Application of multivariate linear regression models for selection of deep eutectic solvent for extraction of apigenin and luteolin from Chrysanthemum indicum L.

Abstract

Among a variety of compounds presented in chrysanthemum, apigenin and luteolin are the two main components that play a major role in numerous biological activities of this herb. We aimed to obtain linear models showing the dependence of the yield of extraction of apigenin and luteolin on the composition of deep eutectic solvent and investigate the extraction of these two ingredients from Chrysanthemum indicum L. Two models showing the dependence of luteolin and apigenin concentrations on the composition of the solvent were established using a multilinear regression algorithm and were applied to screen 119 different solvents. After that, the extraction process was optimized using response surface methodology and an artificial neural network. Apigenin and luteolin were recovered from the extract by the combination of distillation and addition of water. The screening results on 119 solvents revealed that choline chloride-acetic acid (1:4) was the most suitable deep eutectic solvent. It was showed that both response surface methodology and the artificial neural network could accurately determine the optimal conditions of extraction of apigenin and luteolin from C. indicum L., including time of extraction (65 minutes), temperature of extraction (90°C) and water content (20%). By the combination of distillation and addition of water, apigenin and luteolin could be effectively recovered from the deep eutectic solvent extract with a recovery rate of over 80%. Deep eutectic solvent could be used as an effective green alternative to the conventional solvents for the extraction of bioactive compounds from plants.