[Machine learning-optimized solid-state fermentation of non-grain biomass for enhanced feed protein production].

) of 0.84 and a root mean square error (RMSE) of 0.98. Using the trained CatBoost model, we evaluated 8 225 290 fermentation condition combinations by setting stepwise variations for eight key process variables. The model predicted maximum true protein yields for three specific substrate-microbial consortium combinations that exceeded the highest experimental values recorded in our original dataset. We then conducted experimental validation under the model-optimized conditions for the combination of ammonia-pretreated wheat straw and an acclimated microbial consortium. This experiment achieved a true protein yield of 16.51%, which represented a 18.43% improvement over that of the pre-optimization level. Furthermore, the optimized process reduced fermentation time by 108 h and nitrogen source input by 40%, indicating significant potential for cost reduction. This study demonstrates that machine learning can effectively improve the optimization efficiency of solid-state fermentation for feed protein production, thereby laying solid theoretical and practical foundations for developing non-grain biomass resources and alleviating the feed protein shortage in China.