ガーデンクレスジュースの超音波支援持続処理：XGBoost最適化による生理活性化合物と生物学的利用能の向上

This study aimed to improve the functional and nutritional properties of garden cress (Lepidium sativum) juice using ultrasound and optimize process parameters by modeling them with advanced machine learning algorithms. Using a Box-Behnken experimental design, the effects of sonication time (8-16 min) and amplitude (60-100%) on total chlorophyll, total phenolic content (TPC), and ferric reducing antioxidant power (FRAP) were investigated. Nonparametric, high-accuracy estimations were made using the XGBoost algorithm. Optimum conditions were determined to be 12 min and 80% amplitude. Under these conditions, TPC (78.44 mg GAE/mL), FRAP (59.80 mg TE/mL), and chlorophyll (7.15 g/100 mL) values were significantly higher than those in control and pasteurized samples (p < 0.05). HPLC-DAD analysis showed that ultrasound treatment positively impacted the phenolic profile by increasing the release of quercetin, quercetin derivatives, caffeic acid, and chrysin. GC-MS data revealed that volatile aroma compounds (especially 1-hexanol, benzaldehyde, and cinnamaldehyde) were preserved mainly by ultrasound. In vitro digestion simulation showed that total postdigestion recovery rates in ultrasound-treated samples were 34.96% for TPC, 32.50% for chlorophyll, and 28.81% for FRAP, demonstrating a significant increase in bioaccessibility. PCA and hierarchical clustering analyses confirmed a significant biochemical separation of ultrasound-treated samples. The findings indicate that ultrasound technology is a superior method for preserving bioactive compounds, maintaining the aroma profile, and enhancing bioaccessibility compared to heat treatment. This enables data-driven process design. The developed model showed a strong predictive performance under optimal conditions. However, the study is limited by the relatively small data set used for model training.