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

Dan Wang, Yan Chen, Xinyu Ma, Xiaobing Zhang, Ji Zhang, Siqian Guo, Jingming Li, Liping Xiang

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Article in Food chemistry: X, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1 citing paper in PubMed.

Characterizing Shared Sensory Priorities and Age-Group Differences for Feng-Flavor Baijiu: A Consumer Survey Using Hierarchical Weighting and Interpretable Machine Learning.Foods (Basel, Switzerland) · 2026
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8 authors.

Dan WangCollege of Food Science & Nutritional Engineering, China Agricultural University, No. 17 Tsinghua Dong Road, 100083 Beijing, China.

Yan ChenZunyi Product Quality Inspection and Testing Institute, No. 126, Shanghai Road, Huichuan District, Zunyi 563006, China.

Xinyu MaCollege of Food Science & Nutritional Engineering, China Agricultural University, No. 17 Tsinghua Dong Road, 100083 Beijing, China.

Xiaobing ZhangKweichow Zhen Distillery Co.Ltd., Wuxing Village, Huichuan District, Zunyi 563006, China.

Ji ZhangZunyi Product Quality Inspection and Testing Institute, No. 126, Shanghai Road, Huichuan District, Zunyi 563006, China.

Siqian GuoCollege of Food Science & Nutritional Engineering, China Agricultural University, No. 17 Tsinghua Dong Road, 100083 Beijing, China.

Jingming LiCollege of Food Science & Nutritional Engineering, China Agricultural University, No. 17 Tsinghua Dong Road, 100083 Beijing, China.

Liping XiangZunyi Product Quality Inspection and Testing Institute, No. 126, Shanghai Road, Huichuan District, Zunyi 563006, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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).

Indexed as

Age discriminationData fusionMachine learningSauce-flavor Baijiu

Identifiers

PMID41278096

DOI10.1016/j.fochx.2025.102986

PMCPMC12640077

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.