluation, the puddings were conditioned in smooth plastic cups of 50 mL, 4.8 cm in diameter and 4 cm in height. The samples were analysed (15 repetitions) using a texturometer TAX-T2 (Stable Micro Systems) (Surrey, England), using cylindrical stainless probe P35, distance of penetration of 3 mm and force of 0.05 N. Hardness (N), cohesiveness (dimensionless), springiness (dimensionless), adhesiveness (Ns), and gumminess (N) were evaluated. For color analysis, samples were conditioned in Petri plates of 9 cm in diameter (three plates for sample). The readings were made (three repetitions for plate) using colorimeter Minolta CR10 (Tokyo, Japan), with a reading area of 8 mm, lighting CIE D65, and observer standard CIE 10° angle. The colorimeter directly supplied the values of L* (lightness), a* (red–green component), and b* (yellow–blue component) and the hue (H* = arc tang (b*/a*)) was calculated. A randomised design was applied and the results were submitted to the analysis of variance (ANOVA) and Tukey test (p < 0.05) (STATSOFT STATISTICA for Windows – Program manual, 1995). 2.3. Sensory analyses 2.3.1. Testing procedure and glossary development Samples (40 mL) were served cold (taken immediately from the refrigerator) in covered 100 mL transparent plastic cups, labelled with random-three digit codes. White light was used and the presentation order was randomised by session. Attribute terms for the evaluation of samples in the three methods were developed by the panel using the methodology described in Moskowitz (1983). Assessors were requested to record the similarities and differences between each sample. Two sessions were conducted. In each session, a pair of samples was presented by session, in order to obtain the highest possible number of attributes with regard to appearance, flavor, texture, and aroma. One pair was composed of samples B and C and the other
Table 1 Other ingredients of pudding formulations (in gram). Ingredients
Commercial powder cocoa (10–12% of fat) Carragena GENULACTA type LP-60 (CPKelco, USA) Sugar (commercial sucrose) Commercial sweetener with maltodextrine, sucralose and acesulfame potassium Commercial sweetener with maltodextrine, sodium saccharin, sodium cyclamate and sodium citrate Commercial sweetener with maltodextrine, stevia and silicium dioxide
Samples A (g)
B (g)
C (g)
D (g)
16 0.10 120
16 0.10
20 0.20
12 0.10
20 20 6
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pair, samples A and D. A detailed description of the glossary development for each method is presented on the next sections (2.3.2 and 2.3.3). In the three studied methods, during evaluation sessions, assessors received the developed glossary with the correspondent score sheet. For each panel, a protocol with instructions was offered reminding how to observe appearance, odour, flavor, texture, and mouthfeel after swallowing. For texture, it was emphasized that the assessor would need to cut the sample with the spoon, to compress a portion of the sample between the tongue and the palate, and ‘‘to chew” the sample until ready to swallow. The protocol was fixed in the booth during all of the evaluation sessions. For the FCP, the protocol was only used to facilitate the evaluation. For QDA and RDA, the protocol was the group consensus’ result. 2.3.2. Free-Choice Profile Fourteen assessors participated in the FCP. After the development of the attributes, individual score sheets and a specific list of each assessor’s definition of attributes were elaborated. The attributes varied in number, from 7 to 13, with an average of 10 per assessor. Attributes were evaluated using an unstructured 9 cm scale anchored in the endpoints with intensity terms. To verify the score sheets’ adequacy, the presentation for the descriptive test was simulated in two sessions. Three samples were evaluated in each session: A, B and D in the first session; B, C and A in the second session. The assessors could modify their score sheets, removing or including attributes, changing terms in the endpoints of the scales, and improving definitions. For evaluation, a balanced incomplete blocks design for four samples was used, with t = 4, k = 3, r = 3, b = 4, k = 2, E = 89. The results were analysed by Generalized Procrustes Analysis (GPA), using the Senstools program Version 2.3.28 (OP, 1998). In a preliminary analysis of the results, to verify panel’s performance, the general configuration of the assessors, the residual variances, and the individual configuration of the samples by each assessor were observed. Two assessors that presented low discrimination capacity, poor repeatability and lack of consensus with the group (residual variance superior to 1.5%, and individual configuration of samples different from the consensus plot and higher distances between samples repetition), had their results removed and the data analysis was repeated. The criterion adopted for consensual description of samples was to consider the attributes with highest correlation (minimum |0.5|) in each dimension for each assessor. 2.3.3. Quantitative Descriptive Analysis and Ranking Descriptive Analysis 2.3.3.1. Pre-selection. Initially 47 assessors have participated in a series of preliminary tests to evaluate their performance for aroma and basic tastes recognition, discrimination of color and hardness intensity. For aroma recognition, the minimum criterion was 70% correct answers. Approved assessors were then submitted to a test of recognition of basic tastes, while assessors who did not identify at least one of the standard solutions to each basic taste were removed from the panel (Penna, 1980). To evaluate the discrimination capacity for hardness and color intensities, ranking tests were applied, as in Meilgaard et al. (1999). The criterion adopted was 100% correct answers. For hardness, four samples of puddings were used, formulated as described for the sample A (Table 1) but with different starch concentrations: 40, 50, 70, and 90 g. The samples were evaluated by Instrumental Texture Profile and presented the following values of hardness: 0.69, 1.32, 4.2, and 6.7 N, similar to observed in commercial products (Oliveira & Benassi, 2010). For color, the samples were stand-
