Establishment of UPLC Fingerprint of Ficus tikoua and Its Cluster Analysis and Principle Component Analysis
- VernacularTitle:地瓜藤的UPLC指纹图谱建立及聚类分析、主成分分析
- Author:
Feng XU
1
;
Lan YANG
1
;
Tingting CHENG
1
;
Xulong HUANG
1
;
Dongsheng FAN
1
;
Hongmei WU
1
;
Xiangpei WANG
1
Author Information
1. College o f Pharmacy，Guizhou University of TCM，Guiyang 550025，China
- Publication Type:Journal Article
- Keywords:
Ficus tikoua;
Fingerprint;
UPLC;
Cluster analysis;
Principle component analysis;
OPLS-DA
- From:
China Pharmacy
2019;30(24):3388-3392
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE： To establish a UPLC fingerprint of Ficus tikoua. METHODS： UPLC method was adopted. The determination was performed on Waters ACQUITY UPLC BEF C18 column with mobile phase consisted of 0.2％ aqueous acetic acid-acetonitrile （gradient elution）； the detection wavelength was 254 nm； the flow rate was 0.1 mL/min； the column temperature was 25 ℃， and sample size was 2 μL. UPLC fingerprints of 10 batches of samples and 2 batches of adulterants were determined by using No. 14 peak as reference. The similarity evaluation was carried out by using the TCM Chromatographic Fingerprint Similarity Evaluation System （2012 edition） so as to determine common peak. The cluster analysis was performed by using SPSS 20.0 software. SIMCA 13.1 software was used to conduct the principal component analysis and orthogonal partial least squares discriminant analysis （OPLS-DA）. RESULTS： There were 28 common peaks in UPLC fingerprint of 10 batches of F. tikoua. The similarity of 10 batches of F. tikoua was between 0.839 and 0.935， and the similarities of the 2 batches of adulterants were 0.503 and 0.173 respectively， which indicated that F. tikoua could be distinguished from adulterants. 10 batches of F. tikoua could be divided into 2 categories by cluster analysis and principle component analysis， and S3-S5， S9 and S10 were grouped into one category， and the remaining batches were grouped into one category. 7 components with a variable importance in projection （VIP） value ＞1 were screened by OPLS-DA analysis. These 7 components may be the main components that caused the quality difference of 10 batches of F. tikoua samples. CONCLUSIONS： Established fingerprint， cluster analysis， principle component analysis and OPLS-DA can be used for the identification and quality control of F. tikoua.
