Chemometrics in flavor research

Abstract

Chemometrics is playing an increasingly important role in flavor research. Pattern recognition techniques based on multivariate analysis have been most useful in processing data from chromatography and spectrometry mainly due to the intrinsic multi dimensionality of flavor. Multiple regression analysis and its derivatives including partial least squares regression (PLS) have been frequently used for correlating instrumental data to sensory properties. Factor analysis and principal component analysis are widely used for searching latent factors and extracting information as unsupervised pattern recognition. Cluster analysis and discriminant analysis have been successful for classification of samples; however, modeling of samples using SIMCA and nonparametric classification such as KNN have also gained popularity for improving accuracy. Simplex optimization has been well established as a technique in chemometrics, however, it is relatively unknown in flavor research. Computer‐aided optimization has a great potential for application to flavor study, for example, operating conditions, reconstruction, and blending. Fuzzy theory, artificial intelligence, and robotics will become important methodologies in flavor research in the future.