PLS methodology to study relationships between hedonic judgements and product characteristics |
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| Affiliation: | 1. HEC School of Management (GREGHEC), 78351 Jouy-en-Josas, France;2. Agrocampus, 65 rue de Saint-Brieuc, CS 84215, 35042 Rennes, France;1. Consumer Science & Health Group, Food & Biobased Research, Wageningen UR, Bornse Weilanden 9, 6708 WG Wageningen, The Netherlands;2. Marketing and Consumer Behaviour Group, Department of Social Sciences, Wageningen University, Hollandseweg 1, 6706 KN Wageningen, The Netherlands;1. Department of Food Science and Technology, College of Engineering, Ewha Womans University, Seoul 03760, South Korea;2. School of Psychology, The University of Auckland, New Zealand;1. Department of Food Science and Technology, College of Engineering, Ewha Womans University, Seoul 03760, South Korea;2. Unilever R&D, Olivier van Noortlaan 120, Vlaardingen, The Netherlands;1. Department of Food Technology, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Viet Nam;2. Vietnam National University, Ho Chi Minh City, Viet Nam;3. Nofima AS, Osloveien 1, P.O. Box 210, N-1431 Ås, Norway;4. The Norwegian University of Life Sciences, Faculty of Chemistry, Biotechnology and Food Science (IKBM), Ås, Norway;5. The Norwegian University of Life Sciences, Faculty of Science and Technology, Ås, Norway;6. Instituto de Agroquimica y Tecnologia de Alimentos, Valencia, Spain;7. University of Copenhagen, Department of Food Science, Denmark;1. Department of Food Science and Technology, College of Engineering, Ewha Womans University, Seoul 03760, South Korea;2. Unilever R&D, Quarry Road East, Bebington, Wirral CH63 3JW, United Kingdom;3. Unilever R&D, Olivier van Noortlaan 120, Vlaardingen, The Netherlands;1. Department of Food Science and Technology, College of Engineering, Ewha Womans University, Seoul 03760, South Korea;2. Unilever R&D, Olivier van Noortlaan 120, Vlaardingen, The Netherlands;3. Unilever R&D, 40 Merritt Blvd, Trumbull, CT, USA |
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| Abstract: | This paper depicts a methodology devoted to a situation where a few products are described by many physico-chemical and sensory characteristics, and are evaluated by consumers on a preference scale. The objective is to relate the block of hedonic variables to the physico-chemical and to the sensory blocks. The analysis of the link between the responses and the predictors using PLS regression allows to cluster the consumers in homogeneous groups with respect to their tastes, and in such a way that their behaviour can be related to the characteristics of the products. For each group, PLS regression allows obtaining a graphical display of the products with their characteristics, and a mapping of the consumers based on their preferences. Moreover, PLS path modelling allows a detailed analysis of each group by building a causal scheme: each block of consumers is related to the physico-chemical and the sensory blocks, and the sensory block is itself related to the physico-chemical block. Finally this PLS path modelling is compared with hierarchical multi-block PLS model. |
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