Metabolomics Study on Quality Control and Discrimination of Three Curcuma Species based on Gas Chromatograph–Mass Spectrometry

Authors

  • Zheng Xiang,

    Corresponding author
    1. School of Pharmaceutical Sciences, Wenzhou Medical College, Wenzhou, Zhejiang, People's Republic of China
    • Department of Pharmaceutical Analysis and Drug Metabolism, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
    Search for more papers by this author
  • Xian-qin Wang,

    1. School of Pharmaceutical Sciences, Wenzhou Medical College, Wenzhou, Zhejiang, People's Republic of China
    Search for more papers by this author
  • Xiao-jun Cai,

    1. School of Pharmaceutical Sciences, Wenzhou Medical College, Wenzhou, Zhejiang, People's Republic of China
    Search for more papers by this author
  • Su Zeng

    Corresponding author
    • Department of Pharmaceutical Analysis and Drug Metabolism, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
    Search for more papers by this author

Z. Xiang and Zeng Su, Department of Pharmaceutical Analysis and Drug Metabolism, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, People's Republic of China. E-mail: xiangzheng1978@yahoo.cn; zengsu@zju.edu.cn

ABSTRACT

Introduction

Metabonomic analysis is an important molecular phenotyping method for characterising plant ecotypic variations; hence, it may become a powerful tool for quality control and discrimination of traditional Chinese medicine (TCM).

Objective

To discriminate and assess the quality of Curcuma phaeocaulis, C. kwangsiensis and C. wenyujin from different ecotypes. The identification of the compositions of essential oils from the three Curcuma species was included in this study.

Methodology

Metabolomics analysis was carried out on all samples by gas chromatography–mass spectrometry (GC-MS) coupled with multivariate statistical analysis. Characterisation of phytochemicals in essential oils was performed by automated matching to the MS library and comparison of their mass spectra (NIST05 database).

Results

Principal component analysis (PCA) effectively distinguished the samples from different species and ecotypes. Partial least squares discrimination analysis (PLS-DA) was successfully employed in classifying the GC-MS data of authentic, commercial and introduction cultivation samples. Furthermore, the components contributing significantly to the discrimination, namely curzerenone, germacrone, curdione and epicurzerenone, were screened by PCA and PLS-DA loading plots and further can be used as chemical markers for discrimination and quality control among different groups of samples. Copyright © 2011 John Wiley & Sons, Ltd.

Ancillary