Machine learning-guided green ultrasound processing enhances polyphenols, antioxidant capacity, and in vitro...

Black fig vinegar is a functional fermented product rich in phenolic compounds and antioxidants; however, conventional thermal pasteurization may reduce its bioactive composition and functional quality. Although ultrasound processing has emerged as a promising non-thermal alternative, its optimization, predictive modeling, and influence on the in vitro bioaccessibility of black fig vinegar have not yet been comprehensively investigated. Therefore, this study aimed to optimize ultrasound processing conditions and evaluate their effects on phenolic composition, antioxidant capacity, and in vitro bioaccessibility of traditionally produced black fig vinegar using response surface methodology (RSM) integrated with ensemble machine learning algorithms. Untreated, pasteurized, and ultrasound-treated vinegars were compared, while ultrasound processing variables (processing time and amplitude) were optimized through RSM. The experimental dataset was subsequently expanded using data augmentation, and Extra Trees, CatBoost, Gradient Boosting, and AdaBoost models were developed to predict total phenolic content (TPC) and ferric reducing antioxidant power (FRAP). Among the tested algorithms, the Extra Trees model achieved the best predictive performance, with coefficients of determination (R²) exceeding 0.998 and prediction accuracies above 99%. Under the optimized ultrasound conditions (8 min and 58% amplitude), TPC (79.77 ± 3.69 mg GAE/100 mL), FRAP (8.87 ± 0.24 mmol TE/L), and total flavonoid content were significantly higher than those of untreated and pasteurized samples (p < 0.05). Ultrasound processing also significantly increased caffeic acid, catechin hydrate, chrysin, vanillin, p-coumaric acid, o-coumaric acid, and trans-ferulic acid concentrations. During simulated gastrointestinal digestion, all samples exhibited reductions in bioactive compounds; however, ultrasound-treated vinegar consistently retained higher TPC, total flavonoid content, and FRAP values, with bioaccessibility remaining approximately 30%-32%. Pearson correlation and principal component analyses further identified caffeic acid, trans-ferulic acid, rutin, and o-coumaric acid as the principal contributors to antioxidant capacity. This study integrates ultrasound optimization, RSM, data enhancement, community machine learning, phenolic profiling, and in vitro bioavailability assessment for black fig vinegar, presenting a robust and sustainable strategy for process optimization and the development of high-quality functional vinegar products.