Identification of Polygonatum odoratum Based on Support Vector Machine
Zhong Li1, Jie Zheng2, Qin Long1, Yi Li1, Huaying Zhou3★, Tasi Liu4, Bin Han1★ Corresponding author
- 1Department of Traditional Chinese Medicine Resources, College of Traditional Chinese Medicine, Guangdong Pharmaceutical University, China.
- 2Department of Pharmaceutical Engineering, College of Chemical Engineering and Light Industry, Guangdong University of Technology, China.
- 3Department of Computer Science, College of Medical Information Engineering, Guangdong Pharmaceutical University,Guangzhou, China.
- 4Department of Traditional Chinese Medicine Resources, College of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, China.
CORRESPONDENCE
Huaying Zhou
Department of Computer Science, College of Medical Information Engineering, Guangdong Pharmaceutical University,Guangzhou, China.
Received: 27-09-2019; Revised: 31-10-2019; Accepted: 21-04-2020.
Volume 16, Issue 71 · pp. 538–542 · PUBLISHED 20 October 2020 · DOI: 10.4103/pm.pm_410_19
ABSTRACT
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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Li, Z., Zheng, J., Long, Q., Li, Y., Zhou, H., Liu, T., & Han, B. (2020). Identification of Polygonatum odoratum Based on Support Vector Machine. Pharmacognosy Magazine, 16(71), 538–542. https://doi.org/10.4103/pm.pm_410_19