[SIC algorithm based model updating for near infrared analysis of Salvia miltiorrhiza alcohol extraction process]

The near-infrared (NIR) spectroscopy for offline monitoring of alcohol extraction process of Salvia miltiorrhiza was investigated, with high performance liquid chromatography (HPLC) determination of value for reference. The partial least squares method was adopted to establish the tanshinone ⅡA quantitative calibration model, so as to detect extraction process of Salvia miltiorrhiza. Because the differences between batches of raw materials may endanger the robustness of the original model, the simple interval calculation (SIC) was applied in updating the near-infrared quantitative model for traditional Chinese medicine extraction process for the first time, and compared with Random Selection (RS) method. SIC's final updating results showed that root mean square with cross validation (RMSECV), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) of tanshinone ⅡA were 0.006 8 g•L⁻¹, 0.005 4 g•L⁻¹ and 3.14, respectively; but RS' final updating results showed that RMSECV, RMSEP and RPD were 0.006 4 g•L⁻¹, 0.006 8 g•L⁻¹ and 2.50, respectively. This study suggested that SIC is superior to RS, and provided a research foundation for quality control and monitoring of S. miltiorrhiza extraction process in the future.