Screening and Identification of Ester‐Producing Strains From Fengxiang Taibai Baijiu Fermentation: Machine Learning‐Assisted Evaluation Under Ethanol‐Acetic Acid Co‐Stress

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ABSTRACT Fengxiang Taibai baijiu (FTB) fermentation involves complex microbial interactions that critically influence aroma formation; however, the efficient selection of high ester‐producing yeasts and the prediction of their fermentation performance remain challenging. In this study, yeasts were systematically isolated from different stages of FTB fermentation and evaluated using a combination of fermentation experiments and machine learning approaches. A total of 156 strains were obtained, among which seven Wickerhamomyces anomalus strains exhibited high ester‐producing capacity (> 1.15 g/L). Among them, strain F12D‐38 demonstrated the strongest tolerance and aroma‐producing potential under ethanol–acetic acid co‐stress conditions. An XGBoost model achieved high predictive accuracy ( R 2 = 0.89) and was applied to guide strain screening and process optimization, identifying an optimal inoculation range of 14%–16% (v/v). Fermentation validation showed that five strains outperformed the traditional control, with F12D‐38 achieving an approximately 15.0% increase in total volatile compounds. Overall, this study demonstrates that integrating machine learning with experimental validation enables efficient strain selection and fermentation optimization, providing a practical strategy for improving aroma quality and process control in FTB production.

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