基于聚类分析与多元统计分析的东北林蛙油及其类似品、伪品的聚丙烯酰胺凝胶电泳图谱鉴别

目的:建立一种基于聚类分析与多元统计分析对东北林蛙油及其类似品黑龙江林蛙油、伪品中华蟾蜍油、牛蛙油的十二烷基硫酸钠-聚丙烯酰胺凝胶电泳(SDS-PAGE)图谱有效分类与鉴别的方法。方法:利用SDS-PAGE法,得到18批药材的电泳图谱,将SDS-PAGE电泳图谱转化为数据矩阵,利用NTSYSpc 2. 10e统计分析软件进行聚类分析,结合SMICA-P14. 1分析软件进行多元统计分析中的无监督的主成分分析(PCA),有监督的偏最小二乘法判别分析(PLS-DA)和正交偏最小二乘法判别分析(OPLS-DA)方法进行多元分析评价。结果:SDS-PAGE电泳图谱技术结合聚类分析与多元统计分析方法能够对东北林蛙油及其类似品、伪品进行准确分类、鉴别。除1号药材外,聚类分析可将四类药材聚为4支。PCA分析结果优于聚类分析,多元统计分析中有监督的PLS-DA和OPLS-DA的分析结果优于无监督的PCA分析,OPLS-DA的分类与鉴别效果最优,OPLS-DA将东北林蛙油、黑龙江林蛙油、牛蛙油、中华蟾蜍聚成4类,并通过VIP(variable importance in projection)值与OPLS-DA载荷图综合分析得到6个差异性蛋白质成分,相对分子质量分别为51. 363,35. 838,14. 565,17. 563,15. 358及21. 696 k Da。结论:SDS-PAGE电泳图谱技术结合聚类分析及多元统计分析方法可以作为一种有效分类、鉴别东北林蛙油及其类似品、伪品的方法。该研究为东北林蛙油的品质评价及筛选提供了一定的参考依据。

Discrimination of Rana dybowskii,Its Analogues and Counterfeits Based on Polyacrylamide Gel Electrophoretograms Combined with Cluster Analysis and Multivariate Statistical Analysis

Objective:To establish an effective classification and identification method for sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) maps of Rana dybowskii,its analogues and counterfeits based on cluster analysis and multivariate statistical analysis. Method:SDS-PAGE maps of 18 batches of R. dybowskii,its analogues and 2 counterfeits were obtained by SDS-PAGE method. SDS-PAGE maps were transformed into data matrix. NTSYSpc 2.10e statistical analysis software was used for cluster analysis,and SMICA-P 14.1 software was used for multivariate statistical analysis. Unsupervised Principal Component Analysis (PCA),Supervised Partial Least Squares Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) were performed for multivariate analysis and evaluation. Result:SDS-PAGE maps technology combined with cluster analysis and multivariate statistical analysis could accurately classify and identify R. dybowskii,its analogues and counterfeits. Cluster analysis could cluster four kinds of medicinal materials into four branches except No.1 medicinal materials. PCA results were superior to cluster analysis. Supervised PLS-DA and OPLS-DA results in multivariate statistical analysis were superior to unsupervised PCA. The classification and identification efficiencies of OPLS-DA were better than those of unsupervised PCA. OPLS-DA aggregated R. dybowskii,its analogues and 2 counterfeits into four groups. Six different protein components were obtained by comprehensive analysis of variable importance in projection (VIP) value, and OPLS-DA Bi load diagram,with relative molecular weights were 51.363,35.838,14.565,17.563,15.358 and 21.696 kDa,respectively. Conclusion:SDS-PAGE maps combined with cluster analysis and multivariate statistical analysis can be used as an effective method to classify and identify R. dybowskii,its analogues and counterfeits. This study provides a reference for the quality evaluation and screening of R. dybowskii.