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This work offered a productive technique for resveratrol extraction from Polygonum Cuspidatum (P. Cuspidatum) using ionic liquids in synergy with ultrasound-enzyme-assisted extraction (UEAE). Firstly, ionic liquids with different carbon chains and anions were evaluated. Subsequently, a comprehensive investigation was carried out to evaluate the effect of seven crucial parameters on the resveratrol yield: pH value, enzyme concentration, extraction temperature, extraction time, ultrasonic power, concentration of ionic liquid (IL concentration) and the liquid-solid ratio. Employing the Plackett-Burman Design (PBD), the critical factors were effectively identified. Building upon this foundation, the process was further optimized through the application of Response Surface Methodology (RSM) and an Artificial Neural Network-Genetic Algorithm (ANN-GA). The following criteria were determined to be the ideal extraction conditions: an enzyme concentration of 2.18%, extraction temperature of 58 °C, a liquid-solid ratio of 29 mL/g, pH value of 5.5, extraction time of 30 min, ultrasonic power of 250 W, and extraction solvent of 0.5 mol/L 1-butyl-3-methylimidazolium bromide. Under these conditions, the resveratrol yield was determined to be 2.90 ± 0.15 mg/g. Comparative analysis revealed that the ANN-GA model provided a better fit to the experimental data of resveratrol yield than the RSM model, suggesting superior predictive capabilities of the ANN-GA approach. The introduction of a novel green solvent system in this experiment not only simplifies the extraction process but also enhances safety and feasibility. This research paves the way for innovative approaches to extracting resveratrol from botanical sources, showcasing its significant potential for a wide range of applications.
Zhào et al. (Wed,) studied this question.