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Hedonic Response (hedonic + response)
Selected AbstractsESTIMATION OF HEDONIC RESPONSES FROM DESCRIPTIVE SKIN SENSORY DATA BY CHI-SQUARE MINIMIZATIONJOURNAL OF SENSORY STUDIES, Issue 1 2006I.F. ALMEIDA ABSTRACT Six topical formulations were evaluated by a trained panel according to a descriptive analysis methodology and by a group of consumers who rated the products on a hedonic scale. We present a new approach that describes the categorical appreciation of appearance, texture and skinfeel of the formulations by the consumers as a function of related sensory attributes assessed by the trained panel. For each hedonic attribute, a latent random variable depending on the sensory attributes is constructed and made discrete (in a nonlinear fashion) according to the distribution of consumer-hedonic scores in such a way as to minimize a corresponding chi-square criterion. Standard partial least squares (PLS) regression, bootstrapping and cross-validation techniques describing the overall liking of the hedonic attributes as a function of associated sensory attributes were also applied. Results from both methods were compared, and it was concluded that chi-square minimization can work as a complementary method to the PLS regression. [source] Glycemic Responses and Sensory Characteristics of Whole Yellow Pea Flour Added to Novel Functional FoodsJOURNAL OF FOOD SCIENCE, Issue 9 2009Christopher P.F. Marinangeli ABSTRACT:, A fundamental understanding regarding postprandial glycemic responses to foods containing whole yellow-pea flour (WYPF) remains unknown. This, alongside concerns that WYPF possesses unfavorable sensory characteristics has limited the incorporation of WYPF into new functional food products as a healthy novel ingredient. The objective of this study was to evaluate how WYPF modulates postprandial glycemic responses as well as sensory characteristics in novel foods. In a single-blind crossover trial, the present study assessed postprandial glycemic responses of banana bread, biscotti, and spaghetti containing either WYPF or whole wheat flour (WWF). Boiled yellow peas (BYP) and white bread (WB) were used as positive and negative controls, respectively. On day 1, subjects evaluated appearance, taste, texture, smell as well as overall acceptance of each WYPF and WWF food on a 5-point hedonic scale. WYPF banana bread (97.9 ± 17.8 mmol·min/L) and biscotti (83 ± 13 mmol·min/L), as well as BYP (112.3 ± 19.9 mmol·min/L), reduced (P,< 0.05) glycemic responses compared to WB (218.1 ± 29.5 mmol·min/L). The glycemic response of WYPF pasta (160.7 ± 19.4 mmol·min/L) was comparable to WB. WYPF biscotti produced a lower (P,= 0.019) postprandial glycemic response compared to WWF biscotti (117.2 ± 13.1 mmol·min/L). Hedonic responses between corresponding foods were similar except for the WYPF pasta (2.9 ± 0.9) which possessed a lower sensory score (P,= 0.02) for smell compared to WWF pasta (3.6 ± 1). WYPF can be used to produce low-glycemic functional foods possessing sensory attributes that are comparable to identical food products containing WWF. [source] HIDDEN AND FALSE "PREFERENCES" ON THE STRUCTURED 9-POINT HEDONIC SCALEJOURNAL OF SENSORY STUDIES, Issue 6 2008XADENI VILLEGAS-RUIZ ABSTRACT An unspecified number of consumers who used a 9-point hedonic scale were frustrated because they could not express preferences for products with the same ratings. Accordingly, consumers were required to rate samples of yogurt on a 9-point structured hedonic scale. Consumers were able to express preference judgments because the testing was performed one-on-one with the experimenter. Thus, it was possible to determine the proportion of consumers who had given the same hedonic response to yogurts but still had preferences for one or other of the stimuli. Further testing, which included a pair of identical yogurts among the stimuli, allowed the proportion of preference and no preference responses elicited by identical stimuli to be determined in this context. Such data are useful as a control condition in preference testing, to assess the proportion of false preferences induced by the experimental conditions. PRACTICAL APPLICATIONS Consumer liking for various products is typically measured using the 9-point structured hedonic scale. Sometimes, consumers will give the same score to two products yet prefer one to the other. Such preferences are not recorded if the consumer is isolated from the experimenter and has no means of reporting them. However, the situation is easily rectified if the experimenter interacts one-on-one with the consumer. Sometimes, false preferences can be obtained from a hedonic scale. This tendency can be monitored by including identical stimuli in the measurements. The present study investigated the extent of such problems so that methods could be devised to address the problem. [source] A COMPARISON OF THE DISCRIMINATING POWER OF ANOVA AND R-INDEX ANALYSES OF HEDONIC DATA FOR VARIOUS PRODUCTS AND EXPERIMENTAL PROTOCOLSJOURNAL OF SENSORY STUDIES, Issue 3 2007HAENA PARK ABSTRACT Consumers rated a set of toothpastes and a set of orange-flavored beverages on a 9-point hedonic scale, using two experimental protocols: Rank-Rating where stimuli could continually be reassessed and a more traditional approach where they could not. A 21-point hedonic scale was also used in the Rank-Rating condition. The hedonic data were analyzed in the usual way using ANOVA with multiple comparisons and also by ranking the data and using an R-index analysis. Regarding the numbers of significant differences recorded, the two analyses were comparable, with a very slight and nonsignificant advantage for the ANOVA analysis. Unlike with intensity scaling, the difference between Rank-Rating and "traditional" scaling was slight but not for all products. The same was true for scale length effects. Differences among the products suggested effects due to the number of attributes that varied. PRACTICAL APPLICATIONS The key finding in this study is the use of Rank-Rating where stimuli could continually be re-assessed for the assessment of hedonic response of consumers in the various products. Rank-Rating may possibly give a better discrimination than more traditional scaling, depending on the number of attributes which varied. The results of the study also recommend the use of R-index analyses of ranked hedonic data in the analyses rather than ANOVA with multiple comparisons. [source] | ||||||||||