Understanding methods for internal and external preference mapping and clustering in sensory analysis

Doctor of PhilosophyDepartment of Human NutritionEdgar Chambers IVPreference mapping is a method that provides product development directions for developers to see a whole picture of products, liking and relevant descriptors in a target market. Many statistical methods and commercial statistical software programs offering preference mapping analyses are available to researchers. Because of numerous available options, there are two questions addressed in this research that most scientists must answer before choosing a method of analysis: 1) are the different methods providing the same interpretation, co-ordinate values and object orientation; and 2) which method and program should be used with the data provided? This research used data from paint, milk and fragrance studies, representing complexity from lesser to higher. The techniques used are principal component analysis, multidimensional preference map (MDPREF), modified preference map (PREFMAP), canonical variate analysis, generalized procrustes analysis and partial least square regression utilizing statistical software programs of SAS, Unscrambler, Senstools and XLSTAT. Moreover, the homogeneousness of consumer data were investigated through hierarchical cluster analysis (McQuitty’s similarity analysis, median, single linkage, complete linkage, average linkage, and Ward’s method), partitional algorithm (k-means method), nonparametric method versus four manual clustering groups (strict, strict-liking-only, loose, loose-liking-only segments). The manual clusters were extracted according to the most frequently rated highest for best liked and least liked products on hedonic ratings. Furthermore, impacts of plotting preference maps for individual clusters were explored with and without the use of an overall mean liking vector. Results illustrated various statistical software programs were not similar in their oriented and co-ordinate values, even when using the same preference method. Also, if data were not highly homogenous, interpretation could be different. Most computer cluster analyses did not segment consumers relevant to their preferences and did not yield as homogenous clusters as manual clustering. The interpretation of preference maps created by the highest homogeneous clusters had little improvement when applied to complicated data. Researchers should look at key findings from univariate data in descriptive sensory studies to obtain accurate interpretations and suggestions from the maps, especially for external preference mapping. When researchers make recommendations based on an external map alone for complicated data, preference maps may be overused

An initial lexicon of sensory properties for nail polish

OBJECTIVE: The objective of this study was to develop an initial lexicon for sensory properties of nail polish and to validate this lexicon using a descriptive analysis study of selected samples. METHODS: Seventeen commercial products from four categories (regular, flake-containing, water-based and gel) were used in this study. Descriptive sensory analysis was conducted in this study to characterize and evaluate application and removal properties of these nail polishes. Data was then processed by Analysis of Variance (ANOVA), Principal Component Analysis (PCA) and Pearson’s Correlation Coefficient analysis to explore the differences among samples and attributes. RESULTS: A lexicon of twenty-one sensory attributes was developed to describe the application of nail polish. It included three initial texture attributes, thirteen initial appearance attributes and five aroma attributes. A lexicon of five attributes in five stages was developed to describe the removal of nail polish. The results from ANOVA and PCA showed that attributes in the lexicon separated the different product categories. CONCLUSION: The results of this study indicated that descriptive sensory analysis can be used to evaluate nail polish. The results of this study present scientists who are working on nail polish an additional tool to describe application and removal properties of nail polish

Associations of volatile compounds with sensory aroma and flavor: the complex nature of flavor

Attempts to relate sensory analysis data to specific chemicals such as volatile compounds have been frequent. Often these associations are difficult to interpret or are weak in nature. Although some difficulties may relate to the methods used, the difficulties also result from the complex nature of flavor. For example, there are multiple volatiles responsible for a flavor sensation, combinations of volatiles yield different flavors than those expected from individual compounds, and the differences in perception of volatiles in different matrices. This review identifies some of the reasons sensory analysis and instrumental measurements result in poor associations and suggests issues that need to be addressed in future research for better understanding of the relationships of flavor/aroma phenomena and chemical composition

Perception of naturalness in textiles

In many daily contexts, we prefer natural ‘materials’ over un-natural ones. Textiles embodied in garments that are worn on the body all day, or in bed sheets slept under every night touch us literally, on a daily basis. Hence among all other materials, ‘naturalness perception’ has a strong impact on the preference for textile products. Nevertheless, a stark gap can be found in literature articulating when people appraise textiles as natural. Grounding on previously conducted studies on textile perception, we present an empirical study in which we determined three main aspects which might influence the perception of naturalness in textiles: (1) fiber origin, what it is actually made of (natural vs. artificial, or mixed), (2) yarn type (fine vs. thick yarn), (3) exploration mode, i.e. how people interact with textiles (e.g. touch only, vision only, both). The results show that pure wool and pure cotton textiles are perceived most natural. While mixing wool and cotton with polypropylene destroys the perception of naturalness, mixing in acrylic does not. Moreover, a thick yarn is perceived as most natural. No differences were found for exploration modality. We discuss our results in the light of design in textiles.This project was supported by the EU-NEST programme, MONAT (Measurement Of NATuralness, contract number 029000). KO was additionally supported by a personal FWO postdoctoral grant (1.2.610.11.N.00) and an Open Technology Grant of the Dutch Technology Foundation STW, which is part of the Netherlands Organisation for Scientific Research (NWO), and which is partly funded by the Ministry of Economic Affairs, awarded to Jeroen Smeets and Katinka van der Kooij. SS-F was supported by MINECOPSI2013-42626-P) and AGAUR (2014SGR856)