Abstract
Establishing the identity of bioactive compounds to control the quality of Traditional Chinese Medicines is made more challenging by the complexity of the metabolite matrix, the existence of isomers, and the range of compound concentration and polarity observed between individual samples of the same plant in a multicomponent preparation. In addition, LC-MS analysis has limited capability for the separation and analysis of potentially important trace compounds and isomers, which hinders the comprehensive metabolite characterization of functional foods and Traditional Natural Medicine. To facilitate and improve the chemical composition characterization and enhance metabolite discernment, a comprehensive strategy was developed which integrates ion mobility mass spectrometry (IMS) with offline two-dimensional liquid chromatography based on hydrophilic interaction chromatography (HILIC) and conventional reversed phase (RP) C18 chromatography. Through application of the HILIC × RP offline 2D-LC approach, trace compounds were enriched and separated promoting a more efficient and detailed analysis of the matrix complexity. Comprehensive non-targeted multidimensional data (Rt1D, Rt2D, MS, CCS and MS/MS) and data-independent-acquisition (DIA) mass data of the metabolites in complex food and drug samples were obtained in the IMS-DIA-MS/MS mode on a Waters-SYNAPT G2-Si mass spectrometer with an ESI source. Through the application of high-efficiency neutral loss (NLs) and diagnostic product ions (DPIs) filter strategies, information from DIA mass data permitted the rapid detection and identification of compounds. The identification coverage of metabolites with low-quality MS/MS data was also improved. In the absence of analytical standards, Collision Cross Section (CCS) prediction and matching strategies based on theoretical chemical structures provided a method to distingish isomers. To demonstrate the efficacy of the technique this comprehensive strategy was applied to the compound characterization of Gastrodia Rhizoma (GR). Characterization of 272 compounds was achieved, including 146 unreported compounds. The results affirm that this comprehensive five-dimensional data collection strategy has the capacity to support the in-depth study of the high level of chemical diversity in Traditional Chinese Medicines.
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