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

Abstract

This paper reports on the construction of an efficacious model for a non-invasive identification of traditional Chinese medicines, Liuwei Dihuang pills from different manufacturers, on the basis of near-infrared spectra (NIRS) coupled with moving window partial least-squares discriminant analysis (MWPLSDA). Considering the continuity of near-infrared spectral measurements, MWPLSDA is used to identify continuous and highly classification-related information intervals, a simple, yet effective classification model that can be developed for identifying accurate 150 Liuwei Dihuang pills from five different manufacturers. Meanwhile, the method is compared with some traditional pattern-recognition methods including principal component analysis (PCA), linear discriminant analysis (LDA) and partial least-squares discriminant analysis (PLSDA). The obtained results show that the method not only can reduce the operation time, but also significantly improves the classification accuracy. Hence, the nondestructive method can be expected to be promising for more practical applications on quality control and the discrimination of traditional Chinese medicine.