LAUSR

Abstract ComDim (Common Dimensions) analysis was initially introduced within the context of sensometrics to analyze conventional and free choice sensory profiling data, and more generally multiblock datasets. Thereafter, it has gained some popularity in chemometrics and has been extended in different ways to meet specific needs. Recently, this strategy of analysis has been adapted to the supervised case, under the name of P-ComDim. Going further, we propose herein to extend ComDim to Path-ComDim where the datasets at hand are assumed to have a specific pattern of directed relations among them reflecting, for instance, a chain of influence. The aim of Path-ComDim is to analyze these datasets taking into account the structural connections among them. After a brief review of alternative path modeling approaches, Path-ComDim is detailed encompassing both methodological and algorithmic aspects. In the particular case of a single block to be predicted, it is shown that Path-ComDim is equivalent to P-ComDim analysis. Path-ComDim analysis is illustrated on the basis of a case study involving instrumental, sensory and preference data. Finally, the outcomes are compared to those obtained from alternative path modeling methods.