HPLC指纹图谱结合化学模式识别探讨脉安颗粒质量差异
投稿时间:2023-08-23     点此下载全文
引用本文:周维维,冀凤云,王小永,祝新华,王志轩.HPLC指纹图谱结合化学模式识别探讨脉安颗粒质量差异[J].中国现代中药,2024,26(4):720-725
DOI:10.13313/j.issn.1673-4890.20230823002
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作者中文名作者英文名单位中文名单位英文名E-Mail
周维维 ZHOU Wei-wei 承德市食品药品检验检测中心/河北省燕山地区道地药材检验检测技术创新中心,河北 承德 067000 Chengde City Institute for Food and Drug Control/Hebei Yanshan Area Authentic Medicinal Materials Inspection and Testing Technology Innovation Center, Chengde 067000, China  
冀凤云 JI Feng-yun 北京九龙制药有限公司,北京 102100 Beijing Jiulong Pharmaceutical Co., Ltd., Beijing 102100, China  
王小永 WANG Xiao-yong 承德市食品药品检验检测中心/河北省燕山地区道地药材检验检测技术创新中心,河北 承德 067000 Chengde City Institute for Food and Drug Control/Hebei Yanshan Area Authentic Medicinal Materials Inspection and Testing Technology Innovation Center, Chengde 067000, China  
祝新华 ZHU Xin-hua 围场满族蒙古族自治县中医院,河北 承德 068450 Weichang Hospital of Traditional Chinese Medicine, Weichang Manchu and Mongolian Autonomous County, Chengde 068450, China  
王志轩* WANG Zhi-xuan 承德市食品药品检验检测中心/河北省燕山地区道地药材检验检测技术创新中心,河北 承德 067000 Chengde City Institute for Food and Drug Control/Hebei Yanshan Area Authentic Medicinal Materials Inspection and Testing Technology Innovation Center, Chengde 067000, China  
基金项目:河北省承德市基础研究项目(202301A109)
中文摘要:目的 建立脉安颗粒高效液相色谱法(HPLC)指纹图谱,结合化学模式识别影响质量差异的特征化合物,探讨脉安颗粒质量差异。方法 收集19批脉安颗粒,分析并匹配指纹图谱,评价相似度;采用MetaboAnalyst 5.0在线分析系统及Simca 14.1软件进行聚类分析、主成分分析及偏最小二乘法-判别分析,评价样品质量并找出影响样品质量的特征化合物。结果 建立了脉安颗粒HPLC指纹图谱,匹配21个共有峰,分别指认出8个化合物,指纹图谱相似度为0.882~0.987。经分析,19批样品可分为3类,明确了影响脉安颗粒质量差异的特征成分。结论 建立的HPLC指纹图谱结合化学模式识别方法可用于脉安颗粒质量综合评价及差异性成分分析,为其质量标准提升提供参考。
中文关键词:脉安颗粒  指纹图谱  化学模式识别  质量评价  差异性成分
 
Exploring the Quality Differences of Maian Granules through the Integration of HPLC Fingerprint and Chemical Pattern Recognition
Abstract:Objective To establish a high performance liquid chromatography (HPLC) fingerprint of Maian Granules, combined with chemical pattern recognition to identify the characteristic compounds affecting the quality difference, and to explore the quality variations of Maian Granules.Methods Nineteen batches of Maian Granules were collected, and their fingerprint profiles were analyzed and matched to evaluate similarity. Clustering analysis (CA), principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were conducted using the MetaboAnalyst 5.0 online analysis system and Simca 14.1 software to evaluate the quality of the samples and find out the characteristic compounds affecting sample quality.Results The HPLC fingerprint of Maian Granules was established, matching 21 common peaks and identifying 8 compounds. The similarity of fingerprint profiles ranged from 0.882 to 0.987. Using statistical analysis, the 19 batches of samples could be divided into three categories, and the characteristic components affecting the quality difference were identified.Conclusion The combination of HPLC fingerprint and chemical pattern recognition can be used for comprehensive quality evaluation and differential component analysis of Maian Granules, laying a theoretical foundation for the improvement of its quality standard.
keywords:Maian Granules  fingerprint  chemical pattern recognition  quality evaluation  differential components
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