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.
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.
Fu
Hai-Yan
HY
State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
Huan
Shuang-Yan
SY
Xu
Lu
L
Jiang
Jian-Hui
JH
Wu
Hai-Long
HL
Shen
Guo-Li
GL
Yu
Ru-Qin
RQ
D002138:Calibration; D016002:Discriminant Analysis; D004365:Drugs, Chinese Herbal / Q000032:analysis* / Q000145:classification*; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D025341:Principal Component Analysis; D013055:Spectrophotometry, Infrared
D004365:Drugs, Chinese Herbal; C042579:Liuwei Dihuang Decoction