ArticleFood chemistry: X2025
Machine learning combined with GC-FID for discrimination of different categories of maotai-flavor baijiu.
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 8 papers.
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Who cites it
8 citing papers in PubMed.
- Unveiling the Acidic Backbone of Baijiu: A Comprehensive Review on Composition, Metabolic Pathways, and Quality Implications.Comprehensive reviews in food science and food safety · 2026Review
- Decoding the Flavor Structure of Jiang-Flavor Low-Alcohol Base Baijiu: A Machine Learning-Driven Approach to Reveal the Flavor Evolution Patterns and Key Quality Control Nodes.Foods (Basel, Switzerland) · 2026Article
- Elucidating the Impact of High-TemperatureFoods (Basel, Switzerland) · 2026Article
- Innovative Systems Biology in Baijiu Fermentation: Unveiling Omics Landscapes and Microbial Synergy.Foods (Basel, Switzerland) · 2026Review
- Molecular sensory science combined with machine learning for exploring key odor-active compounds and aging-feature compounds of Shanxi aged vinegar.NPJ science of food · 2026Article
- Machine learning assisted intelligent identification strategy for adulteration peanut oil based on fatty acid GC fingerprint.Food chemistry: X · 2026Article
- Construction of microecological discrimination models for high-temperature Daqu from different regions.Food chemistry: X · 2026Article
- Ecological isolation by the Chishui River drives microecology divergence in Daqu.Frontiers in microbiology · 2026Article
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Authors and funding
12 authors.
Funding
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Abstract
Maotai-flavor Baijiu, a traditional Chinese liquor produced via solid-state fermentation, exhibits diverse base Baijiu types due to variations in fermentation rounds, styles, and grades. While crucial for flavor complexity, current manual identification methods hinder blending efficiency and quality control. This study employed GC-FID and machine learning to analyze 410 base Baijiu samples. Decision Tree (74.36 %), XGBoost (92.9 %), and Random Forest (62.3 %) emerged as optimal classifiers for fermentation rounds, typical styles, and Chuntian grades, respectively. SHAP analysis revealed: (1) esters as primary markers for fermentation rounds, (2) ester-trimethylbutanol combinations for grade differentiation, and (3) multi-compound signatures (butyric acid, tetramethylpyrazine, 2-butanol et al.,) for style discrimination. Notably, marker compounds' flavor properties - beyond mere concentration - critically influenced their discriminative power, as evidenced by correlations with nine sensory dimensions.
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Registered trials
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 Socratic graph.