Optimization Study of Extraction Process of Active Components and Antioxidant Activity of Chuanxiong Rhizoma with Natural Low Eutectic Solvent

OBJECTIVE  To optimize the extraction process conditions for the phenolic acid components and their antioxidant activities in Chuanxiong Rhizoma.

METHODS  High-performance liquid chromatography(HPLC) was utilized to screen the extraction solvents for the phenolic acid components of Chuanxiong Rhizoma from 8 natural low eutectic solvents, pure water, and methanol, and then ultrasound-assisted to carry out the relevant extraction experiments. A four-factor, three-level response surface experimental design was carried out using betaine-methylurea(Bet 13) concentration(A), solid-liquid ratio(B), ultrasound time(C), and extraction temperature(D) as process parameters. The composite scores(Y₁) of the extraction rate of five components, ferulic acid, protocatechuic acid, chlorogenic acid, caffeic acid, and vanillic acid in Chuanxiong Rhizoma, as well as the composite scores(Y₂) of the antioxidant activity of the DPPH radical scavenging rate and the ability to inhibit hydroxyl radicals were calculated by entropy weighting, Y₁ and Y₂ were taken as the response values, respectively. The relationship between the extraction process and the response values was established by response surface methodology and genetic neural network, respectively, and then the optimal conditions were obtained by optimization using a genetic algorithm and experimentally verified.

RESULTS  Comparison revealed that using Bet 13 as the extraction solvent has a relative advantage. For Y₁, the R² of the response surface and genetic neural network models were 0.7921 and 0.9442, respectively, and the relative errors were 15.34% and 6.46%, respectively. Genetic neural network modeling yielded optimal process conditions of Bet 13 concentration of 55%, solid-liquid ratio of 96 mg∶1 mL, ultrasonic time of 36 min, and extraction temperature of 36 °C. For Y₂, the R² of the response surface and genetic neural network models were 0.6478 and 0.9516, respectively, and the relative errors were 19.32% and 7.41%, respectively, which gave the optimal process conditions of Bet 13 concentration of 55%, solid-liquid ratio of 71 mg∶1 mL, ultrasonic time of 39 min, and extraction temperature of 38 °C.

CONCLUSION  The fit between the genetic neural network and the experimental data is high, and the extraction process obtained by optimization is stable and reliable, which provides a reference for the industrialized extraction and experimental research of Chuanxiong Rhizoma.