Epiberberine

Huanglian (HL) is a member of the Ranunculaceae family, including Coptis chinensis Franch., Coptis deltoidea C. Y. Cheng et Hsiao, or Coptis teeta Wall. The dried rhizomes are highly esteemed herbal medicine in Chinese pharmacopeia. However, the composition of HL is complex, and current identification technologies are insufficient for conducting a comprehensive analysis of HL, leading to major obstacles in quality control. Therefore, an in-depth exploration of the influence of species diversity and geographic provenance on the chemical profile of HL is imperative for its rational application and quality assurance. To comprehensively analyze compounds in HL samples from diverse geographical regions, this study employed an integrated approach combining offline two-dimensional ultra-high-performance liquid chromatography coupled with quadrupole-Orbitrap mass spectrometry (2D-LC-MS/MS) with online 2D-LC-MS/MS. This dual-platform strategy enabled detailed characterization of complex compound profiles. Additionally, the retention time prediction (RT-Ensemble Pred) models were utilized to predict and identify the retention times of unknown compounds, which particularly facilitated the differentiation of isomers. The comprehensive research resulted in the identification of 150 chemical constituents in HL, including 72 isomers. Furthermore, the compounds were analyzed and categorized according to mathematical classification models, allowing for distinction between various geographical origins. Based on unexposed data, the model demonstrated robust predictive capability, enabling the selection of 20 distinctive characteristic compounds with prominent features for use in geographical origin discrimination. Overall, this multidimensional investigation significantly enhanced our understanding of the chemical composition and inherent variability of HL plant resources, providing crucial technical underpinnings and methodological insights for the comprehensive exploitation and utilization of HL in biomedical and pharmaceutical applications.

A comprehensive characterization strategy for the intelligent analysis of multiple chemical components in Yangxinshi Tablet (YXST) was established. The strategy developed the deep learning-assisted mass defect filtering intelligent classification, preferred ions capture list and active exclusion (DLA-MDF-PIL-AE) data acquisition mode by online comprehensive two-dimensional liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (2DLC-Q-TOF-MS/MS). Firstly, the online 2DLC-Q-TOF-MS/MS system was constructed and the orthogonality was evaluated. Secondly, the user interface for deep learning-assisted MDF intelligent classification technology was developed and applied to compounds classification to generate preferred ion capture lists of various types compounds. Finally, molecular networking (MN), associated neutral loss (NL) fragments, and characteristic diagnosis ion (CDI) were utilized for the automatic and manual annotation of compounds, respectively. As a result, a total of 228 compounds including 80 flavanoids, 52 alkaloids, 36 phenolic acids, 15 terpenoids, 17 saponins and 28 others were preliminary identified from YXST and source attribution was assigned to them. Furthermore, 39 compounds were simultaneously quantified by ultra-high performance liquid chromatography coupled with triple quadrupole mass spectrometry (UHPLC-MS/MS) method. Conclusively, the proposed integrated strategy proved to be a powerful method for characterizing multiple components in complex natural products.