Optimization of enzyme-ultrasound assisted extraction from mulberries anthocyanins based on response surface methodology and deep neural networks and analysis of in vitro antioxidant activities

This study used Xinjiang native "medicinal and food dual-use" resource mulberries as raw material, and optimized the extraction process of mulberries anthocyanins by enzyme-ultrasound-assistance through the establishment of a response surface model (RSM) and deep neural network model (DNN). A single-factor-Box-Behnken experiment was conducted to investigate the effects of pectinase dosage, enzymatic hydrolysis time, ultrasonic temperature, ultrasonic time, solvent concentration, and solid-liquid ratio on the extraction rates of total anthocyanins (TAC), and cyanidin-3-O-glucoside (C3G), cyanidin-3-O-rutinoside (C3R) two anthocyanin compounds, and the comprehensive evaluation index was used as a reference to obtain the optimal extraction conditions. The results show that both the RSM and DNN models could predict accurately, but by comparing the coefficient of determination (R2) of the two models, it was found that the DNN model (R2 = 0.990 0) has a better predictive effect than the RSM model (R2 = 0.940 4), and the relative error of the DNN model is 0.85%, far lower than the 4.50 % of the RSM model. The predictive accuracy of the DNN model is better than that of the RSM model. It indicates that the DNN model can accurately reflect the experimental results when predicting the extraction process of mulberries anthocyanins. Finally, the optimal extraction process of mulberries anthocyanins components was determined by the DNN model: solid-liquid ratio of 50 mL/g, ethanol concentration of 63 %, ultrasonic temperature of 40 degrees C, pectinase dosage of 0.5 %, and the total anthocyanin content in mulberry could reach 3.16 mg/g under these conditions. The DPPH, ABTS, and & sdot;OH maximum scavenging rates were 80%, 98%, and 54 %, respectively, indicating that mulberries anthocyanins have significant antioxidant capacity. The results of this study provide an effective and sustainable process optimization scheme for extracting anthocyanin components from mulberries.