Application of Multivariate Statistical Analysis and Machine Learning to Sensory Data Analysis

Abstract

This chapter discusses the application of multivariate techniques and machine learning (ML) to the analysis of data originating from the sensory analysis of dairy products. The main multivariate techniques used in the analysis of sensory data concerning dairy products are principal component analysis (PCA), correspondence analysis (CA), hierarchical cluster analysis (HCA), generalised Procrustes analysis (GPA) and multiple-factor analysis (MFA), allowing us to determine the drivers of liking, characterise the sensory profile, determine sensory acceptance and observe relationships between emotional profiles and sensory acceptance. These techniques can be used to segment consumers or products based on overall liking, type of processing or intra-cultural differences. Comparisons between different sensory methodologies could be made. ML can evaluate the interaction of physical and chemical characteristics with sensory acceptance and predict consumer responses based on descriptive tests. It is essential to observe the peculiarities and limitations of each multivariate statistical techniques or ML, the characteristics of the data set and the objectives of the study, to choose the appropriate analysis to be applied.