摘要
目的:建立灯盏花样品HPLC指纹图谱并结合一测多评法(QAMS)对其中绿原酸、咖啡酸、洋荆素、灯盏花乙素、异绿原酸B、灯盏花甲素、异绿原酸A、异绿原酸C 8种成分进行含量测定。方法:色谱柱为Agilent ZORBAX Eclipse XDB-C18柱(250 mm×4.6 mm,5μm),流动相为0.1%甲酸溶液(A)-乙腈(B)-四氢呋喃(C),梯度洗脱;流速为0.8 mL/min;进样量为10μL;柱温为30℃;检测波长为330 nm。并对数据进行主成分分析和聚类分析。分别用外标法和QAMS计算含量。结果:建立了不同产地灯盏花样品的HPLC指纹图谱,标定了22个共有峰,指认出8个成分。QAMS法与外标法实测值间无显著性差异。结论:所建立的方法可用于灯盏花药材的质量控制。
Objective:To establish the HPLC fingerprint of Erigeron breviscapus and to determine the contents of chlorogenic acid,caffeic acid,cynarin,scutellarin,isochlorogenic acid B,apigenin-7-O-glucronide,isochlorogenic acid A and isochlorogenic acid C by QAMS.Methods:HPLC analysis was performed on an Agilent ZORBAX Eclipse XDB-C18(250 mm×4.6 mm,5 μm)column using 0.1% formic acid water(A)-acetonitrile(B)-tetrahydrofuran(C)as mobile phase with gradient elution and the flow rate was 0.8 mL/min,injection volume was 10 μL,column temperature was 30 ℃,detection wavelength was 330 nm.The samples from different regions were analysed by cluster analysis and principal component analysis.The contents were calculated by external standard method and QAMS method,respectively.Results:The HPLC fingerprint of Erigeron breviscapus from diffrent regions was established.A totle of twenty-two common peaks were found in the fingerprint and eight components were identified.There was no significant difference between the calculated value of QAMS of each component and the measured value of external standard method.Conclusion:The established quality control methods of fingerprint chromatogram and chemical pattern recognition combined with QAMS can be applied to quality control of Erigeron breviscapus.
作者
李艳荣
姚建伶
潘海峰
LI Yan-rong;YAO Jian-ling;PAN Hai-feng(Hebei Key Laboratory of Study and Exploitation of Chinese Medicine, Chengde Medical College, Chengde 067000, China;Baitai Pharmaceutical Co. Ltd. ,Beijing 100176,China)
出处
《中药材》
CAS
北大核心
2019年第1期110-115,共6页
Journal of Chinese Medicinal Materials
关键词
灯盏花
指纹图谱
一测多评
主成分分析
聚类分析
Erigeron breviscapus(Vant.)Hand.-Mazz.
Fingerprint
QAMS
Principal component analysis
Cluster analysis