An integrated strategy to rapidly characterize non-targeted benzylisoquinoline alkaloids from Plumula nelumbinis ethanol extract using UHPLC/Q-orbitrap HRMS

Abstract Plumula nelumbinis (P. nelumbinis) is a traditional Chinese medicine (TCM) used for hundreds of years to treat various health problems including anxiety and hemorrhage. In this study, we describe a practical integrated data-mining strategy to rapidly profile non-targeted benzylisoquinoline alkaloid constituents from an ethanol extract of P. nelumbinis. The approach combined diagnostic fragment ion filtering (DFIF) with reverse diagnostic fragment loss filtering (RDFLF) and ultra-high performance liquid chromatography coupled with hybrid quadrupole-orbitrap high resolution mass spectrometry (UHPLC/Q-orbitrap HRMS). This was executed in full MS/dd-MS2 mode with different collision energies in positive ionization mode. This method enabled a total of 31 benzylisoquinoline alkaloids to be tentatively identified from the P. nelumbinis ethanol extract, with five identified as new compounds. This integrated approach, combining DFIF with RDFLF to mine non-targeted mass spectral data, was developed to systematically identify non-targeted complicated constituents in TCMs using UHPLC/Q-orbitrap HRMS without the need for reference standards. The UHPLC/Q-orbitrap HRMS executed in full MS/dd-MS2 mode provided a rapid and reliable method for determination of benzylisoquinoline alkaloids. The reliable integrated data-mining strategy, DFIF and RDFLF, could be useful for rapid profiling of non-target chemical constituents of TCMs using LC tandem HRMS.

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