Preference mapping by PO-PLS: Separating common and unique information in several data blocks

Abstract In food development, preference mapping is an important tool for relating product sensory attributes to consumer preferences. The sensory attributes are often divided into several categories, such as visual appearance, smell, taste and texture. This forms a so-called multi-block data set, where each block is a collection of related attributes. The current paper presents a new method for analysing such multi-block data: Parallel Orthogonalised Partial Least Squares regression (PO-PLS). The main objective of PO-PLS is to find common and unique components among several data blocks, and thereby improve interpretation of models. In addition to that, PO-PLS overcomes some challenges from the standard multi-block PLS regression when it comes to scaling and dimensionality of blocks. The method is illustrated by two case studies. One of them is based on a collection of flavoured waters that are characterised by both odour and flavour attributes, forming two blocks of sensory descriptors. A consumer test has also been performed, and PO-PLS is used to create a preference map relating the sensory blocks to consumer liking. The new method is also compared to a preference map created by standard PLS regression. The same is done for the other data set where instrumental data are applied together with sensory data when predicting consumer liking. Here the sensory variables are divided into two blocks: one related to appearance and mouth feel attributes and the other one describing odour and taste properties. In both cases the results clearly illustrate that PO-PLS and PLS regression are equivalent in terms of model fit, but PO-PLS offer some interpretative advantages.