PLS methodology to study relationships between hedonic judgements and product characteristics

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.

[1]  Jean A. McEwan,et al.  Preference Mapping for Product Optimization , 1996 .

[2]  Ph. Courcoux,et al.  Preference mapping using a latent class vector model , 2001 .

[3]  Svante Wold,et al.  Hierarchical multiblock PLS and PC models for easier model interpretation and as an alternative to variable selection , 1996 .

[4]  H. J. H. MacFie,et al.  Preference mapping in practice , 1994 .

[5]  Evelyne Vigneau,et al.  Segmentation of a panel of consumers using clustering of variables around latent directions of preference , 2001 .

[6]  Roger N. Shepard,et al.  Multidimensional scaling : theory and applications in the behavioral sciences , 1974 .

[7]  Michel Tenenhaus,et al.  PLS path modeling , 2005, Comput. Stat. Data Anal..

[8]  Jérôme Pagès,et al.  Hierarchical Multiple Factor Analysis: application to the comparison of sensory profiles , 2003 .

[9]  H. J. H. MacFie,et al.  Measurement of Food Preferences , 1994 .

[10]  George A. Marcoulides,et al.  Modern methods for business research , 1998 .

[11]  Evelyne Vigneau,et al.  Segmentation of consumers taking account of external data. A clustering of variables approach , 2002 .

[12]  T. Næs,et al.  Multivariate analysis of data in sensory science , 1996 .

[13]  R. Sabatier,et al.  Comparison between linear and nonlinear PLS methods to explain overall liking from sensory characteristics , 1997 .

[14]  Tormod Næs,et al.  Consumer preference mapping of dry fermented lamb sausages , 1997 .

[15]  Michel Tenenhaus,et al.  Multiple factor analysis combined with PLS path modelling. Application to the analysis of relationships between physicochemical variables, sensory profiles and hedonic judgements , 2001 .

[16]  K. Jöreskog A general method for analysis of covariance structures , 1970 .

[17]  Wynne W. Chin The partial least squares approach for structural equation modeling. , 1998 .

[18]  R. Hoyle Statistical Strategies for Small Sample Research , 1999 .

[19]  Wynne W. Chin,et al.  Structural equation modeling analysis with small samples using partial least squares , 1999 .

[20]  Michel Tenenhaus,et al.  L'approche PLS , 1999 .

[21]  Magni Martens,et al.  Multivariate Analysis of Quality : An Introduction , 2001 .

[22]  Karl G. Jöreskog,et al.  Lisrel 8: User's Reference Guide , 1997 .