Use of Partial Least Squares Regression and Multidimensional Scaling on Aroma Models of California Chardonnay Wines

Abstract

The aroma models of California Chardonnay wines were developed and evaluated using multivariate statistical procedures to investigate the sensory significance of odor-active (OA) compounds previously screened by gas chromatography/olfactometry. Partial least squares regression (PLSR) analysis was used to find the relevant combinations of OA compounds, representing aroma properties of wines determined by descriptive analysis. To test the ability of combinations of OA compounds to reproduce the aromas of wines, the four wines with the most different aromas by descriptive analysis were examined. Two combinations of OA compounds determined by PLSR and sensory testing were added to a neutral base wine at the concentrations found in the original wines. By similarity rating, the aromas of the original wine, the base wine, and two spiked aroma models were compared pair-wise. Similarity data were analyzed by multidimensional scaling. For one wine intense in fruit-related aroma attributes, addition of OA compounds produced an aroma more similar to the original wine than to the base wine. For the other three wines, although the spiked aroma models differed from the neutral base wine, none was more similar to the original wine than to the base wine, suggesting the importance of unidentified compounds.