Discrimination of raw and vinegar-processed Genkwa Flos using metabolomics coupled with multivariate data analysis: a discrimination study with metabolomics coupled with PCA.

In this paper, a novel approach using UPLC-MS (Ultra performance liquid chromatography tandem mass spectrometry) coupled with multivariate statistic analysis was established for the profiling and discrimination of raw and processed herb using Genkwa Flos as a model herb. A batch of raw and processed samples was analyzed, and the datasets of t(R)-m/z pairs, ion intensities and sample codes were subjected to the principal component analysis (PCA). Raw and processed herb showed a clear classification of the two groups on the score plot. Loading plot was performed, and the chemical markers having great contributions to the differentiation were screened out. The identities of the chemical markers were identified by comparing the mass spectra and retention times with those of reference compounds and/or tentatively assigned by matching empirical molecular formulae and mass fragments with those of the known compounds published in the literatures. Based on the proposed strategy, yuanhuacine, genkwadaphnin, genkwanin-5-O-β-d-primeveroside, genkwanine N, genkwanin, 3'-hydroxy-genkwanin and apigenin were explored as representative markers in distinguishing the raw from the processed herbs. The method has been successfully applied in the distinguishing raw from processed herbs. Furthermore, the underlying detoxification mechanism of traditional processing procedure on the herb was predicted, and was related to the changes in the metabolic profiling. This research could be valuable to explore the chemical markers, investigate the mechanism underlying the processing procedure, and promote the quality control and safety application of traditional Chinese herbs.