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ardised with skimmed milk and brown food colourants. After the tests, the group was composed of 33 selected assessors. 2.3.3.2. Definition of the descriptive terminology. After attributes development, assessors selected the most frequent terms to compose the score sheet. In a second session, assessors defined attributes and developed a consensus evaluation procedure using the suggested samples and market products as qualitative references (Table 2). A final session was conducted to verify the consensus on the list of attributes, glossary, and protocol of instructions. After this phase, the assessors were randomly divided into two panels, one panel with 21 people for the RDA and the other with 12 people for the QDA. Training and samples evaluation of each panel were conducted separately. 2.3.3.3. Quantitative Descriptive Analysis (QDA). Three sessions were necessary for assessors to reach consensus on reference samples that represented the extremities of the scale for each attribute and form elaboration. After this stage, four sessions were conducted for training in the use of the scales (Table 2). The score sheet was composed of unstructured 9 cm line scales anchored in the end points with intensity terms. At the final selection of assessors, four samples were used. To avoid an extreme familiarisation with samples that would be later evaluated, only sample C was used. The other two products were formulated in a manner similar to sample A, but with different starch concentrations (40 and 70 g) (Section 2.3.3.1). A commercial pudding characterised with regard to color (L* = 31 and H* = 38) and hardness (0.43 N) was also used. The presentation order of the four samples followed the statistical design of the FCP. To evaluate the discrimination and repeatability abilities of the assessors, a two factor (samples and repetitions) ANOVA and an F test for each attribute were conducted. To select assessors by discrimination ability, they had to present values of Fsamples with a maximum level of significance of 50% (p < 0.50). For repeatability, assessors with values of Frepetitions with a minimum level of significance of 5% were selected (p < 0.05). For evaluation of the agreement of the assessor with the panel, the average individual scores were compared to panel scores. Samples were evaluated in four sessions using the design of the FCP. Results were analysed using analysis of variance (ANOVA) for two sources of variation (samples and assessors) and an interaction of sample assessor, as well as a multiple comparison Tukey test (p < 0.05). In cases of significant Finteraction sample assessor, the main effects (samples) were tested against interaction mean square data. The results were also analysed by GPA, as described for the FCP. 2.3.3.4. Ranking Descriptive Analysis (RDA). One training session was conducted to demonstrate the score sheet used in the RDA. Two posterior sessions were used to define a more specific protocol for RDA. Samples were served at a table and the assessors were requested to evaluate all of the attributes so that the panel arrived at a consensus on the procedure and amount of sample to be served. Due to the number of attributes, the panel opted to evaluate the samples in two phases sequentially: one for analysis of appearance and aroma attributes and another for texture and flavor attributes. To familiarise assessors with the score sheet and attributes and to verify the efficiency of the protocol, a test with four samples was conducted. The same products used in the final selection of the assessors in QDA were used. The assessors had considered the procedure to be adequate (score sheet, attributes, and protocol) and the observed results showed that the panel was able to rank the products. The evaluation of the samples by RDA was completed in one session in which, for each attribute, samples were ordered by
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Table 2 Attributes, definitions and references used by QDAa,b and RDAb panels to describe the sensory properties of chocolate puddings. Attributea
Homogeneousness
Smooth, appearance with absence of lumps or bubbles Intensity of light reflection in the product, brightness intensity, opposite of opaque
Semi-sweet dark chocolate bar (Garoto ); milk chocolate (GarotoÒ) Creamy yogurt and consistent yogurt Sample A cover with oil; chocolate cream desert (DanetteÒ) and sample A with different starch content (20 and 35 g) Semi-sweet dark chocolate bar (GarotoÒ), milk chocolate (GarotoÒ)
Chocolate aroma
Chocolate aroma intensity
Chocolate flavor
Chocolate flavor intensity, characteristic of chocolate
Semi-sweet dark chocolate bar (GarotoÒ), milk chocolate (GarotoÒ)
Sweet taste
Sweet taste intensity, sugar taste
Water solutions (1 L) with different content of sugar: 200, 100, 50 and 10 g
Residual bitter taste
Residual bitter taste intensity; persistence of bitter taste in the mouth after swallowing the sample; off-taste of sweetener
Water solutions (1 L) with different sweeteners: sugar (10 g), sucralose and acesufame-k (10 g), saccharin and cyclamate (10 g) and stevia (10 g)
Consistency
Force required to obtain a given deformation, observed in the mouth or cutting with spoon Capacity to dissolve homogeneously in the mouth
Sample A with different starch content: 20, 25, 30 and 35 g Chocolate cream desert (DanetteÒ)
3. Results 3.1. Instrumental characterisation of texture and color Pudding samples showed different texture and color profiles (Table 3). Sample D was characterised as more firm and gummy than puddings B and C and it was also identified as more clear and yellowish than the others. Pudding C was differentiated as the darkest sample, less hard and gummy, and more elastic than samples A and B. Sample B was characterised as the least adhesive and presented intermediate values of hardness, lightness, and hue. Sample A was the most reddish sample.
