The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
Hildegarde Heymann - One of the best experts on this subject based on the ideXlab platform.
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Do panelists memorize products when performing Descriptive Analysis on few products?
Journal of Sensory Studies, 2018Co-Authors: Pauline Lestringant, Julien Delarue, Hildegarde HeymannAbstract:Few studies have investigated the impact of the number of samples on the Descriptive Analysis (DA) process and results. One may hypothesize that when training on and evaluating small product sets, panelists memorize the products instead of actually evaluating the samples based on their perceptions of the attributes. In this study, we tested whether products were recognized during the training and evaluation phases of separate ketchup and lemonade panels. After performing a full DA task, panelists did a recognition test, in which they were asked to estimate the number of products they evaluated and which ones they recognized from a larger sample set. Panelists did not realize how many different samples they tasted, neither during training nor evaluation. Yet, they were able to recognize samples they had previously tasted in DA. Similarities among products led to mistakes. However, samples that were very different from others were not better recognized. Practical applications: This study is a first attempt at evaluating the extent of product recognition in a realistic Descriptive Analysis context. The goal of sensory profiles is to measure human perception of products, which can be biased by memory effects. Since memorization may depend on the number of products panelists are trained on, we tested a “worst case scenario” with only four products during the training phase. This is relevant to many situations in which small sample sizes occur: a 2 x 2 experimental design, few innovative products present on the market, or limited number of prototypes available.
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At the heart of Descriptive Analysis: effects of adding extra samples to a product set
2017Co-Authors: Pauline Lestringant, Julien Delarue, Hildegarde HeymannAbstract:At the heart of Descriptive Analysis: effects of adding extra samples to a product set. 12. Pangborn Sensory Science Symposium
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2010-2015: How have Descriptive Analysis methods really been used? A review of recent publications
2017Co-Authors: Pauline Lestringant, Julien Delarue, Hildegarde HeymannAbstract:2010-2015: How have Descriptive Analysis methods really been used? A review of recent publications. 12. Pangborn Sensory Science Symposium (Pangborn 2017)
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Limitations of performing Descriptive Analysis methods with few samples: Do judges memorize the products?
2016Co-Authors: Pauline Lestringant, Julien Delarue, Hildegarde HeymannAbstract:Limitations of performing Descriptive Analysis methods with few samples: Do judges memorize the products?. 7. European Conference on Sensory and Consumer Research (EuroSense 2016)
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PROJECTIVE MAPPING AND Descriptive Analysis OF MILK AND DARK CHOCOLATES
Journal of Sensory Studies, 2009Co-Authors: J. Kennedy, Hildegarde HeymannAbstract:ABSTRACT Recently there has been renewed interest in the use of projective mapping to evaluate overall product similarity. Previous studies show a high degree of similarity between product maps from projective mapping and Descriptive Analysis techniques; however, trained panels had been used in these studies. In this study, three groups of untrained panelists performed projective mapping on a set of chocolates and then were trained and performed Descriptive Analysis. Principal component Analysis and multifactor Analysis were used to generate product maps of the Descriptive Analysis and projective mapping data, respectively. All six maps were visually similar, and RV-coefficients between the six data sets were greater than 0.8. The results indicate that the use of untrained judges for the projective mapping provided equivalent product spaces as obtained by Descriptive Analysis for this set of products. Furthermore, the similarity among panels indicates that overall the panelists perceived the chocolates in a similar manner. PRACTICAL APPLICATIONS The current study indicates that projective mapping can provide equivalent product maps to those obtained by Descriptive Analysis. On its own, projective mapping presents an efficient method to obtain overall product differences from consumers or trained judges. In combination with a Descriptive technique, projective mapping performed by consumers can provide insight as to which attributes are most important to consumers in determining product differences.
Harry T. Lawless - One of the best experts on this subject based on the ideXlab platform.
