Machine learning-assisted identification of core flavor compounds and prediction of core microorganisms in fermentation grains and pit mud during the fermentation process of strong-flavor Baijiu

Jiang Xie, Jiaxin Hong, Chunsheng Zhang, Xin Yuan, Zhigang Zhao, Dongrui Zhao, Shimin Wang, Baoguo Sun, Ran Ao, Jinyuan Sun, Yuling Sun, Mingquan Huang, Xiaotao Sun,
Machine learning-assisted identification of core flavor compounds and prediction of core microorganisms in fermentation grains and pit mud during the fermentation process of strong-flavor Baijiu,
Food Chemistry,
Volume 495, Part 2,
2025,
146426,
ISSN 0308-8146,
https://doi.org/10.1016/j.foodchem.2025.146426.
(https://www.sciencedirect.com/science/article/pii/S0308814625036787)
Abstract: The quality of strong-flavor Baijiu (SFB) is directly determined by key flavor compounds, which are influenced by microorganisms during fermentation. This study employed flavoromics and machine learning technologies to explore the relationship between sensory attributes and flavor compounds in SFB. Initially, sweety aroma and grain aroma were determined as the core sensory attributes impacting the quality grading of SFB. Moreover, key flavor compounds influencing the sweety aroma and grain aroma of SFB were successfully predicted through machine learning classification models. Validation experiments further confirmed the core flavor compounds influencing these sensory attributes. Predictive models revealed that core microorganisms in the fermentation pit, such as Wickerhamomyces anomalus, modulate flavor compounds, thereby affecting the expression of sweety aroma and grain aroma and ultimately determining the quality of SFB. This study demonstrated the potential of machine learning in flavor research of SFB and provided valuable insights into its flavor formation mechanisms.
Keywords: Strong-flavor baijiu; Machine learning; Flavor compounds; Core microorganisms; Sensory characteristics; Quality grade