Table 3 Instrumental characterisation of textureA and color.B,C Attributes
Samples A
Hardness (N) Adhesiveness (Ns) Springiness Cohesiveness Gummines (N) Lightness Hue B C
Light: milk chocolate (GarotoÒ) Dark: semi-sweet dark chocolate bar (GarotoÒ) Low: creamy yogurt Much: consistent yogurt Low: sample A Much: chocolate cream desert (DanetteÒ) Weak: milk chocolate (GarotoÒ) Intense: semi-sweet dark chocolate bar (GarotoÒ) Weak: milk chocolate (GarotoÒ) Intense: semi-sweet dark chocolate bar (GarotoÒ) Weak: milk dispersion (0.5 L) with unsweetened cocoa powder (15 g) and sugar (30 g) Intense: milk dispersion (0.5 L) with unsweetened cocoa powder (15 g) and sugar (30 g) None: milk dispersion (0.5 L) with unsweetened cocoa powder (15 g) and sugar (50 g) Intense: milk dispersion (0.5 L) with unsweetened cocoa powder (15 g) and commercial sweetener with saccharin and cyclamate (15 g) Low: sample A with 40 g of starch Much: sample A with 70 g of starch Low: sample A with 40 g of starch Much: chocolate cream desert (DanetteÒ)
Applied for QDA and RDA. Applied for QDA.
increasing intensity. The results were evaluated using Friedman test (Newell & MacFarlane, 1987) to evaluate sample differences for each attribute. In order to compare the configuration obtained with those observed in the two other methods, data was also evaluated by GPA, as described for the FCP.
A
Quantitative referenceb Ò
Brown color intensity
Creaminess a
Qualitative referencea
Brown color
Brightness
b
Definitiona
B ab
1.7 ± 0.2 0.4 ± 0.1a 0.86 ± 0.05b 0.57 ± 0.02a 0.99 ± 0.12ab 36.5 ± 0.2b 46.4 ± 0.4c
C b
1.6 ± 0.2 0.2 ± 0.2b 0.86 ± 0.05b 0.57 ± 0.01a 0.93 ± 0.09b 36.5 ± 1.2b 47.9 ± 0.4b
D c
1.4 ± 0.1 0.4 ± 0.5a 0.89 ± 0.0a 0.57 ± 0.02a 0.81 ± 0.07c 35.2 ± 0.6c 47.9 ± 0.6b
1.8 ± 0.1a 0.4 ± 0.1a 0.90 ± 0.01a 0.57 ± 0.12a 1.04 ± 0.06a 38.5 ± 0.4a 49.5 ± 2.1a
Average of 15 analyses ± standard deviation. Average of nine analyses ± standard deviation. Different letters in the same line indicates significant differences (p < 0.05).
3.2. Sensory characterisation 3.2.1. Evaluation of assessor’s performance: comparison among panel’s behaviour Analysis of the general configuration and residual variance of assessors of each panel demonstrated that there was consensus in the three panels, without behaviour discrepancies among members (Figs. 1 and 2). The QDA panel (Figs. 1B and 2B), which received quantitative training, and the FCP panel (Figs. 1A and 2A), which did not receive training, showed a greater dispersion of the assessors in the general configuration and in the residual variance (maximum of 1.2% and 1.5%, respectively) than the RDA panel; which showed less dispersion and a low residual variance (0.5%) (Figs. 1C and 2C).
