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High-throughput targeted quantification and interpretable machine learning for differentiating Baijiu aroma types

science

The quantitative analysis of volatile flavor compounds in Baijiu presents challenges owing to the extensive variety and differing concentrations of these compounds. This research developed a quantitative method for 249 compounds using gas chromatography flame ionization detection (GC-FID) and gas chromatography tandem mass spectrometry (GC-MS/MS). Chemometrics with an interpretable machine learning model were employed to analyze the flavor profiles of Baijiu. The results indicated that the...

NCBI     Sep 19, 2026