Optimization of the extraction process of total steroids from Phillinus gilvus (Schwein.) Pat. by artificial neural network (ANN)-response surface methodology and identification of extract constituents.

Abstract

Phillinus gilvus (Schwein.) Pat has pharmacological effects such as tonifying the spleen, dispelling dampness, and strengthening the stomach, in which sterol is one of the main compounds of P. gilvus, but there has not been thought you to its extraction and detailed identification of its composition, in the present study, we used artificial neural network (ANN) and response surface methodology (RSM) to optimize the conditions of ultrasonic-assisted extraction, and the parameters of the independent and interaction effects were evaluated. Ultra performance liquid chromatography-quadrupole-time of flight mass spectrometry (UPLC-Q-TOF-MS/MS) was used to identify the major components in the purified extract. The results showed that the optimal extraction process conditions were: ultrasonic time 96 min, ultrasonic power 140 W, liquid to material ratio 1:25 g/ml, and ultrasonic temperature 30.7 °C. The compliance rates of the predicted and experimental values for the artificial neural network model and the response surface model were 98.3% and 96.12%, respectively, indicating that both models have the potential to be used for optimizing the extraction process of P. gilvus in industry. A total of 120 compounds and 30 major steroids were identified by comparison with the reference compounds. Among the major steroidal components are these findings will contribute to the isolation and utilization of active ingredients in P. gilvus.