マオタイフレーバー白酒の製造技術の正確な識別を可能にする機械学習によるマルチソースデータ融合

The brewing process-particularly the distinctions among Kunsha (KSJ), Suisha (SSJ), and Fansha (FSJ)-critically governs the final quality and flavor characteristics of Maotai-Flavor Baijiu (MFB), underscoring the necessity for robust authentication methodologies. To address this challenge, this study developed an integrated analytical framework combining multi-source flavor profiling with advanced machine learning techniques. A total of 80 samples representing the three processes were comprehensively characterized using electronic nose (e-nose), electronic tongue (e-tongue), GC-FID, and HS-SPME-GC-MS. Significant disparities in volatile profiles and taste attributes were observed, with e-nose sensors exhibiting high sensitivity to nitrogen oxides and e-tongue analysis identifying sourness as a key discriminative attribute. Chromatographic analyses further identified 45 key aroma-active compounds (ROAV ≥ 1). Nine machine learning classifiers were constructed and systematically refined through Bayesian hyperparameter optimization. The CatBoost model demonstrated superior performance, achieving a classification accuracy of 97.0 %. Interpretability analysis via the SHAP framework revealed that compounds such as ethyl acetate and 2,3,5,6-tetramethylpyrazine served as core features driving process discrimination, consistent with established principles of brewing chemistry. These findings indicate that the integration of multi-source data and optimized machine learning offers a robust, data-driven strategy for process traceability and quality control in MFB production.