Abstract
The volatile organic compounds (VOCs) in wines of 'Dornfelder' (DF), 'Petit Verdot' (PV), 'Pinot Noir' (PN), 'Sangiovese' (SV) and 'Malbec' (MB) were analyzed using an E-nose, HS-SPME-GC-MS and HS-GC-IMS. A total of 94 VOCs were identified by two techniques. Specifically, HS-SPME-GC-MS identified 70 compounds (alcohols' concentration accounting for 52.56%-68.75 %), and HS-GC-IMS identified 36 compounds (esters' concentration accounting for 35.58 %-42.05 %), with 12 compounds were identified by both methods. 15 key differential VOCs identified through chemometrics and machine learning analysis. Additionally, correlation analysis of E-nose sensor responses with key differential VOCs indicated that W2S, W2W, and W5S may be more suitable for predicting levels of 2-methylbutyl acetate, 3-methyl-butanoic acid, and isoamyl acetate, which can thus help to quickly identify PV wine. These results help to understand the flavor differences between different varieties of wines and provide a theoretical basis for wine flavor differentiation, quality control and product development.