AbstractAuthentication of traditional Chinese medicines (TCMs) has become important because they can be adulterated with relatively cheap herbal medicines similar in appearance. Detection of such adulterated samples is needed because their presence is likely to reduce the pharmacological potency of the original TCM and, in the worst cases, the samples may be harmful. The aim of this study was to develop a rapid near-infrared spectroscopy (NIRS) analytical method which was supported by multi-variate calibration, e.g. partial least squares regression (PLSR) and radial basis function artificial neural networks (RBF-ANN), in order to quantify the TCM and the adulterants. In this work, Cynanchum stauntonii (CS), a commonly used TCM, in mixtures with one or two adulterants — two morphological types of TCM, Cynanchum atrati (CA) and Cynanchum paniculati (CP), were determined using NIR reflectance spectroscopy. The three sample sets, CS adulterated with CA or CP, and CS with both CA and CP, were measured in the range of 800–2500 nm. Both PLSR and RBF-ANN calibration models provided satisfactory results, even at an adulteration level of 5 mass %, but the RBF-ANN models with better root mean square error of prediction (RMSEP) values for CS, CA, and CP arguably performed better. Consequently, this work demonstrates that the NIR method of sampling complex mixtures of similar substances such as CS adulterated by CA and/or CP is capable of producing data suitable for the quantitative analysis of mixtures consisting of the original TCM adulterated by one or two similar substances, provided the spectral data are interrogated by multi-variate methods of data analysis such as PLS or RBF-ANN.
Construction of an Efficacious Model for a Nondestructive Identification of Traditional Chinese Medicines Liuwei Dihuang Pills from Different Manufacturers Using Near-infrared Spectroscopy and Moving Window Partial Least-squares Discriminant Analysis
