An integrated strategy based on characteristic fragment filter supplemented by multivariate statistical analysis in multi-stage mass spectrometry chromatograms for the large-scale detection and identification of natural plant-derived components in rat: The rhubarb case

Abstract

An integrated strategy based on characteristic fragment filter (CFF) supplemented by multivariate statistical analysis (MSA) for MSⁿ chromatograms [(CFF)s MSA] was proposed for the large-scale detection of natural plant-derived ingredients in vivo. To prove the practicability of this [(CFF)s MSA] strategy, rhubarb was taken as an example. First, representative authentic standards of homologous components contained in rhubarb were chosen, from which the fragmentation rules and chemical characteristic fragments (CCFs) were proposed. Second, the metabolic pathways of the representative compounds were deciphered, and the metabolic characteristic fragments (MCFs) of each family of compounds were acquired. Third, combined with CCFs and MCFs, a CFF method was established. Finally, MSA was used to supplement the xenobiotics missed by the CFF method. In our research, 274 compounds were detected in rhubarb, and 298 ingredients were identified in vivo after oral administration. The results demonstrated that this integrated strategy could comprehensively screen for plant-derived compounds in vivo.

Keywords: Characteristic fragment filter; Multivariate statistical analysis; Plant-derived metabolite; Rhubarb.