Back

PM · Lens · Identification of Medicinal Mugua Origin by Near Infrared Spectroscopy Combined with Partial Least‑squares Discriminant Analysis

PDF

Article · Volume 12, Issue 46 · pp. 93–97

Identification of Medicinal Mugua Origin by Near Infrared Spectroscopy Combined with Partial Least‑squares Discriminant Analysis

Bangxing Han1, Huasheng Peng2, Hui Yan3

  1. 1Department of Pharmaceutical Engineering, College of Biological and Pharmaceutical Engineering, West Anhui University, Anhui, Lu’an 237012.
  2. 2Department of Traditional Chinese Medicine Resources, College of Pharmacy, Anhui University of Chinese Medicine, Hefei 230012.
  3. 3Department of Biotechnology, School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212018, China Pharmacogn. Mag. A multifaceted peer reviewed journal in the field of Pharmacognosy and Natural Products.

Abstract

Background: Mugua is a common Chinese herbal medicine. There are three main medicinal origin places in China, Xuancheng City Anhui Province, Qijiang District Chongqing City, Yichang City, Hubei Province, and suitable for food origin places Linyi City Shandong Province.Objective: To construct a qualitative analytical method to identify the origin of medicinal Mugua by near infrared spectroscopy (NIRS).Materials and Methods: Partial least squares discriminant analysis (PLSDA) model was established after the Mugua derived from five different origins were preprocessed by the original spectrum. Moreover, the hierarchical cluster analysis was performed.Results: The result showed that PLSDA model was established. According to the relationship of the origins‑related important score and wavenumber, and K‑mean cluster analysis, the Muguas derived from different origins were effectively identified.Conclusion: NIRS technology can quickly and accurately identify the origin of Mugua, provide a new method and technology for the identification of Chinese medicinal materials.

Keywords: Chinese geoherbs; Mugua; near infrared spectroscopy; origin identification; partial least‑squares discriminant analysis model

The full text of this article is available as a PDF. Open the PDF.