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基于ICP-MS对不同产地小根蒜无机元素的主成分分析和聚类分析 被引量:19

Principal component analysis and cluster analysis of inorganic elements based on ICP-MS in Allium macrostemon from different areas
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摘要 目的:研究小根蒜中无机元素的含量及其分布特征,为中药材薤白的品质评价和质量控制提供实验依据。方法:采用电感耦合等离子体质谱(ICP-MS)法,对10个小根蒜样品中14个无机元素的含量进行全定量分析,从元素组学的角度建立其无机元素特征谱,并采用SPSS聚类分析和主成分分析对其特征元素进行评价。结果:主成分分析选出6个主因子,得出小根蒜的特征元素为Cu、Zn、Fe、Mn、Ni、Cr、Se;聚类分析将10个小根蒜样品聚为2大类,不同产地小根蒜体内无机元素的含量与气温、地理位置等生态环境存在着一定的相关性。结论:全定量分析法可用于小根蒜无机元素的含量测定;主成分分析法和聚类分析法是小根蒜无机元素含量分析的有效方法。 Objective: To study the character of the contents and distribution of inorganic elements of bulbs of Allium macrostemon Bge. and to provide valuable experimental evidence for the quality evaluation and control of Allii Macrostemonis Bulbus. Methods :The contents of 14 inorganic elements in ten samples of A. macrostemon were determined by the means of ICP - MS with the result that feature prints of inorganic elements were established from the perspective elements of group and the characteristic elements were evaluated by SPSS cluster analysis and principal component analysis. Resuits: Six principal components were extracted from the original data. The principal component analysis results showed that Cu, Zn, Fe, Mn, Ni, Cr and Se may be the characteristic elements in A. macrostemon. The results of cluster analysis showed that the ten samples could be clustered reasonably into two groups, and the contents of inorganic elements of A. macrostemon were related to the ecological environment, such as temperature and location. Conclusions:The overall quantitative similarity method can be used for the determination of the inorganic elements in A. macrostemon. The principal component analysis and cluster analysis can be perfectly applied in data processing in inorganic elements.
出处 《药物分析杂志》 CAS CSCD 北大核心 2011年第11期2063-2066,共4页 Chinese Journal of Pharmaceutical Analysis
关键词 中药材 薤白 小根蒜 电感耦合等离子体质谱 无机元素 全定量分析 主成分分析 聚类分析 Chinese medicinal crop longstamen onion bulb Allium macrostemon ICP/MS inorganic elements all the quantitative analysis principal component analysis cluster analysis
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