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Metabolomics

, Volume 4, Issue 3, pp 248–260 | Cite as

Application of ultra-performance LC-TOF MS metabolite profiling techniques to the analysis of medicinal Panax herbs

  • Guo X. Xie
  • Yan Ni
  • Ming M. Su
  • Yuan Y. Zhang
  • Ai H. Zhao
  • Xian F. Gao
  • Zhong Liu
  • Pei G. Xiao
  • Wei Jia
Original Article

Abstract

The morphological appearance and some ingredients of Panax ginseng, Panax notoginseng and Panax japonicus of the Panax genus are similar. However, their pharmacological activities are obviously different due to the significant differences in the types and quantity of saponins in each herb. In the present study, ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOFMS) was used to profile the abundances of metabolites in the three medicinal Panax herbs. Multivariate statistical analysis technique, that is, principle component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used to discriminate between the Panax samples. PCA of the analytical data showed a clear separation of compositions among the three medicinal herbs. The critical markers such as chikusetsusaponin IVa, ginsenoside R0, ginsenoside Rc, ginsenoside Rb1, ginsenoside Rb2 and ginsenoside Rg2 accountable for such variations were identified through the corresponding loading weights, and the tentative identification of biomarkers is completed by the accurate mass of TOFMS and high resolution and high retention time reproducibility performed by UPLC. The proposed analytical method coupled with multivariate statistical analysis is reliable to analyze a group of metabolites present in the herbal extracts and other natural products. This method can be further utilized to evaluate chemical components obtained from different plants and/or the plants of different geographical locations, thereby classifying the medicinal plant resources and potentially elucidating the mechanism of inherent phytochemical diversity.

Keywords

Panax Metabolite profiling UPLC-QTOFMS Saponin Multivariate statistical analysis 

Notes

Acknowledgements

This study was financially supported by the International Collaboration Project (project number 2006DFA02700) of the Ministry Science and Technology of the People’s Republic of China. We also thank Yongbo Wang for assisting with sample collection.

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Guo X. Xie
    • 1
  • Yan Ni
    • 2
  • Ming M. Su
    • 2
  • Yuan Y. Zhang
    • 2
  • Ai H. Zhao
    • 2
  • Xian F. Gao
    • 2
  • Zhong Liu
    • 2
  • Pei G. Xiao
    • 3
  • Wei Jia
    • 1
    • 2
  1. 1.Shanghai Center for Systems BiomedicineShanghai Jiao Tong UniversityShanghaiPeople’s Republic of China
  2. 2.School of PharmacyShanghai Jiao Tong UniversityShanghai People’s Republic of China
  3. 3.Institute of Medicinal Plant DevelopmentChinese Academy of Medical Sciences, Peking Union Medical CollegeBeijingChina

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