3.2.2. Samples evaluation Fig. 3A and B show configuration of samples consensus in the FCP and the QDA; the products are represented by triangles that indicate the repeatability (the higher the distance between the vertices, the less repeatability). Both panels presented good repeatability for all samples. The attributes better correlated with the first two dimensions for each assessor were represented for FCP (Table 4), QDA (Table 5) and RDA (Table 6) panels. To facilitate the evaluation and comparison between methods, there were considered the attributes that presented higher correlation (minimum |0.5|) in each dimension for each assessor. Considering the consensus configuration, 38% of the observed variability was explained for the FCP. Dimension 1, responsible for 25% of the variance, could be explained (in the negative direction) for the attributes brown color, sweet taste, and chocolate flavor. These terms had high correlation for most of the panel members (nine assessors). Pudding D, located at the right side,
V.B. Richter et al. / Food Quality and Preference 21 (2010) 611–620
Fig. 1. Graphic representation of the assessors dispersion along the two first dimensions: general configuration of assessors in FCP (A), QDA (B) and RDA (C) panels. Numbers (1–21) indicates assessors.
was considered the clearest sample, with less sweet taste and chocolate flavor. The samples configured at the left side, B, A, and C (in this sequence), were characterised as presenting more accented color and flavor of chocolate, and also more sweet taste. Dimension 2 (13% of the variance) separated samples primarily by bitter taste and residual bitter taste. Puddings C and D, located in the superior part of the graph, were characterised by higher intensity of bitter taste (Fig. 3A, Table 4). In the QDA method, the first two dimensions were responsible for 46% of the variance (32% for dimension 1 and 14% for dimension 2). Dimension 1 was explained, in the negative direction, by attri-
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Fig. 2. Residual variance (%) associate with each assessor in FCP (A), QDA (B) and RDA (C) panels.
butes of brown color (12 assessors), chocolate flavor (10), chocolate aroma (9), creaminess (8), and brightness (6), and, in the positive direction, by attribute consistency (5). Dimension 2 was correlated negatively to sweet taste and positively to residual bitter taste. Therefore, samples located at the upper side were characterised by a higher intensity of residual bitter taste and lower sweet taste. Samples configured at the left side were described as more characteristic of chocolate (brown color, aroma and flavor of chocolate) and those located towards the right side were characterised as more consistent (Fig. 3B, Table 5).
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samples C and D, but sample B presented a less intense chocolate flavor as compared to sample A (Tables 5 and 7, Fig. 3B). In RDA method, the first two dimensions were responsible for 75% of the variance. Dimension 1 (51% of the variance) could be explained (in the negative direction) for the attributes brown color, chocolate flavor, sweet taste, and chocolate aroma. Dimension 2 (24% of the variance) separated the samples, primarily for the attribute bitter taste. The texture attributes correlated with two dimensions, positive for creaminess and negative for consistency, in dimension 1, with an inverse relationship in dimension 2 (Fig. 3C, Tables 6 and 8). Table 8 shows the results of the RDA panel by the attributes analysed. Pudding D was characterised as presenting the lightest brown color and with lowest sweet taste, while pudding C had the most intense chocolate aroma and flavor. These samples presented a residual bitter taste that was more intense than puddings A and B. Samples A and B were significantly different in relation to texture attributes, sample B was characterised as creamier and less consistent as compared to sample A. Sample B and D presented similar characteristics of texture.
3.3. Comparison of the applicability of the sensory methods A comparison of the number of required assessors, number of samples each assessor had to taste and time to run the tests for each stage of the methods is detailed in Table 9.
4. Discussion
Fig. 3. Two-dimensional consensus plot for chocolate puddings (A, B, C and D) obtained by FCP (A), QDA (B) and RDA (C) methods. Triangle vertices represent one of the three replicates analyses for each sample.
