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Multi-responses extraction optimization combined with high-performance liquid chromatography-diode array detection-electrospray ionization-tandem mass spectrometry and chemometrics techniques for the fingerprint analysis of Aloe barbadensis Miller.

Authors
  • Zhong, Jia-Sheng1
  • Wan, Jin-Zhi2
  • Ding, Wen-Jing1
  • Wu, Xiao-Fang3
  • Xie, Zhi-Yong4
  • 1 School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, PR China. , (China)
  • 2 School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, PR China. Electronic address: [email protected] , (China)
  • 3 Analysis and Testing Center, Chinese Academy of Tropical Agricultural Sciences, Haikou 571101, PR China; Hainan Provincial Key Laboratory of Quality and Safety for Tropical Fruits and Vegetables, Haikou 571101, PR China. , (China)
  • 4 School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, PR China. Electronic address: [email protected] , (China)
Type
Published Article
Journal
Journal of pharmaceutical and biomedical analysis
Publication Date
Mar 25, 2015
Volume
107
Pages
131–140
Identifiers
DOI: 10.1016/j.jpba.2014.12.032
PMID: 25590942
Source
Medline
Keywords
License
Unknown

Abstract

A quality control strategy using high-performance liquid chromatography-diode array detector-electrospray ionization-tandem mass spectrometry (HPLC-DAD-ESI-MS/MS) coupled with chemometrics analysis was proposed for Aloe barbadensis Miller. Firstly, the extraction conditions including methanol concentration, extraction time and solvent-to-material ratio were optimized by multi-responses optimization based on response surface methodology (RSM). The optimum conditions were achieved by Derringer's desirability function and experimental validation implied that the established model exhibited favorable prediction ability. Then, HPLC fingerprint consisting of 27 common peaks was developed among 15 batches of A. barbadensis samples. 25 common peaks were identified using HPLC-DAD-ESI-MS/MS method by their spectral characteristics or comparison with the authentic standards. Chemometrics techniques including similarity analysis (SA), principal components analysis (PCA) and hierarchical clustering analysis (HCA) were implemented to classify A. barbadensis samples. The results demonstrated that all A. barbadensis samples shared similar chromatographic patterns as well as differences. These achievements provided an effective, reliable and comprehensive quality control method for A. barbadensis.

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