Novel natural deep eutectic solvents as efficient green reagents to extract phenolic compounds from olive leaves and predictive modelling by artificial neural networking

Abstract

Six new NDES (#1–6) were designed to extract polyphenols in olive leaves in comparison to 7 previously reported ones (#7–13) including aqueous 75 % ethanol. Artificial neural networking (ANN) was used to model the extraction parameters and predict the extraction yield. Twelve phenolic compounds were identified and quantified in leaf extract using HPLC-MS. NDES #3 extracted the highest yield of rutin (4.84 mg/g), luteolin 7-O-glucoside (8.36 mg/g), luteolin 4′-O-glucoside (5.42 mg/g), luteolin (23.10 mg/g), apigenin (0.52 mg/g), and chrysoeriol (0.32 mg/g). NDES #4 extracted higher amount of flavones and rutin from the olive leaves than 75 % ethanol. NDES #10 showed selectivity to extracted hydroxytyrosol. The ANN optimization of the extraction parameters using NDES #3 increased the summed yield of flavones and rutin. This study demonstrated that newly designed NDES are more effective than previous ones and 75 % ethanol to extract major phenolic compounds in olive leaves.