The mean values of the attributes’ intensity analysed by the QDA panel are represented in Table 7. The analysis of the results by ANOVA demonstrated that the formulations differed significantly with relation to the attributes of appearance, aroma, flavor, and texture. Sample D was characterised as being the most clear, less creamy, more consistent, with highest residual bitter taste and less sweet taste than the other samples. Pudding C presented brown color, high aroma, and chocolate flavor and was the second in intensity of residual bitter taste, when compared with the other samples. Samples A and the B were not differentiated in intensity of brown color and presented less residual bitter taste than
Comparing the panel’s performance on each method, it was observed consensus among assessors in the three panels (Figs. 1 and 2). The greater consensus observed in the RDA panel was likely due to the facility of the use of ordinal scales as compared with the use of interval scales in the traditional descriptive techniques. FCP and RDA panel’s presented good repeatability. Like others ranking tests, RDA does not allow checking panel repeatability because just one measure is taken for each sample (Fig. 3). The comparison of the samples configuration of consensus obtained by the three methods demonstrated that a higher amount of variance (75%) was observed for the RDA compared with FCP (38%) and ADQ (46%). This behaviour was already expected since the results of RDA consisted in less complex data than the obtained for FCP and QDA, allowing to show the main information in two dimensions. Comparing the samples distribution in RDA with FCP and QDA (Fig. 3), similarities were observed in configuration (primarily in dimension 1) and in the attributes more important to discrimination (Tables 4–6). Delarue and Sieffermann (2004) also obtained similar results comparing the profiles obtained by conventional descriptive method and a ranking procedure (FP). The largest difference in the samples distribution was observed in dimension 2, in which samples A and B appeared more discriminated. The attributes chocolate flavor and brown color in dimension 1 and residual bitter taste in dimension 2 presented high correlation in the three sensory methods. Both QDA and RDA assessors used aroma and texture descriptor more consensually than the FCP panel (Fig. C, Tables 4–6). In general, consensus among the methods was observed. All of the results were coherent for the discrimination of the products and similar for the three panels. The same attributes were important for the characterisation in the three sensory methods and all samples were discriminated. It should be noted that the use of GPA allowed the comparison of the performance of the three panels.
V.B. Richter et al. / Food Quality and Preference 21 (2010) 611–620
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Table 4 Attributes better correlated (r) with the first two dimensions for each assessora on FCP. Aa
Dimension 1
Dimension 2
1 2 3
Brown color ( 0.78); sweet taste ( 0.95); chocolate flavor ( 0.78) and brightness ( 0.61) Brown color ( 0.85); sweet taste ( 0.92); chocolate flavor ( 0.70) and brightness ( 0.69) Brown color ( 0.65) and sweet taste ( 0.50)
4 6
Chocolate color ( 0.57); sweet taste ( 0.57); chocolate flavor ( 0.50); consistency (appearance) ( 0.56) and firmness (cut) (0.74) Sweet taste ( 0.82); chocolate flavor ( 0.81) and bubbles ( 0.63)
Bitter taste (0.72); crust thickness (0.50) and crust firmness (0.51) Bitter taste (0.77) and chocolate aroma ( 0.54) Bitter taste (0.52); residual bitter taste (0.50); chocolate aroma (0.50) and syneresis (0.57) Bitter taste (0.83); sour taste (0.86) and consistency ( 0.53)
7
Brown color ( 0.76); sweet taste ( 0.50) and chocolate flavor ( 0.64)
8
Brown color ( 0.59) and sweet taste ( 0.88)
9 10 11
Sweet taste ( 0.74); chocolate flavor ( 0.72) and consistency (cut) (0.69) Chocolate flavor ( 0.73); chocolate aroma (0.59) and homogeneous surface ( 0.69) Chocolate color ( 0.87); sweet taste ( 0.83); chocolate flavor ( 0.77); chocolate aroma ( 0.79) and homogeneousness ( 0.91) Brown color (0.64); chocolate flavor ( 0.79); chocolate aroma ( 0.60) and consistency (cut) (0.76) Brown color (0.60) and smooth surface (0.57)
13 14 a
Bitter taste (0.69); residual bitter taste (0.71) and chocolate aroma (0.51) Bitter taste (0.59); chocolate aroma (0.52) and creamy texture ( 0.61) Smooth surface (0.57); consistency (cut) (0.53) and homogeneousness (0.53) Surface crust (0.59) Sweet taste ( 0.69) Consistency (mouth) ( 0.50) Sweet taste ( 0.65) Bitter taste (0.69); sweet taste ( 0.71); chocolate flavor ( 0.58) and viscosity (0.51)
Assessors were identified by numbers 1–14.