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Group Exercise in Descriptive Analysis
Food Science Text Series, 2012Co-Authors: Harry T. LawlessAbstract:This exercise shows how a Descriptive Analysis project can be performed by small groups of students. Students may select their own product category, build a lexicon and ballot for sensory Analysis, and conduct an evaluation of several products from their chosen category. Analysis of data, graphing of results, a report, and presentation may also be required. The goals of this lab are (1) to reinforce the understanding of Descriptive Analysis procedures, (2) to provide an experience in teamwork and division of labor, and (3) to provide practice in written and verbal communication skills. This exercise may be used in place of the Descriptive Analysis lab in Chap. 10.
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Introduction to Descriptive Analysis
Food Science Text Series, 2012Co-Authors: Harry T. LawlessAbstract:This exercise provides basic exposure to quantitative Descriptive Analysis procedures. It demonstrates how term generation works in a group setting with several sample products from the same category. Using the terms generated for the products, a ballot is then constructed, and students evaluate blind samples on intensity scales. Basic Analysis of variance and planned comparisons between means are used for data Analysis. This exercise is best performed by an instructor who is familiar with panel leadership and term generation.
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Quantitative Descriptive Analysis and principal component Analysis for sensory characterization of ultrapasteurized milk.
Journal of dairy science, 2001Co-Authors: Kathryn W. Chapman, Harry T. Lawless, Kathryn J. BoorAbstract:Quantitative Descriptive Analysis was used to describe the key attributes of nine ultrapasteurized (UP) milk products of various fat levels, including two lactose-reduced products, from two dairy plants. Principal components Analysis identified four significant principal components that accounted for 87.6% of the variance in the sensory attribute data. Principal component scores indicated that the location of each UP milk along each of four scales primarily corresponded to cooked, drying/lingering, sweet, and bitter attributes. Overall product quality was modeled as a function of the principal components using multiple least squares regression (R2 = 0.810). These findings demonstrate the utility of quantitative Descriptive Analysis for identifying and measuring UP fluid milk product attributes that are important to consumers.
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Descriptive Analysis of complex odors: reality, model or illusion?
Food Quality and Preference, 1999Co-Authors: Harry T. LawlessAbstract:Abstract The primary sensory tool for specifying the characteristics of a complex aroma, fragrance, flavor or other odorous mixture of volatiles is Descriptive Analysis. Descriptive Analysis uses a trained panel to specify the intensities of specific attributes, based on a psychophysical model for intensity scaling. However, the use of Descriptive techniques for complex and well-blended aromas gives rise to several problems. The psychophysical intensity model based upon independent odor notes may be a poor way to characterize odor experience, bringing into question whether Descriptive Analysis is an adequate tool for sensory Analysis of complex smells. These problems include the following: (1) disagreement among experts in the most prominent odor notes of a single product and other individual differences problems, (2) a correspondence between similarity scaling and intensity scaling, (3) the substitution of applicability measures for intensity, (4) the need to use mid-tier, general odor terms for profiling complex fragrances, and (5) blending and integration effects. Data will be presented on citrus–woody mixtures showing that ratings of similarity and intensity are highly correlated, suggesting a common underlying process for both ratings. A related issue concerns whether odors and their mixtures are perceived as unitary or analyzable percepts. With these same stimuli, the perception of singularity vs. mixed-ness of stimuli is difficult to predict. Sensory scientists should question the validity of Descriptive data for such stimuli and avoid the simplistic mistake of equating data with perception. The use of simple and apparently independent intensity scales may produce the illusion that the odor experience is a collection of independent analyzable “notes” when it is not. ©
Julien Delarue - One of the best experts on this subject based on the ideXlab platform.
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Do panelists memorize products when performing Descriptive Analysis on few products?
