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基于HPLC指纹图谱结合化学计量学的痢泻灵片质量评价 被引量:1

Quality evaluation of Lixieling Tablets based on HPLC fingerprints combined with chemometrics
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摘要 目的基于HPLC指纹图谱结合化学计量学对痢泻灵片质量进行评价。方法建立HPLC指纹图谱,系统聚类分析、主成分分析、正交偏最小二乘法判别分析筛选质量差异标志物,测定其含量。结果指纹图谱中有17个共有峰,相似度0.835~0.977。各批样品聚为3类,发现7个质量差异标志物,其中5个为番石榴酸、绿原酸、儿茶素、没食子酸、穿心莲内酯,在各自范围内线性关系良好(r=0.9999),平均加样回收率95.0%~105.0%,RSD 0.44%~1.66%。结论该方法简便、准确、高效,可为痢泻灵片全面质量控制和科学质量评价提供参考。 AIM To evaluate the quality of Lixieling Tablets based on HPLC fingerprints combined with chemometrics.METHODS The HPLC fingerprints were established,after which hierarchical cluster analysis,principal component analysis and orthogonal partial least squares discriminant analysis were adopted in the screening of quality difference markers,and their contents were determined.RESULTS There were seventeen common peaks in the fingerprints with the similarities of 0.835-0.977.Various batches of samples were clustered into three catagories,seven quality difference markers were found,five of which were piscidic acid,chlorogenic acid,catechin,gallic acid and andrographolide,which showed good linear relationships within their own ranges(r=0.9999),whose average recoveries were 95.0%-105.0%with the RSDs of 0.44%-1.66%.CONCLUSION This simple,accurate and efficient method can provide a reference for the comprehensive quality control and scientific quality evaluation of Lixieling Tablets.
作者 于密密 王安琪 杜小伟 王京辉 张文婷 陈有根 傅欣彤 郭洪祝 YU Mi-mi;WANG An-qi;DU Xiao-wei;WANG Jing-hui;ZHANG Wen-ting;CHEN You-gen;FU Xin-tong;GUO Hong-zhu(Beijing Institute for Drug Control,NMPA Key Laboratory for Quality Evaluation of Traditional Chinese Medicine(Traditional Chinese Patent Medicine),Beijing Key Laboratory of Analysis and Evaluation on Chinese Medicine,Beijing 102206,China)
出处 《中成药》 CAS CSCD 北大核心 2023年第9期2846-2852,共7页 Chinese Traditional Patent Medicine
基金 2022年国家抽检项目(2022)。
关键词 痢泻灵片 HPLC指纹图谱 质量差异标志物 系统聚类分析 主成分分析 正交偏最小二乘法判别分析 Lixieling Tablets HPLC fingerprints quality difference markers hierarchical cluster analysis principal component analysis orthogonal partial least squares discriminant analysis
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