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ORIGINAL ARTICLE
Year : 2020  |  Volume : 16  |  Issue : 71  |  Page : 538-542

Identification of Polygonatum odoratum based on support vector machine


1 Department of Traditional Chinese Medicine Resources, College of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou, China
2 Department of Pharmaceutical Engineering, College of Chemical Engineering and Light Industry, Guangdong University of Technology, Guangzhou, China
3 Department of Computer Science, College of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, China
4 Department of Traditional Chinese Medicine Resources, College of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, China

Correspondence Address:
Huaying Zhou
College of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou,510006
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/pm.pm_410_19

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Background: The dried rhizome of Polygonatum odoratum (Mill.) Druce has been widely used in traditional medicinal preparations in China, Japan, and Korea. In China, it is distributed in Hunan, Guangdong, and Liaoning provinces, and its quality differs from habitat to habitat. In addition, P. odoratumhas some adulterants, such as Polygonatum inflatumKom, Polygonatum prattii Baker, and Polygonatum cyrtonema Hua. The morphological traits and chemical composition of the aforementioned adulterants have many similarities with those of P. odoratum. Therefore, it is possible that people often use adulterants instead of P. odoratum for clinical treatment. Objectives: We aimed to establish a reliable and accurate classification model of P. odoratum based on the support vector machine (SVM) and identify it from different habitats; we also aimed to identify its adulterants. Materials and Methods: In this study, we first determined the ultraviolet (UV) absorption spectrum of the water extract of the rhizome from 162 samples (including P. odoratum from Hunan, Guangdong, Heilongjiang, Yunnan, and Liaoning Provinces and adulterant species including P. inflatum, P. prattii,P. cyrtonema, and Disporopsis pernyi (Hua) Diels) by UV-visible spectrophotometry. The UV absorption data were preprocessed with the SVM model before establishing the habitat and other details. Results: According to our results, the SVM model showed a prediction accuracy of 100%. The model accurately identified five different habitats and four different adulterants of P. odoratum. Pretreatment of samples with UV spectrum might be useful in the accurate identification of P. odoratum. Conclusion: The SVM model seems very prospective in identifying herbs with multiple habitats and its adulterants.


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