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引用本文:张立军,李芸,戴海蓉,樊秦,姚娟.高乌头炮制品的HPLC指纹图谱研究及其2种生物碱成分含量测定[J].中国现代应用药学,2018,35(1):57-62.
ZHANG Lijun,LI Yun,DAI Hairong,FAN Qin,YAO Juan.HPLC Fingerprints Analysis of Processed Aconitum Sinomontanum Nakai and Determination of the Contents of Two Alkaloids[J].Chin J Mod Appl Pharm(中国现代应用药学),2018,35(1):57-62.
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高乌头炮制品的HPLC指纹图谱研究及其2种生物碱成分含量测定
张立军1, 李芸1, 戴海蓉1, 樊秦2, 姚娟1
1.甘肃中医药大学, 兰州 730000;2.甘肃省中藏药化学与质量研究省级重点实验室, 兰州 730000
摘要:
目的 建立不同产区高乌头炮制品的HPLC指纹图谱,并测定2种生物碱成分的含量,为高乌头制品质量控制提供参考依据。方法 通过HPLC梯度洗脱建立高乌头炮制品的指纹图谱,并采用中药指纹图谱相似度评价系统(2012版)、主成分分析和聚类分析对指纹图谱进行分析。结果 建立了高乌头炮制品指纹图谱,10批高乌头制品的相似度<0.90,标定共有峰18个,并对其中2主要成分(高乌甲素、冉乌头碱)进行含量测定;聚类分析将所有批次高乌头制品分为4类,反映了10批不同产区高乌头制品的质量特征;主成分分析筛选出累计贡献率达到88.8%的4个主成分,得到决定高乌头药材质量7个化学成分。结论 该方法重复性好、特征性强,可用于高乌头炮制品全面质量评价。
关键词:  高乌头炮制品  指纹图谱  主成分分析  聚类分析  不同产区  高乌甲素  冉乌头碱
DOI:10.13748/j.cnki.issn1007-7693.2018.01.013
分类号:R917.101
基金项目:国家自然科学基金项目(81560650)
HPLC Fingerprints Analysis of Processed Aconitum Sinomontanum Nakai and Determination of the Contents of Two Alkaloids
ZHANG Lijun1, LI Yun1, DAI Hairong1, FAN Qin2, YAO Juan1
1.Gansu University of TCM, Lanzhou 730000, China;2.Provincial-Level Key Laboratory for Tibet Herbal Chemicals and Quality Research in Gansu Colleges and Universities, Lanzhou 730000, China
Abstract:
OBJECTIVE To establish an HPLC fingerprint of processed Aconitum sinomontanum Nakai from different areas, and determine the contents of two alkaloids to evaluate the quality of the preparation. METHODS HPLC gradient elution method were developed to establish fingerprints for processed Aconitum sinomontanum Nakai, and the fingerprint were analyzed and compared by Chinese Materia Medica (CMM) Fingerprint Similarity Evaluation System (2012 edition), principle component analysis and cluster analysis. RESULTS The common mode for processed Aconitum sinomontanum Nakai fingerprint was established, the similarity was greater than 0.90 in 10 batches of processed Aconitum sinomontanum Nakai medicinal herbs, 18 common fingerprint peaks were identified, and the content of lappacontine and ranaconitine were determined. The different areas of samples could be classified into four groups by using cluster analysis, which reflected the quality characteristics of 10 batches of processed Aconitum sinomontanum Nakai from different areas. We screened four main components whose cumulative contribution rate were 88.8% by principle component analysis, and get seven chemical composition which determined the quality of processed Aconitum sinomontanum Nakai. CONCLUSION This method has good reproducibility, and strong characteristic, which can be used for the quality evaluation of processed Aconitum Sinomontanum Nakai.
Key words:  processed Aconitum sinomontanum Nakai  fingerprint  principle component analysis  cluster analysis  different areas  lappacontine  ranaconitine
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