Fast discrimination of the geographical origins of notoginseng by near-infrared spectroscopy and chemometrics

• Published in: Journal of pharmaceutical and biomedical analysis Vol. 161; pp. 239 - 245
• Main Authors: Chen, Hui, Lin, Zan, Tan, Chao
• Format: Journal Article
• Language: English
• Published: England Elsevier B.V 30.11.2018
• Subjects: Algorithms; Discriminant Analysis; Drugs, Chinese Herbal - analysis; Geography; Models, Statistical; Near-infrared spectroscopy; Notoginseng; Panax notoginseng - chemistry; Partial least squares-discriminant analysis; Principal Component Analysis; Sensitivity and Specificity; Spectroscopy, Near-Infrared - methods; Partial least squares-discriminant analysis; Notoginseng; Near-infrared spectroscopy
• ISSN: 0731-7085; 1873-264X; 1873-264X
• DOI: 10.1016/j.jpba.2018.08.052

•NIR spectroscopy is used for geographical origin detection of notoginseng.•PLSDA and SIMCA serve as the modeling tools and are compared.•Such a procedure is fast and environment-friendly. Notoginseng is a type of highly valued Traditional Chinese medicine (TCM) due to its hemostatic and cardiovascular functions. Notoginseng of Yunnan in China usually commands a premium price and is often the subject of fraudulent practices. The feasibility of combining near-infrared (NIR) spectroscopy with chemometrics was investigated to discriminate notoginseng of different geographical origins. A total of 250 samples of four different provinces in China were collected and divided equally into the training and test sets. Principal component analysis (PCA) was used for observing possible trend of grouping. Two chemometric algorithms including partial least squares-discriminant analysis (PLSDA) and soft independent modeling of class analogy (SIMCA) were used to construct the discriminant models. Standard normal variate (SNV) and first derivative were used for pre-processing spectra. On the independent test set, the PLSDA model outperforms the SIMCA model. When combining both pre-processing methods, the constructed PLSDA model achieved 100% sensitivity and 100% specificity on both the training set and the test set. It indicates that SNV+first derivative pre-processing and PLSDA algorithm can serve as the potential tool of fast discriminating the geographical origins of notoginseng.