Ecofriendly Extraction of Polyphenols from Ampelopsis grossedentata Leaves Coupled with Response Surface Methodology and Artificial Neural Network-Genetic Algorithm

Polyphenols as second major components in the leaves of Ampelopsis grossedentata (AGPL) exhibit excellent antioxidant properties. This study aimed to optimize a novel deep eutectic solvents (DESs)-assisted extraction process for AGPL with response surface methodology (RSM) and genetic algorithm-artificial neural network (GA-ANN). Under the participation of ultrasonic waves, L-carnitine-1,4-butanediol system was selected for the phenolics extraction process. The ideal conditions for AGPL extraction were as following: liquid to solid ratio of 35.5 mL/g, ultrasonic power of 697 W and extraction duration of 46 min. Under those conditions, the actual AGPL yield was 15.32% ± 0.12%. The statistical analysis showed that both of models could predict AGPL yield well and GA-ANN had a relatively higher accuracy in the prediction of AGPL output, supported by coefficient of determination (R2=0.9809) in GA-based ANN compared to R2=0.9145 in RSM, as well as lower values for mean squared error (MSE=0.0279), root mean squared error (RMSE=0.1669) and absolute average deviation (AAD=0.1336) in GA-ANN model. Moreover, the extracted polyphenols were determined by HPLC-MS to have 23 phenolic compounds correspondingly by some bioactive acids such as nonadecanoic acid and neochlorogenic acid. The in vitro ORAC assay revealed that Carn-Bu4 assisted AGPL extract exhibited a notable antioxidant capacity of 275.3 ± 0.64 μmol TE/g. Our results provided a green extraction method to accumulate the polyphenols in the leaves of Ampelopsis grossedentata with more accurate ANN model.