Table 5 Attributes better correlated (r) with the first two dimensions for each assessora on QDA. A
Dimension 1
Dimension 2
1
Brown color ( 0.88); chocolate aroma ( 0.78); chocolate flavor ( 0.67) and residual bitter taste (0.67) Brown color ( 0.76); chocolate aroma ( 0.61) and chocolate flavor ( 0.71)) Brown color ( 0.73); chocolate aroma ( 0.75); creaminess ( 0.87); consistency (0.77) and residual bitter taste (0.84) Brown color ( 0.76); brightness ( 0.80); chocolate aroma ( 0.52); creaminess ( 0.64); consistency (0.77) and chocolate flavor ( 0.50) Brown color ( 0.87); brightness ( 0.51); chocolate aroma ( 0.69); creaminess ( 0.70); consistency (0.51); chocolate flavor ( 0.50) and residual bitter taste ( 0.68) Brown color ( 0.85); brightness ( 0.82); homogeneousness ( 0.74); chocolate aroma ( 0.64) and chocolate flavor ( 0.82) Brown color ( 0.83); brightness ( 0.58); chocolate aroma ( 0.69); creaminess ( 0.85); consistency (0.64); chocolate flavor ( 0.68); sweet taste ( 0.83) and residual bitter taste (0.67) Brown color ( 0.71); brightness ( 0.75); chocolate aroma ( 0.51); creaminess ( 0.60) and chocolate flavor ( 0.71) Brown color ( 0.72) and brightness ( 0.61) Brown color ( 0.78); creaminess ( 0.79); consistency (0.77) and chocolate flavor ( 0.72) Brown color ( 0.81); creaminess ( 0.67); chocolate flavor ( 0.88) and sweet taste ( 0.79) Brown color ( 0.56); chocolate aroma ( 0.57) and creaminess ( 0.82)
Creaminess ( 0.63); consistency (0.65) and sweet taste ( 0.55)
2 3 4 5 6
7
8 9 10 11 12 a
Homogeneousness (0.51) and sweet taste ( 0.73) Chocolate flavor (0.73) and sweet taste ( 0.64) Sweet taste ( 0.74) and residual bitter taste (0.87) Sweet taste ( 0.51) Sweet taste ( 0.52); consistency ( 0.51) and residual bitter taste ( 0.50)
Sweet taste ( 0.67) and residual bitter taste (0.51) Residual bitter taste (0.53) Sweet taste ( 0.65) and residual bitter taste (0.94) Residual bitter taste (0.64) Sweet taste ( 0.81) and residual bitter taste (0.61)
Assessors were identified by numbers 1–12.
These results agreed with those described by Rodrigue et al. (2000) in a study with two panels: one trained for descriptive analysis and another not trained for the ranking test. Results from the study showed similarities between the trained and untrained panels. These authors suggest that when time is insufficient to train a panel, the use of an untrained panel and a ranking test should be considered. However, we observed it was important to train a panel in order to obtain good descriptor conceptualisation and greater panel consensus. Consensus across the characterisations of the samples obtained the descriptive analysis methods (Fig. 3, Tables 4–6) and the instrumental evaluation of color and texture (Table 3) was observed. With regard to appearance, instrumental measure of color described sample D as the clearest sample and C as the darkest sample (Table 3). It agreed with the observed in all sensorial methods
that considered D as presenting the least intensity and C as the most intensity of brown color (Fig. 3 and Tables 4–6). Samples A and B, which were configured close in sensory methods with regard to dimension 1 (high correlation with attribute brown color) (Fig. 3 and Tables 4–6), did not present a difference in L* value (Table 3). In a general approach, similar profiles were observed in the samples configuration by GPA for the three methods (Fig. 3, Tables 4–6). The three studied methods described similar intensity of sweet taste and chocolate flavor in samples A and B, the highest intensity of these attributes in sample C, and the lowest in sample D. Samples C and D were described as presenting a higher residual bitter taste than samples A and B. The QDA and RDA panels presented similar results for aroma attribute, both characterised sample C with intense aroma of chocolate (Tables 7 and 8).