Journal of Sensory Studies, 2018Co-Authors: Pauline Lestringant, Julien Delarue, Hildegarde HeymannAbstract:Few studies have investigated the impact of the number of samples on the Descriptive Analysis (DA) process and results. One may hypothesize that when training on and evaluating small product sets, panelists memorize the products instead of actually evaluating the samples based on their perceptions of the attributes. In this study, we tested whether products were recognized during the training and evaluation phases of separate ketchup and lemonade panels. After performing a full DA task, panelists did a recognition test, in which they were asked to estimate the number of products they evaluated and which ones they recognized from a larger sample set. Panelists did not realize how many different samples they tasted, neither during training nor evaluation. Yet, they were able to recognize samples they had previously tasted in DA. Similarities among products led to mistakes. However, samples that were very different from others were not better recognized. Practical applications: This study is a first attempt at evaluating the extent of product recognition in a realistic Descriptive Analysis context. The goal of sensory profiles is to measure human perception of products, which can be biased by memory effects. Since memorization may depend on the number of products panelists are trained on, we tested a “worst case scenario” with only four products during the training phase. This is relevant to many situations in which small sample sizes occur: a 2 x 2 experimental design, few innovative products present on the market, or limited number of prototypes available.
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At the heart of Descriptive Analysis: effects of adding extra samples to a product set
2017Co-Authors: Pauline Lestringant, Julien Delarue, Hildegarde HeymannAbstract:At the heart of Descriptive Analysis: effects of adding extra samples to a product set. 12. Pangborn Sensory Science Symposium
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2010-2015: How have Descriptive Analysis methods really been used? A review of recent publications
2017Co-Authors: Pauline Lestringant, Julien Delarue, Hildegarde HeymannAbstract:2010-2015: How have Descriptive Analysis methods really been used? A review of recent publications. 12. Pangborn Sensory Science Symposium (Pangborn 2017)
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Limitations of performing Descriptive Analysis methods with few samples: Do judges memorize the products?
2016Co-Authors: Pauline Lestringant, Julien Delarue, Hildegarde HeymannAbstract:Limitations of performing Descriptive Analysis methods with few samples: Do judges memorize the products?. 7. European Conference on Sensory and Consumer Research (EuroSense 2016)
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Descriptive Analysis: going beyond sensory
2016Co-Authors: Julien DelarueAbstract:Descriptive Analysis: going beyond sensory. 5. E3S symposium
Kathryn J. Boor - One of the best experts on this subject based on the ideXlab platform.
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Quantitative Descriptive Analysis and principal component Analysis for sensory characterization of ultrapasteurized milk.
Journal of dairy science, 2001Co-Authors: Kathryn W. Chapman, Harry T. Lawless, Kathryn J. BoorAbstract:Quantitative Descriptive Analysis was used to describe the key attributes of nine ultrapasteurized (UP) milk products of various fat levels, including two lactose-reduced products, from two dairy plants. Principal components Analysis identified four significant principal components that accounted for 87.6% of the variance in the sensory attribute data. Principal component scores indicated that the location of each UP milk along each of four scales primarily corresponded to cooked, drying/lingering, sweet, and bitter attributes. Overall product quality was modeled as a function of the principal components using multiple least squares regression (R2 = 0.810). These findings demonstrate the utility of quantitative Descriptive Analysis for identifying and measuring UP fluid milk product attributes that are important to consumers.
Nathalie Martin - One of the best experts on this subject based on the ideXlab platform.
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sorting procedure as an alternative to quantitative Descriptive Analysis to obtain a product sensory map
Food Quality and Preference, 2006Co-Authors: Raphaelle Cartier, Andreas Rytz, Angele Lecomte, Fabienne Poblete, Jocelyne Krystlik, Emmanuelle Belin, Nathalie MartinAbstract:The objective of the present study was to investigate the efficiency of sorting procedures, as an alternative to quantitative Descriptive Analysis, to obtain a sensory map of food products. First, we investigated if sorting by a trained panel would give similar results to quantitative Descriptive Analysis. Principal component Analysis of quantitative Descriptive data and multidimensional scaling of sorting results led to similar product maps and sensory description, even if slightly more detailed in quantitative Descriptive Analysis. Second, we examined if sorting performed by trained and untrained panelists led to the same conclusions. The perceptual organisation was similar whatever the level of panelist expertise. Finally, we investigated if sorting by untrained panelists led to consistent data. Familiarisation with the procedure and the products did not induce any major change. The results showed that sorting combined with verbalisation led to meaningful and consistent product sensory mapping, whatever the panelist's level of training.
