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基于HPLC指纹图谱结合化学模式识别及定量测定的夏枯草质量控制研究
引用本文:邱俊娜,张榆,张双,刘斌,姜艳艳,程发峰.基于HPLC指纹图谱结合化学模式识别及定量测定的夏枯草质量控制研究[J].中草药,2020,51(10):2842-2850.
作者姓名:邱俊娜  张榆  张双  刘斌  姜艳艳  程发峰
作者单位:北京中医药大学中药学院, 北京 102488;北京中医药大学中医学院, 北京 100029
基金项目:北京市教委共建科研基地建设项目(2016022)
摘    要:目的建立夏枯草药材HPLC指纹图谱,结合化学模式识别技术筛选出不同批次夏枯草质量差异性标志物,并以其为指标建立夏枯草含量测定方法,为科学全面地评价夏枯草药材质量提供参考。方法采用HPLC法建立30批不同产地夏枯草的指纹图谱,利用中药色谱指纹图谱相似度评价系统2004A进行相似度评价,确定共有峰;运用主成分分析和正交偏最小二乘判别分析,筛选出不同批次夏枯草质量差异性标志物,基于筛选出的质量差异性标志物建立含量测定方法,并对61批夏枯草药材进行含量测定。结果建立了夏枯草HPLC指纹图谱,共标定28个共有峰,其相似度均在0.970以上,表明30批夏枯草药材的整体质量相对稳定;采用主成分分析和正交偏最小二乘判别分析筛选出了不同批次夏枯草质量差异的5个标志物,分别为咖啡酸(5号峰)、金丝桃苷(9号峰)、异槲皮苷(10号峰)、异迷迭香酸苷(11号峰)和迷迭香酸(12号峰),以5个标志物为指标,对其进行含量测定,色谱峰分离度良好,且线性关系良好,平均加样回收率为95.0%~105.0%,RSD值均低于3%。结论该方法科学、准确、可靠,指纹图谱结合化学模式识别技术和含量测定构建了一个更加完善、合理、有效的夏枯草质量评价方法。

关 键 词:夏枯草  指纹图谱  化学模式识别  质量控制  主成分分析  正交偏最小二乘判别分析  咖啡酸  金丝桃苷  异槲皮苷  异迷迭香酸苷  迷迭香酸
收稿时间:2019/11/3 0:00:00

Quality control of Prunella vulgaris based on HPLC fingerprints combined with chemical pattern recognition and content determination
QIU Jun-n,ZHANG Yu,ZHANG Shuang,LIU Bin,JIANG Yan-yan,CHENG Fa-feng.Quality control of Prunella vulgaris based on HPLC fingerprints combined with chemical pattern recognition and content determination[J].Chinese Traditional and Herbal Drugs,2020,51(10):2842-2850.
Authors:QIU Jun-n  ZHANG Yu  ZHANG Shuang  LIU Bin  JIANG Yan-yan  CHENG Fa-feng
Institution:School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China;School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
Abstract:Objective The quantitative method for the quality makers screened by a chemical pattern recognition method combined with the HPLC fingerprint was established, so as to provide reference for scientific and comprehensive quality evaluation of Prunella vulgaris. Methods Fingerprints of 30 batches of P. vulgaris were established by HPLC. Similarity evaluation was performed by using Similarity Evaluation System for Fingerprint Chromatogram of TCM (2004A) to confirm the common peak. Principal component analysis and orthogonal partial least squares discriminant analysis were used to screen out the components that caused the quality differences in the batches. Quantitative method of the screened quality makers was established, and its content in 61 batches of samples was determined. Results The HPLC fingerprints of P. vulgaris were obtained, and a total of 28 common peaks were marked. The similarity of 30 batches of samples was higher than 0.970, which indicated that the overall quality of P. vulgaris was relatively stable. Caffeic acid (F5), hyperoside (F9), isoquercitrin (F10), salviaflaside (F11), and rosmarinic acid (F12) were recognized as the quality makers using principal component analysis and orthogonal partial least squares discriminant analysis. Five markers, which showed good linear relationship, were used as indicators for content determination. The average recovery was 95.0%-105.0%, with the RSD value less than 3%. Conclusion The analysis method established was scientific, accurate, and reliable. A more perfect, reasonable, and effective method for quality evaluation of P. vulgaris was constructed using a fingerprint combined with chemical pattern recognition technique.
Keywords:Prunella vulgaris L    fingerprint  chemical pattern recognition  quality control  principal component analysis  orthogonal partial least squares discrimination  caffeic acid  hyperoside  isoquercitrin  salviaflaside  rosmarinic acid
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