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目的:建立10批不同产地天麻中天麻总苷的HPLC指纹图谱,并对建立的指纹图谱信息进行多元统计分析,筛选出可能影响天麻总苷差异的指标性成分。方法:建立天麻总苷的HPLC指纹图谱,运用指纹图谱相似度评价软件对指纹图谱数据进行处理,运用SPSS 19.0进行系统聚类分析和主成分分析,根据主成分分析结果再进行聚类分析,筛选出可能影响天麻总苷差异的指标性成分。结果:天麻总苷HPLC指纹图谱共标记了12个共有峰,根据聚类分析结果,可将10个产地天麻聚为4类,导致差异的原因可能与地理位置和生长环境有一定关系。由PCA分析结果可知,筛选出6个天麻总苷差异成分,将这6个差异成分再进行聚类分析与之前结果基本一致,初步确定了天麻差异的指标性成分。结论:该实验建立了天麻总苷的HPLC指纹图谱,通过多元统计分析,筛选出可能影响天麻总苷差异的指标性成分,为天麻药材的全面质量控制奠定基础,为天麻总苷差异成分与药效关联研究提供参考。
OBJECTIVE: To establish 10 HPLC fingerprints of gastrodia elata glycosides glycosides from different areas, and to conduct multivariate statistical analysis on the fingerprints of the established gastrodia to screen out the index components that may affect the differences of gastrodia Total Gastrodia. Methods: HPLC fingerprinting of Gastrodia elata glycosides was established. The fingerprinting data were processed by fingerprint similarity evaluation software. The data were analyzed by cluster analysis and principal component analysis (SPSS 19.0). According to the results of principal component analysis, clustering analysis and screening Indicators that may affect differences in glycosides of Gastrodia elata. Results: According to the results of cluster analysis, there were 12 common peaks in HPLC fingerprints of gastrodia elata glycosides, which could be classified into 4 groups. The reason of the differences may be related to the geographical location and the growth environment. According to the result of PCA analysis, six components of GTIs were screened out, and then the clustering analysis of the six components was basically the same as the previous one. The index components of GMS were initially identified. Conclusion: This experiment established HPLC fingerprints of gastrodiagine total glycosides, through multivariate statistical analysis, screened the index components that may affect the differences of glycosides of Gastrodia elata, which laid the foundation for the total quality control of Gastrodia elata Effectiveness of related research to provide a reference.