A novel strategy to rapidly explore potential chemical markers for the discrimination between raw and processed Radix Rehmanniae by UHPLC–TOFMS with multivariate statistical analysis

In traditional Chinese medicine, raw and processed herbs are used to treat different diseases. Suitable chemical markers are crucial for the discrimination between raw and processed herbs. In this study, a novel strategy using UHPLC–TOFMS coupled with multivariate statistical analysis to rapidly explore potential chemical markers was proposed and validated. Using Radix Rehmanniae as a model herb, batches of raw and processed samples were determined by UHPLC–TOFMS. The datasets of tR–m/z pair, ion intensity and sample code were subjected to principal component analysis (PCA) and orthogonal partial least squared discriminant analysis (OPLS-DA) to holistically compare the difference between raw and processed samples. Once a clear cluster was found, extended statistics was performed to generate S-plot, in which the variables (tR–m/z pair) contributing most to the difference were clearly indicated as points at the two ends of "S", and the components that correlate to these ions should be the processing-induced transformed components. These transformed components could be regarded as the potential chemical markers that can be used to distinguish between raw and processed herbs. The identity of the potential markers can be identified by comparing the mass/UV spectra and retention time with that of reference compounds and/or tentatively assigned by matching empirical molecular formula with that of the known compounds published. Using this proposed strategy, leonuride or its isomer and 5-(α-d-glucopyranosyl-(1-6)-α-d-glucopyranosyloxymethyl)-2-furancarboxaldehyde were rapidly explored as the most characteristic markers of raw and processed Radix Rehmanniae, respectively. This newly proposed strategy can not only be used to explore chemical markers but also to investigate the chemical transforming mechanisms underlying traditional herb processing.