PLS discriminant analysis applied to conventional sensory profiling data

Several methods have been proposed in the literature to analyze conventional sensory profiling data. We focus on factor analytical methods which have been extensively used due to their ability to produce graphical displays which are both useful and easy to interpret. Available factor analytical methods include principal components analysis on averaged assessors data or on the data matrix obtained by stacking the assessors’ datasets one on top of the others, and canonical variates analysis; each method has advantages and drawbacks. As an alternative it is advocated the use of PLS discriminant analysis, which is at the intersection of the methods mentioned above. This method of analysis takes account of the within and between product variations while minimizing the impact of multicolinearity. It provides statistical tools to assess on the one hand the agreement among assessors and the discrimination among products by means of the between to total variance ratio, and on the other hand the relative importance of variables by means of VIP (variable importance in the projection) indices. The VIP indices may also be useful to guide the selection of a subset of relevant attributes from the complete set of attributes. In this paper, PLS discriminant analysis is compared with other methods, and results are illustrated through a case study. In particular, the stability of the various methods is investigated using assessors’ re-sampling (bootstrap) and confidence ellipses.

Research highlights
► PLS discriminant analysis is used to analyze conventional sensory profiling data.
► Its outcomes are compared to those of alternative methods.
► Thereafter, VIP indices are used to select a subset of relevant variables.