Integration of multidimensional data acquisition and data processing strategy for comprehensive characterization of chemical constituents in Qingre Sanjie capsule by UPLC-TWIMS-QTOF-MS and molecular networking.

Molecular networking (MN) is widely utilized in the compositional analysis of herbal medicines and complex formulations. While the technique effectively links compounds through shared MS2 fragmentation patterns and structural homology, it is often compromised by dispersed in-source fragment ions and adduct ions, which weaken spectral correlations and introduce redundant nodes. To address these limitations, we propose a novel integrated strategy combining Ion Identity Molecular Networking (IIMN) and Feature-Based Molecular Networking (FBMN), aiming to reduce interference from co-eluted ions and to enhance isomers discrimination, respectively. Evaluation based on precursor ion node counts, edge connectivity, and cluster topology confirmed IIMN and FBMN as complementary methods for identifying non-isomers and isomers, respectively. Importantly, IIMN significantly strengthens network connectivity, reduces data redundancy, and improves annotation reliability across diverse ion species. To demonstrate the applicability of this strategy, we employed Qingre Sanjie Capsule (QSC), a traditional Chinese medicine prescription for treating upper respiratory tract infections and allergic conjunctivitis, as a case study and performed systematic chemical identification using ultra-high performance liquid chromatography coupled with traveling wave ion mobility quadrupole time-of-flight mass spectrometry (UPLC-TWIMS-QTOF-MS). Two hybrid scan modes, Data-Dependent Acquisition (DDA) and high-definition DDA, were utilized to acquire both precursor and fragment ion data. A total of 143 compounds were unambiguously identified or tentatively characterized, including three novel constituents first reported. This strategy effectively minimizes interference from redundant nodes, increases the capacity for novel compound discovery, and provides a valuable approach for the systematic analysis of complex systems.