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RESEARCH ARTICLE
Year : 2009  |  Volume : 5  |  Issue : 20  |  Page : 279-286

Discrimination Of Radix Pseudostellariae According To Geographical Origin By FT-NIR Spectroscopy And Supervised Pattern Recognition


1 School of Pharmacy, Jiangsu Unviersity, 301, xuefuroad, Zhenjiang, 212013; Engineering Technology Research Center of Plant Cell Engineering, Anhui Province, Lu’an, 237012, China
2 Engineering Technology Research Center of Plant Cell Engineering, Anhui Province, Lu’an, 237012, China
3 Xuancheng Jinquan ecological agriculture Co. Ltd. Xuancheng 242000, China

Correspondence Address:
Bang-xing Han
School of Pharmacy, Jiangsu Unviersity, 301, xuefuroad, Zhenjiang, 212013; Engineering Technology Research Center of Plant Cell Engineering, Anhui Province, Lu’an, 237012
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0973-1296.58145

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Radix Pseudostellariae is one of the most popular Traditional Chinese Medicine (TCM) for promoting the immune system, treating asthenia after illnesses with a long history in China and some other Asian countries. Rapid discrimination of R. Pseudostellariae according to geographical origin is crucial to pharmacodynamic action control. FT-NIR spectroscopy and supervised pattern recognition was attempted to discriminate R. Pseudostellariae according to geographical origin in this work. LDA, ANN and SVM were used to construct the discrimination models based on PCA, respectively. The number of PCs and model parameters were optimized by crossvalidation in the constructing model. The performances of three discrimination models were compared. Experimental results showed that the performance of SVM model is the best among three models. The optimal SVM model was achieved when 5 PCs were used, discrimination rates being 100% in the training and 88% in prediction set. The overall results demonstrated that FT-NIR spectroscopy has a high potential to discriminate qualitatively R. Pseudostellariae according to geographical origins by means of an appropriate supervised pattern recognition technique.


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