Simultaneous optimization of ultrasonic-assisted extraction of Danshen for maximal tanshinone IIA and salvianolic acid B yields and antioxidant activity: A comparative study of the response surface methodology and artificial neural network

Abstract

Dried roots and rhizomes of Salvia miltiorrhiza Bge. (Danshen in Chinese) are widely used in Chinese herbal medicine. These components from Danshen have significant anti-oxidation properties owing to high levels of tanshinone IIA (TIIA) and salvianolic acid B (Sal B). To make the best use of this natural resource, response surface methodology (RSM) and artificial neural network (ANN) were used for the modeling and optimization of ultrasound-assisted extraction (UAE) of TIIA and Sal B together to determine the antioxidant activity of the extracts obtained from Danshen. In this study, the Box-Behnken design (BBD) was used to improve extraction time (X1), solvent-to-material ratio (X2), extraction temperature (X3), and ethanol concentration (X4) for the optimal combination of the comprehensive yield of TIIA and Sal B (Y1) and the antioxidant activity (Y2). The optimal process parameters were determined to be as follows: extraction time, 73 min; solvent-to-material ratio, 11 mL/g; extraction temperature, 76℃; and ethanol concentration, 80 %. Using these conditions, the predictive optimal combination revealed a comprehensive evaluation value of 16.2281 and an antioxidant activity of 1.1453 mM FeSO4/5 g, while the experimental average values for these parameters were determined to be 16.1826 and 1.1415 mM FeSO4/5 g, respectively. It was clear that the ANN model had higher accuracy in predictive and optimization capabilities, with higher R2 and lower RMSE, MAE, and relative deviations values, than did RSM. Hence, the ANN model proved to be more effective for the analysis and improvement of the extraction process.