Quantitative Analysis of Multi-components by Single Marker and Fingerprint Analysis of Achyranthes bidentata Blume

Quantitative Analysis of Multi-components by Single Marker and Fingerprint Analysis of Achyranthes bidentata Blume
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牛膝多成分单标记定量分析及指纹图谱分析

DOI:
10.1093/chromsci/bmy031
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发表时间:
2018-08-01
影响因子:
1.3
通讯作者:
Deng, Ran
Deng, Ran
中科院分区:
化学4区
文献类型:
--
作者:
Li, Feng;Wu, Hong;Deng, Ran

文献摘要

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建立了一种简便有效的高效液相色谱二极管阵列检测方法——色谱指纹图谱结合相似度分析、层次聚类分析和单标记多组分定量分析(QAMS)对牛膝药材进行产地鉴别和质量评价。在色谱指纹图谱中,选取16个峰作为共同模型,评价了18批(S-1-S-18)中国不同产地的苦参样品的相似度。18批样品与对照指纹图谱相似度均大于0.75。将18批山参样品分为两组进行定量分析,QAMS法与外标法对3种生物活性成分(β -ecdysterone、cyasterone和5-羟甲基糠醛)的定量分析结果证实了两种方法的一致性,3种成分在线性范围内具有良好的回归(R = 0.9995),加样回收率在976 ~ 101.5%范围内。本研究表明,采用HPLC指纹图谱和QAMS相结合的方法可以很好地评价白刺草药材的质量。
A simple and effective method of high performance liquid chromatography (HPLC) with diode array detection was established to identify the origin of Achyranthes bidentata Blume and evaluate its quality, based on chromatographic fingerprint combined with the similarity analysis, hierarchical cluster analysis and the quantitative analysis of multi-components by single marker (QAMS). In the chromatographic fingerprint, 16 peaks were selected as the common model to evaluate the similarities among 18 batches (S-1-S-18) of A. bidentata Blume samples collected from different origins in China. The similarities values for 18 batches of samples were more than 0.75, which compared with control fingerprint. Furthermore, 18 batches of A. bidentata Blume samples were categorized into two groups for quantitative analysis, the quantification of three bioactive constituents (beta-ecdysterone, cyasterone and 5-hydroxymethyl furfural) between QAMS and external standard method proved the consistency of the two methods, the three constituents showed good regression (R > 0.9995) within linear ranges, and their recoveries were within the range of 97.6-101.5%. This study demonstrated that the quality of A. bidentata Blume can be successfully evaluated by means of a combination of HPLC chromatographic fingerprint and QAMS approach.