Discrimination of Rana dybowskii,Its Analogues and Counterfeits Based on Polyacrylamide Gel Electrophoretograms Combined with Cluster Analysis and Multivariate Statistical Analysis
10.13422/j.cnki.syfjx.20191614
- VernacularTitle: 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
- Author:
Jing-feng LI
1
;
Meng LAN
2
;
Xue-feng BIAN
1
;
LYU
1
;
Hui ZHANG
1
;
Hui YAO
3
Author Information
1. Jilin Ginseng Academy in Changchun University of Chinese Medicine, Changchun 130117, China
2. Jilin Xinshui Science and Technology Development Co. Ltd., Changchun 130117, China
3. Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100193, China
- Publication Type:Research Article
- Keywords:
Rana chensinensis;
polyacrylamide gel electrophoresis;
cluster analysis;
multivariate statistical analysis;
differential components
- From:
Chinese Journal of Experimental Traditional Medical Formulae
2019;25(24):111-117
- CountryChina
- Language:Chinese
-
Abstract:
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.
