鳖甲HPLC指纹图谱的分析模式探讨

目的:建立不同产地鳖甲药材的高效液相指纹图谱评价方法。方法:采用HPLC-DAD方法测定了12个产地鳖甲药材样品,通过"中药色谱指纹图谱相似度评价系统"2004 A进行评价,建立了共有模式,以相关度评价图谱的相似性,对结果进行聚类分析和主成分分析。结果:12批不同产地的鳖甲药材有7个共有峰,其高效液相色谱指纹图谱相似度均在0.9以上,不同产地鳖甲药材可分为两类,质量相似性较好;主成分分析结果同聚类分析法分析结果一致,经主成分分析,12批鳖甲药材共提取出3个主成分,其累积贡献率达83.21%。结论:用SPSS软件对鳖甲药材的指纹图谱进行主成分分析及聚类分析,所建立的模式识别方法,操作简便,统计结果具有可靠性,对鳖甲药材化学计量学分类及其质量评价提供有效参考。

Chemical Pattern Recognition Study on HPLC Fingerprint of Trionycis Carapax Based on Principal Component Analysis and Cluster Analysis

Objective: To establish a sensitive and specific HPLC-fingerprint method for the quality control of Trionycis Carapax from different areas. Method: The samples from different areas were determined with the method. Mean chromatogram was generated as the representative standard fingerprint and the similarity of each chromatogram against the mean chromatogram was also calculated using the 'Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine' (Version 2004A). Samples were clustered using Hierarchical cluster analysis (HCA) and principal component analysis (PCA) based on the ratio of characteristic peaks and standard peak. Result: Seven common peaks in the HPLC-fingerprint of Trionycis Carapax were obtained, and one peak was identified. In addition, all the similarities were over 0.9.12 batches of Trionycis Carapax from different areas were classified into two groups based on hierarchical cluster, which showed the quality of Trionycis Carapax from different areas was in good consistency. The classification result according to PCA was consistent with the HCA. The PCA showed that seven common peaks were integrated into three principal components (PC) and their accumulation contributing rate amounted to 83.21%. Conclusion: SPSS 19.0 software can be used to cluster samples. Its operation is simple and convenient. The result of statistics is reliable and able to provide a reference for the chemometrics classification and quality evaluation of Trionycis Carapax.