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| A Comparative Study of Inorganic Elements in Cultivativing Astragalus Membranaceus From Different Habitats |
| WANG Ling-ling1, 2, 3, WANG Bo1, 2, 3, XIONG Feng1, 2, 3, YANG Lu-cun1, 2, LI Jing-jing4, XIAO Yuan-ming1, 2, 3, ZHOU Guo-ying1, 2* |
1. Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810008, China
2. Key Laboratory of Tibetan Medicine Research, Chinese Academy of Sciences, Xining 810008, China
3. College of Resources and Environment, University of Chinese Academy of Sciences，Beijing 100049, China
4. College of Life Sciences, Qinghai Normal University, Xining 810008, China
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Abstract Mineral elements are closely related to the efficacy of traditional Chinese medicine. They affect the synthesis of secondary metabolites by regulating the activities of various enzymes in the secondary metabolism pathway, which is an indispensable characteristic parameter for the quality control of traditional Chinese medicine. In order to effectively identify the quality differences of Astragalus membranaceus, the characteristic elements of Astragalus membranaceus and their relationship with the region were found out. The species and contents of mineral elements in Astragalus membranaceus from different areas in Qinghai Province were determined by ICP-OES. The data were analyzed by cluster analysis and principal component analysis (PCA) with SPSS 22.0 and R package respectively. 12 kinds of elements in Astragalus membranaceus were determined. The results of PCA showed that Ca, Fe, Li, P, K, Mg, Zn, AL, Na were characteristic elements of Astragalus membranaceus; the clustering analysis results showed that the types and contents of mineral elements in the samples were related to the producing area. When the Euclidean distance was 8, the samples could be clustered into three categories: S12 was clustered into one group, S1, S2, S4, S5, S6, S7, S10, S11, S13, S16 could be clustered into one category, the other samples were in one group. Combining with the distribution map of ArcGIS, the relationship between producing area and its quality can be seen more intuitively. In addition, the distribution characteristics of inorganic elements in Astragalus membranaceus were established. The similarity of the characteristics of inorganic elements in Astragalus membranaceus from different areas was more than 0.996. Therefore, the fingerprint can be used to identify and analyze Astragalus membranaceus from different areas. From the results of PCA of element content, the quality of Astragalus membranaceus produced in S10 (Naka village, Donggou Township, Huzhu county) was the best, the total factor score F was the highest, followed by S7 and S8, which showed that the quality of Astragalus membranaceus in Huzhu county was good. The results show that Astragalus membranaceus is rich in mineral elements, and its content is affected by the place of production. Paying attention to the content and types of elements in the medicinal materials is a supplementary explanation of the pharmacological components and can further reveal the quality from the perspective of elements. The results provide a reference basis for the development and utilization of resources and quality identification of Astragalus membranaceus and provide a guarantee for quality and safety control it.
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Received: 2021-02-24
Accepted: 2021-06-28
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Corresponding Authors:
ZHOU Guo-ying
E-mail: zhougy@nwipb.cas.cn
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[1] Chinese Pharmacopoeia Commission（国家药典委员会）. Drug Standard of Ministry of Public Health of the People’s Republic of China （中华人民共和国卫生部药品标准）. Beijing: People’s Medical Publishing House（北京：人民卫生出版社），2015. 336.
[2] LIU Xiao-fang, ZHANG Shu-rong, GUO Ai-ling, et al（刘晓芳, 张淑蓉, 郭爱玲, 等）. Chinese Traditional Patent Medicine（中成药）, 2017，39(3): 642.
[3] QIN Xue-mei, LI Zhen-yu, SUN Hai-feng, et al（秦雪梅, 李震宇, 孙海峰, 等）. China Journal of Chinese Materia Medica（中国中药杂志）, 2013，38：3234.
[4] LI Xin-ai, DUAN Bao-zhong, YAN Ya（李新爱, 段宝忠, 严 亚）. Journal of Anhui Agricultural Sciences（安徽农业科学）, 2014, 42：12883.
[5] LEI Lian-ti, OUYANG Li, LIU Ya-qiong, et al（雷连娣, 欧阳荔, 刘雅琼, 等）. China Journal of Chinese Materia Medica（中国中药杂志）, 2008，(3): 255.
[6] QIANG Zheng-ze, WANG Yan, XIAO Wen, et al（强正泽, 王 燕, 肖 文, 等）. Journal of Chinese Medicinal Materials（中药材）, 2016, 39(1): 53.
[7] YAN Hui, DUAN Jin-ao, QIAN Da-wei, et al（严 辉，段金廒，钱大玮，等）. Journal of Chinese Medicinal Materials（中药材）, 2011, 4(4): 513.
[8] HU Xiang-fang, LIU Dan, ZHAO Rui, et al（胡香芳, 刘 丹, 赵 睿, 等）. Sichuan Environment（四川环境）, 2007, 4: 36.
[9] WU Xin-xin, SHENG Zhen-hua, WU Qiao-feng（吴新新, 盛振华, 吴巧凤）. Chinese Traditional Patent Medicine（中成药）, 2015，37：370.
[10] ZHANG Zhi-jun, LEI Chao-hai, LI Pei, et al(张治军，雷超海，李 沛，等). Chinese Journal of Pharmaceutical Analysis（药物分析杂志）, 2018, 38: 1500.
[11] GU Wei, SHEN Xiu-yuan, ZHOU Juan-juan, et al（谷 巍, 申修源, 周娟娟, 等）. Journal of Chinese Medicinal Materials（中药材）, 2012, 35：1893.
[12] TIAN Zhu-ping, HE Bang-ping, WANG Xiao-yan, et al（田柱萍, 何邦平, 王小燕, 等）. Studies of Trace Elements and Health（微量元素与健康研究）, 2005, 4：54.
[13] HUANG Jian-hui, CHEN Ling-zhi（黄建辉, 陈灵芝）. Chinese Journal of Plant Ecology（植物生态学与地植物学学报）, 1991, 15(3)：224.
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