Use of multivariate statistics in understanding wine flavor1

Abstract

With the increase in sensitivity and selectivity of instruments, volatiles can now be analyzed under conditions very close to those by which humans perceive aroma. However, without sensory evaluation, even precise information about the volatile composition in the nasal passages cannot predict the flavor of the system as perceived by humans. To illustrate one facet of the complexity of flavor research, multivariate statistical methods that have been used in wine flavor studies to relate sensory descriptive analysis and volatile composition are reviewed. Principal component analysis of instrumental variables is a technique to find the fewest number of volatiles that match the configuration of the sensory profile data. Generalized procrustes analysis (GPA) and Partial least squares regression (PLS) are used to model data from different data sets, such as volatiles identified by GC-MS and intensity ratings for the sensory attributes. The GPA is used to determine if the two data sets “fit each other”, whereas PLS determines underlying dimensions in each set of data that best explain the variation in the other set of data. Neither GPA nor PLS determine if any of the compounds associated with specific sensory attributes are actually responsible for specific aromas but they identify compounds on which the sensory studies should focus. These three methods are compared by the analysis of data from a study of Chardonnay wines that were profiled by descriptive analysis and the headspace volatiles identified by GC- MS, responsible for those aroma notes. However, they do identify compounds on which sensory studies should focus in subsequent research. Dedicated to the memory of Dr. Roy Teranishi