Rapid and Simultaneous Determination of Three Active Components in Raw and Processed Root Samples of Scutellaria baicalensis by Near-infrared Spectroscopy.

Abstract

Motivated by the wide use of Scutellariae Radix (SR) in the food and pharmaceutical industries, a rapid and non-destructive near-infrared spectroscopy (NIRS) method was developed for the simultaneous analysis of three main active components in raw SR and SR processed by stir-frying with wine. From seven geographical areas, 58 samples were collected. The reference contents for the SR components baicalin, baicalein, and wogonin were determined by high-performance liquid chromatography. Two multivariate analysis methods, partial least-squares (PLS) regression as a linear regression method and artificial neural networks (ANN) as a nonlinear regression method, were applied to the NIR data, and their results were compared. In the PLS model, different model parameters (i.e., 11 spectral pre-treatment methods), spectral region, and latent variables were investigated to optimize the calibration model; additionally, the ANN model was applied with five different spectral pre-treatment methods and six algorithms. For the optimal model parameters, the correlation coefficients of the calibration set for baicalin, baicalein, and wogonin were 0.9979, 0.9786, and 0.9773, respectively; the correlation coefficients of the prediction set were 0.9756, 0.9843, and 0.9592, respectively; the root mean square error of validation values were 0.215, 0.321, and 0.174, respectively. The optimal NIR models were then employed to analyze the effects of processing and geographical regions on analyte contents. The established NIR methods were robust, accurate, and reproducible. NIRS may be a promising approach for the routine screening and quality control of traditional Chinese medicines.