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Table 6 Attributes better correlated (r) with the first two dimensions for each assessora on RDA. A
Dimension 1
Dimension 2
1
Brown color ( 0.99); homogeneousness ( 0.86) and creaminess ( 0.86)
2
Brown color ( 0.75); brightness ( 0.99); creaminess (0.76); consistency ( 0.76) and chocolate flavor ( 0.99) Brown color ( 0.99); brightness ( 0.99); homogeneousness ( 0.99); consistency (0.86) and chocolate flavor ( 0.52) Homogeneousness ( 0.86); brightness ( 0.76); chocolate aroma ( 0.76); creaminess ( 0.86); sweet taste ( 0.99); chocolate flavor ( 0.86) and residual bitter taste ( 0.99) Brown color ( 0.75); homogeneousness ( 0.86); creaminess (0.76) and consistency ( 0.76) Brown color ( 0.75); brightness (0.99); creaminess ( 0.76); sweet taste ( 0.86) and residual bitter taste (0.86) Brown color ( 0.99); brightness (0.99); chocolate flavor ( 0.76) and sweet taste ( 0.86) Brown color ( 0.86); homogeneousness (0.86); chocolate aroma ( 0.99); creaminess (0.99); consistency ( 0.99); chocolate flavor ( 0.76) and sweet taste ( 0.99) Brown color ( 0.99); chocolate aroma ( 0.99); creaminess (0.75); consistency ( 0.75); chocolate flavor ( 0.99); sweet taste ( 0.99) and residual bitter taste (0.99) Brown color ( 0.86); brightness (0.76); homogeneousness (0.86); chocolate aroma ( 0.86); creaminess (0.75); consistency ( 0.76); chocolate flavor ( 0.99) and sweet taste ( 0.86) Brown color ( 0.99); chocolate aroma ( 0.76); creaminess (0.99); consistency ( 0.99); chocolate flavor ( 0.99) and sweet taste ( 0.52) Brown color ( 0.86); brightness (0.86); creaminess (0.75); consistency ( 0.75) and sweet taste ( 0.86) Chocolate flavor ( 0.99) and sweet taste ( 0.86)
Brightness ( 0.84); consistency (0.92); chocolate flavor ( 0.82) and residual bitter taste (0.82) Chocolate aroma ( 0.82); residual bitter taste (0.51); creaminess ( 0.65) and consistency (0.65) Creaminess ( 0.84) and residual bitter taste (0.51)
3 4 5 6 7 8 9 10
11 12 13 14 15 16 17 18 19 20
21 a
Brown color ( 0.99); homogeneousness ( 0.75); chocolate aroma ( 0.99) and chocolate flavor ( 0.76) Brown color ( 0.99); brightness ( 0.99); homogeneousness ( 0.86); chocolate aroma ( 0.99); chocolate flavor ( 0.99); sweet taste ( 0.99) and residual bitter taste (0.99) Brown color ( 0.99); brightness ( 0.86); homogeneousness ( 0.75); chocolate aroma ( 0.99); chocolate flavor ( 0.86) and sweet taste ( 0.86) Brown color ( 0.99); chocolate flavor ( 0.86) and sweet taste ( 0.99) Brown color ( 0.99); chocolate aroma ( 0.86); consistency ( 0.99) and chocolate flavor ( 0.76) Homogeneousness ( 0.99); chocolate aroma ( 0.86); creaminess (0.75); consistency ( 0.75) and sweet taste ( 0.86) Brown color ( 0.99); brightness ( 0.99); homogeneousness (0.86); chocolate aroma ( 0.99); creaminess (0.76); consistency ( 0.76); chocolate flavor ( 0.86) and sweet taste ( 0.86) Brown color ( 0.99); chocolate aroma ( 0.76); creaminess (0.99); consistency ( 0.99); chocolate flavor ( 0.99) and sweet taste ( 0.75)
Brown color ( 0.84) and consistency (0.67) Chocolate aroma ( 0.92); sweet taste ( 0.92) and residual bitter taste (0.92) Consistency (0.67) Chocolate aroma ( 0.92); creaminess ( 0.57) and consistency (0.57) Brightness ( 0.92) and residual bitter taste (0.82) Brightness ( 0.57) Residual bitter taste (0.92)
Brightness ( 0.67) and residual bitter taste ( 0.57) Homogeneousness ( 0.92); chocolate aroma (0.57); chocolate flavor (0.82) and residual bitter taste (0.51) Brown color ( 0.84); brightness (0.84); chocolate aroma ( 0.84); creaminess (0.51); consistency ( 0.51) and residual bitter taste (0.51) Brightness (0.92); sweet taste ( 0.92); residual bitter taste (0.82); creaminess ( 0.57) and consistency (0.57) Creaminess (0.84) and consistency (0.84) Creaminess ( 0.67) Homogeneousness ( 0.82); brightness (0.82); chocolate aroma (0.82); creaminess ( 0.82); consistency (0.82) and residual bitter taste (0.92) Brightness ( 0.82); creaminess ( 0.67); sweet taste ( 0.84) and residual bitter taste (0.82) Brown color ( 0.92); brightness ( 0.92); chocolate flavor (0.82) and residual bitter taste (0.82) Residual bitter taste (0.92)
Homogeneousness ( 0.92) and residual bitter taste (0.92)
Assessors were identified by numbers 1–21.
