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

= 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.