Machine learning-enhanced modeling and characterization for optimizing dietary Fiber production from Highland barley bran.

This study investigated the modification of highland barley bran through co-fermentation of Lactobacillus bulgaricus and Kluyveromyces marxianus, and developed a dynamic prediction model for DF content under these co-fermentation conditions using machine learning algorithms. The results showed that the XGBoost algorithm could predict changes in the DF component content (R = 0.9553(SDF/IDF), RMSE = 0.0464.) and identify optimal fermentation conditions. Under these optimal conditions, both strains exhibited synergistic effects, where the lactic acid produced by Lactobacillus bulgaricus and β-glucosidase produced by Kluyveromyces marxianus may facilitate IDF decomposition and conversion, resulting in a maximum SDF/IDF ratio of 0.6911. This led to a 27.65 % reduction in IDF content and a 19.11 % increase in SDF content. Moreover, the physicochemical and functional properties of DF were enhanced after co-fermentation. The structure of DF became looser and more porous, its thermal stability improved, and its water-holding, oil-holding, and swelling capacities increased by 53.54 %, 16.11 %, and 44.96 %, respectively, compared with the unfermented counterpart; in terms of adsorption characteristics, its glucose, cholesterol and nitrite adsorption capacities were also significantly improved. According to in vitro gastrointestinal simulated digestion, digestion would have a great impact on the fermented DF, which showed good antioxidant properties during the intestinal digestion stage.