Optimization of Ultrasonic-Assisted Extraction Conditions for Bioactive Components and Antioxidant Activity of <i>Poria cocos</i> (Schw.) Wolf by an RSM-ANN-GA Hybrid Approach

Abstract

In this study, a response surface methodology and an artificial neural network coupled with a genetic algorithm (RSM-ANN-GA) was used to predict and estimate the optimized ultrasonic-assisted extraction conditions of Poria cocos. The ingredient yield and antioxidant potential were determined with different independent variables of ethanol concentration (X1; 25–75%), extraction time (X2; 30–50 min), and extraction solution volume (mL) (X3; 20–60 mL). The optimal conditions were predicted by the RSM-ANN-GA model to be 55.53% ethanol concentration for 48.64 min in 60.00 mL solvent for four triterpenoid acids, and 40.49% ethanol concentration for 30.25 min in 20.00 mL solvent for antioxidant activity and total polysaccharide and phenolic contents. The evaluation of the two modeling strategies showed that RSM-ANN-GA provided better predictability and greater accuracy than the response surface methodology for ultrasonic-assisted extraction of P. cocos. These findings provided guidance on efficient extraction of P. cocos and a feasible analysis/modeling optimization process for the extraction of natural products.

Keywords

• <i>Poria cocos</i> (Schw.) wolf
• artificial neural network
• genetic algorithm
• optimization
• antioxidant