Authentication of Dendrobium Species Using Near-Infrared and Ultraviolet-Visible Spectroscopy with Chemometrics and Data Fusion
文献类型： 外文期刊
作者： Wang, Ye 1 ; Zuo, Zhi-Tian 2 ; Shen, Tao 3 ; Huang, Heng-Yu 1 ; Wang, Yuan-Zhong 2 ;
作者机构： 1.Yunnan Univ Tradit Chinese Med, Coll Tradit Chinese Med, 1076 Yuhua Rd, Kunming 650500, Yunnan, Peoples R China
2.Yunnan Acad Agr Sci, Inst Med Plants, 2238 Beijing Rd, Kunming 650200, Yunnan, Peoples R China
3.Yuxi Normal Univ, Coll Resources & Environm, Yuxi, Peoples R China
关键词： Dendrobium plants; k-nearest neighbor model; near-infrared (NIR) spectroscopy; recognition methods; ultraviolet-visible spectroscopy
期刊名称：ANALYTICAL LETTERS （ 影响因子：2.329； 五年影响因子：1.738 ）
ISSN： 0003-2719
年卷期： 2018 年 51 卷 17 期
页码：
收录情况： SCI
摘要： Herbal products produced from multiple plants have special characteristics in the clinical practice of traditional Chinese medicine. These traits provide the opportunity for fraudulent merchants to mix other herbal products similar in appearance into authentic herbal medicine. Shihu is a tonic herbal medicine from the Dendrobium plants with complex botanical origins. In this context, 11 Dendrobium plants including 109 individuals from China were collected for authentication work. Nine species have been described as herbal medicines in the literature while D. hookerianum and D. xichouense are not reported to have medicinal benefits. A key feature of this study was that multiple recognition approaches, based on near-infrared and ultraviolet-visible spectra as well as their combination, were compared to investigate their classification performance. Intuitively, score plots using principal component analysis and hierarchical cluster diagrams were used to evaluate the genetic relationships among these species. Compared with support vector machine discrimination analysis and k-nearest neighbor models, the partial least square discrimination analysis model combined with low-level data fusion provided excellent performance for authentication and was the most robust model with 100% accuracy rates for the training and prediction sets. The results indicated that near-infrared and ultraviolet-visible spectra and their fusion dataset combined with supervised recognition analysis are effective and therefore recommended for the authentication of genuine and sham of herbal Shihu species.
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