A fusion strategy of multivariate flavor analysis techniques for the discrimination of aged sauce-flavor Baijiu

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In the present work, effective methods for determining the age of sauce-flavor Baijiu by multivariate data analysis and machine learning techniques were explored. Considering the complex and dynamic flavor changes during Baijiu storage, four analytical techniques, including gas chromatography–mass spectrometry (GC–MS), gas chromatography-ion mobility spectrometry (GC-IMS), electronic nose (E-nose) and electronic tongue (E-tongue) were integrated, to build a multilayered flavor profile of Baijiu. Four types of classification models were further constructed. The fusion data strategy combined with oversampling method of synthetic minority over-sampling technique (SMOTE) and neural network, significantly enhance the accuracy (0.96) and precision (0.97) of aged Baijiu determination (ranged from 1 year to 30 years). A total of 28 important features were screened out, including furfural, 2-hexanol (GC–MS), Area 65 (GC-IMS), and bitterness (E-tongue). Furthermore, potential correlations among different data sources were discussed. The astringency (E-tongue) showed a positive correlation with ethyl lactate (GC–MS) and Area 40 (GC-IMS). • Integrated strategy of data-fusion combines neural network discriminated aged Baijiu. • A total of 155 characteristics were acquired by the four flavor analysis techniques. • Four flavor analysis techniques screened out 28 key characteristics. • Stringency showed a positive correlation with ethyl lactate and Area 40.