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The aim of this study is to examine the effectiveness of Partial Least Squares Regression (PLSR) method in determing correlations between the aroma profiles and sensory characteristics of wines. A total of 45 volatile compounds in five different Chinese grape wines were identified and quantified by HS-SPME/GC-MS and 26 of them with OAV (odour activity value) 1. All aroma compounds with OAV1 were selected for evaluating the correlations between the aroma profiles and 12 sensory descriptors using PLSR and their ROC (Relative Odour Contribution). The results showed that ethyl decanoate, ethyl hexanoate, acetaldehyde, isoamyl acetate, hexanoic acid, 4-vinylguaiacol and geraniol were the major contributors to the desirable balanced aroma of muscat wine. Ethyl hexanoate, ethyl butyrate, isoamyl acetate, acetaldehyde, hexanoic acid, 3-methyl-1-butanol and octanoic acid were mainly responsible for the aroma of black beet wine and cabernet gernischt wine whereas ethyl tetradecanoate, neryl acetate and nerol were the particular aroma compounds in black beet wine and γ-butyrolactone, nerolidol and β-ionone were special aroma compounds in cabernet gernischt wine. Both PLSR and ROC are effective methods to demonstrate the correlations between the sensory characteristics of the analyzed wines and their aroma compositions.

Correlations between Aroma Profiles and Sensory Characteristics of Red Wines by Using Partial Least Squares Regression Method