Modeling extraction process of Salvia miltiorrhiza based on multi-source information fusion technology / 中草药

ABSTRACT

Objective: The potency of multi-source information fusion technology was explored to improve the model calibration and prediction performance of Chinese medicine extraction process. Methods: The ethanol extraction process for isolating fat-soluble components from Salvia miltiorrhiza was taken as the research carrier. S. miltiorrhiza from different sources were collected to simulate the fluctuation of materials. The changes of process parameters were simulated by design of experimental (DOE), and the process near infrared spectra (NIRS) were used as the process state variables. The contents of tanshinone IIA, cryptotanshinone, and tanshinone I were determined by HPLC. The raw material properties, process parameters and process state variables were combined as independent variables. The content of effective components in the extract was taken as the dependent variable. The partial least squares (PLS) algorithm was used to establish the quality prediction model of the extracts. Results: The modeling results respectively showed that the RMSECV was 0.172 8 mg/g, RMSEP was 0.031 7 mg/g, RPD was 6.91 (tanshinone IIA); RMSECV was 0.153 4 mg/g, RMSEP was 0.024 2 mg/g, RPD was 4.02 (cryptotanshinone); RMSECV was 0.117 1 mg/g, RMSEP was 0.043 2 mg/g, RPD was 4.76 (tanshinone I). Conclusion: The calibration and prediction performance of multi-source information fusion model are better than the conventional model, which can effectively improve the quality predictability and controllability of S. miltiorrhiza extract.