An intelligent strategy integrating polygonal mass defect filtering and preferred ion lists based on high-resolution mass spectrometry for the characterization of chemical compounds in Phellodendri Chinensis Cortex.

[Display omitted] • A polygonal MDF and preferred ion lists were used for progressive data acquisition. • Deep learning-assisted MDF combined with GNPS for compound identification. • 116 compounds containing 86 alkaloids were identified in PCC. Advanced analytical methods are often required for detailed and comprehensive interpretation of the complex traditional Chinese medicine system. Phellodendri Chinensis Cortex (PCC) is a common herb involving alkaloids as its main active constituent. The systemic characterization of alkaloids in PCC was carried out by an intelligent process combining polygonal mass defect filtering (p-MDF) with preferred ion lists based on high-resolution mass spectrometry. Firstly, the combination of p-MDF and preferred ion lists was used for data acquisition. Secondly, the mass spectrometry data sets were classified by the deep learning-assisted MDF method for manual annotation, including alkaloids and other components. The characteristic diagnosis ions and neutral loss fragments were also used to assist the manual annotation. Subsequently, the Global Natural Product Social platform was employed for automated annotation. A total of 116 compounds from PCC were identified, containing 86 alkaloids, 1 amino acid, 9 phenylpropanoids, 2 triterpenoids, 13 fatty acids and 5 others. The established strategy provided a powerful guide for the chemical characterization of complex herbal substrates. [ABSTRACT FROM AUTHOR]