Metabolite identification strategy of non-targeted metabolomics and its application for the identification of components in Chinese multicomponent medicine Abelmoschus manihot L.

Abstract

Identification of multicomponent in traditional Chinese medicine (TCM) is complex and time-consuming. The inspection of the full-scan mass chromatograms was usually performed manually, which is labor-intensive. It is difficult to distinguish low response signals from complex chemical background. Furthermore, this process is typically based on earlier knowledge of the chemical composition of TCM, and those molecules that have not been characterized earlier were thus ignored. In this paper, a strategy using UPLC-MS combined with pattern recognition analysis was developed to simplify and quicken the identification of multicomponent in Abelmoschus manihot (L.) Medik. First, complex signals obtained by UPLC-MS were processed using automated data mining algorithm and further processed with multivariate chemometric methods. Multicomponent in Abelmoschus manihot L. can be clearly displayed in S- and VIP-plot. Using this method, 320 peaks which present in Abelmoschus manihot L. were detected. In the next step, accurate mass spectra of the characteristic markers acquired by QTOF MS were used to estimate their elemental formulae and enable structure identification. By searching in METLIN database, 41 components were tentatively identified in Abelmoschus manihot L. Our results showed that UPLC-MS based-pattern recognition analysis approach can be used to quickly identify TCM multicomponent and for standardization of herbal preparations.