Effects of Process Parameters on the Extraction of Quercetin and Rutin from the Stalks of Euonymus Alatus (Thumb.) Sieb and Predictive Model Based on Least Squares Support Vector Machine Optimized by an Improved Fruit Fly Optimization Algorithm

Abstract

Ultrasonic-assisted extraction (UAE) of quercetin and rutin from the stalks of Euonymus alatus (Thunb.) Sieb in our laboratory, which aimed at evaluating and optimizing the process parameters, was investigated in this work. In addition, process parameters such as ethanol solution concentration, solvent volume/sample ratio, ultrasound power and extraction time, ultrasound frequency and extraction temperature were also first applied for evaluating the influence of extraction of quercetin and rutin. Optimum process parameters obtained were: ethanol solution 60%, extraction time 30 min, solvent volume/sample ratio 40 mL/g, ultrasound power 200 W, extraction temperature 30 °C and ultrasound frequency 80 kHz. Further a hybrid predictive model, which is based on least squares support vector machine (LS-SVM) in combination with improved fruit fly optimization algorithm (IFOA), was first used to predict the UAE process. The established IFOA-LS-SVM model, in which six process parameters and extraction yields of quercetin and rutin were used as input variables and output variables, respectively, successfully predicted the extraction yields of quercetin and rutin with a low error. Moreover, by comparison with SVM, LS-SVM and multiple regression models, IFOA-LS-SVM model has higher accuracy and faster convergence. Results proved that the proposed model is capable of predicting extraction yields of quercetin and rutin in UAE process.