Table 7 Values of sensory attribute intensities analyzed by QDA.A,B Attributes
Samples A
Brown color Homogeneousness Brightness Chocolate aroma Chocolate flavor Sweet taste Residual bitter taste Consistency Creaminess
Table 8 Characterization of samples by RDA.A,B Attributes
B b
4.3 4.8a 4.2ab 4.5b 4.7b 5.1a 0.4c 3.4b 5.5b
C b
3.9 3.6a 4.2ab 3.4bc 3.0c 5.1a 1.4bc 3.0b 6.4ab
D a
7.5 3.4a 5.3a 6.0a 7.0a 3.9a 3.5ab 3.4b 6.7a
Samples A
c
1.6 4.0a 3.0b 2.4c 2.4c 1.8b 5.3a 5.5a 3.3c
A
Brown color Homogeneousness Bright Aroma Chocolate flavor Sweet taste Residual bitter taste Consistency Creaminess
B ab
61 65a 48a 53ab 54b 56a 37b 70a 36c
C b
51 55ab 58a 50b 43b 58a 39b 32c 67a
D a
77 54ab 56a 74a 80a 72a 66a 64ab 45bc
21c 36b 48a 33b 33b 24b 69a 44bc 62ab
A
Mean of 12 assessors. Different letters in the same line indicates significant differences. Tukey test (p < 0.05).
Rank sums values of 21 assessors. Different letters in the same line indicates significant differences (p 6 0.05) according to Newell and MacFarlane (1987).
The greatest differentiation between the methods’ performance was observed in texture evaluation. The QDA demonstrated better correlation with the instrumental results, considering sample B less consistent than D (Table 7). The RDA panel considered sample A more consistent than B and D (Table 8). In the instrumental profile of texture, sample B presented
hardness and gumminess less intense than sample D (Table 3). These parameters cold be associated with the sensory attribute of consistency (Table 2). In this way, it seems that the qualitative training (use of reference standards and glossary) was not sufficient for the consensual use of texture attributes by QDA and RDA panels.
B
B
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V.B. Richter et al. / Food Quality and Preference 21 (2010) 611–620 Table 9 Comparison of the requirements in the descriptive sensory methods studied.
Stages
a
Pre-selection Attributes developmenta Definition of attributesa Methods familiarisation Training Selection Samples evaluation
Total of sessions Number of assessors Amount of sample (pudding units) Analysis of results a
FCP
QDA
RDA
– Two sessions 56 samples One individual session Two sessions 84 samples – –
Four sessions Two sessions 48 samples Four sessions –
Four sessions Two sessions 84 samples Four sessions One session 84 samples – –
Four sessions 168 samples 7 14 308 GPA
Seven sessions Four sessions 144 samples Four sessions 144 samples 26 12 336 GPA e ANOVA
One session 84 samples 12 21 252 GPA e Friedman
Products, apart of samples, used in the selections and as standards had not been considered.
Comparing instrumental data with sensory methods, texture attributes analysed by QDA showed good agreement. The assessors of QDA and RDA were submitted to the same pre-selection tests and they all participated in the attributes development, and they had a ‘‘qualitative training” with the samples of reference. Therefore, all of the assessors were considered to have the same perception for each attribute. In this work, the better performance for texture attributes of QDA panel could be attributed to the training with scale endpoints. Although the three methods studied show efficient samples discrimination and consensus among assessors, the QDA training facilitated the understanding of the attributes definitions. A comparison of the applicability of the sensory methods (number of assessors, number of samples and time required) could be observed in Table 9. The data, however, do not consider the additional difficulty in the definition and preparation of the products used in the stages of pre-selection and standards for training (QDA and RDA). The FCP was the fastest method (seven sessions), since some stages conducted in the QDA and the RDA were eliminated from the FCP (pre-selection, consensus for attributes, training, and selection), implying an economy of samples in relation to the QDA. Due to the procedure of evaluation (three repetitions), even with fewer assessors, the FCP required a larger amount of samples when compared with the RDA. The initial procedures for the QDA and RDA were similar. In these two methods, the assessors underwent a pre-selection test and qualitative training sessions to improve their perception and to consensually define the attributes; this required the analyst to have experience in conducting these initial procedures. In RDA, due to the facility of application technique in the final evaluation of the samples, a reduction in the number of sessions and, consequently, the number of samples required compared with the QDA, was observed. However, it should be considered that since evaluation using RDA method was made only once for each sample, it did not allow to check repeatability of panel performance. All results were analysed using GPA; however, ANOVA can also be used for the QDA and the Test of Friedman for the RDA. This represents an advantage in relation to the FCP in view of the small availability GPA programmes. The requirement, in quantitative terms, of assessors, sessions, and samples will no doubt vary in relation to specific situations (e.g., the requirement for assessor retraining or new assessor selection in QDA). 5. Conclusions The method of Ranking Descriptive Analysis (RDA), using the pre-selection of assessors, attributes development and definition
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