Direct infusion-tandem mass spectrometry combining with data mining strategies enables rapid chemome characterization of medicinal plants: A case study of Polygala tenuifolia.

Data-independent MS2 spectrum acquisition after fragmenting the precursor ion cohort with 1 Da bin, termed as MS/MSALL ®, offers an opportunity to achieve rapid chemome characterization when being coupled with direct infusion (DI). Some post-acquisition data processing strategies, such as mass defect filtering (MDF), diagnostic fragment ion filtering (DFIF), and neutral loss filtering (NLF), facilitate data extraction from massive dataset, and moreover, molecular weight (MW) imprinting allows rapid capturing those reported components. Here, DI-MS/MSALL ® was employed to acquire cubic spectral dataset, and the strategies such as MW imprinting, MDF, DFIF, and NLF, were subsequently applied to filter the structural information. The integrated pipeline was utilized for the chemome characterization of Polygala tenuifolia, a famous edible medicinal plant. To aid information filtering, an in-house chemical library was built by comprehensively collecting structural information from some available databases. A single analytical run was completed within 5 min. For MS1 spectrum processing, MW imprinting was firstly applied to capture the compounds in the chemical library, and "five-point" MDF frames were employed to pursue saponins, oligosaccharide esters, and xanthones. Regarding MS2 spectral plot, DFIF and NLF were deployed to search information-of-interest. Structural identification was accomplished by carefully correlating precursor ions and MS2 spectra, applying the well-defined mass cracking rules, and referring to literature information as well as available databases. A total of 109 compounds, mainly saponins (40 ones), oligosaccharide esters (29 ones), and xanthones (19 ones), were captured and structurally annotated. MS1 spectra were also implemented for chemome comparison between Polygala tenuifolia and several similar plants belonging to Polygala genus, resulting in the observation of significant inter- and intra-species differences. Above all, DI-MS/MSALL ® is a promising choice for high-throughput chemome profiling of, but not limited to, medicinal plants, in particular when being integrated with post-acquisition data processing strategies.