Search

• A liquid chromatography-ion mobility-mass spectrometry method was established. • A 2-step filtering strategy integrated CCS interval predicting and MDF was developed. • Power regression was used to generate the CCS interval with 99% confidence level. • Totally, 172 isoquinoline alkaloid analogues were identified in four herbal samples. • False positive rates of integrated 2-step filtering and common MDF were compared. Rapid identification of chemical analogues in herbal medicines using liquid chromatography-mass spectrometry was an efficient tool for discoveries of potentially active ingredients. Multi-dimensional combination of various separation technologies could significantly enhance the capacities for detection of trace components and discrimination of multiple isomers. In this study, an integrated two-step filtering strategy on liquid chromatography-ion mobility tandem with quadrupole-time-of-flight mass spectrometry (LC-IM-QTOF MS) was developed for identification of analogues in complex matrixes. The extracted raw data were preliminarily filtered by a collision-cross section (CCS) interval generated from power regression with confidence level at 99% for prediction of analogues. Then, the remained ions were further screened using a mass defect filtering (MDF) window based on m/z and decimal m/z of potential skeletons and substituents. By applying this strategy, 86, 102, 73, and 57 isoquinoline alkaloids were identified in herbal materials of Coptis chinensis Franch (CC), C. deltoidea C.Y.Cheng et Hsiao (CD), C. teeta Wall (CT), and Corydalis yanhusuo W.T.Wang (CY). The integrated two-step filtering presented higher efficiencies on exclusion of the background interference and reducing the false-positive rates than previously reported approaches. This study facilitated the application of LC-IM-MS on small molecular analysis and promoted the discoveries of bioactive components of herbal medicines for further pharmacological researches and quality control. [ABSTRACT FROM AUTHOR]

• A modified mass defect filtering strategy with four detailed steps was developed. • The strategy was applied to analysis procyanidins in grape seed extracts with IM-MS. • Collision cross-sections of 769 procyanidin ions were measured at single field. • The trendlines of procyanidin ions were predicted using multi-model regression. • The tendency of collision cross-sections with various factors was discussed. Polymers, widely existing in food or dietary materials, have been attracting researchers, facing challenges, and needing effective strategies on targeted characterization in complex matrixes. A modified data filtering strategy (including locating with drift time and m / z ranges, multiple mass defect filtering, validating MS information, and evaluating MS/MS spectra) was developed and applied for procyanidins in the grape seed extracts (GSE) using drift tube ion mobility-mass spectrometry. The procyanidin ions' trendlines were predicted by multi-model regression. Their collision cross-sections (CCSs) were calculated using single-field methods. Totally, 769 CCSs belonging to 686 procyanidins with polymer degrees at 1–15 were characterized. The exponent regression was the most reasonable model (r 2 ≥ 0.9379) to reveal the trendlines. The change tendency of CCSs with their polymer degrees, charge states, and linkage types were investigated. This study provided an innovative strategy for targeted characterization of polymers in complex matrixes. [ABSTRACT FROM AUTHOR]

Ion mobility coupled with mass spectrometry (IM-MS), an emerging technology for analysis of complex matrix, has been facing challenges due to the complexities of chemical structures and original data, as well as low-efficiency and error-proneness of manual operations. In this study, we developed a structural similarity networking assisted collision cross-section prediction interval filtering (SSN-CCSPIF) strategy. We first carried out a structural similarity networking (SSN) based on Tanimoto similarities among Morgan fingerprints to classify the authentic compounds potentially existing in complex matrix. By performing automatic regressive prediction statistics on mass-to-charge ratios (m/z) and collision cross-sections (CCS) with a self-built Python software, we explored the IM-MS feature trendlines, established filtering intervals and filtered potential compounds for each SSN classification. Chemical structures of all filtered compounds were further characterized by interpreting their multidimensional IM-MS data. To evaluate the applicability of SSN-CCSPIF, we selected Ginkgo biloba extract and dripping pills. The SSN-CCSPIF subtracted more background interferences (43.24%∼43.92%) than other similar strategies with conventional ClassyFire criteria (10.71%∼12.13%) or without compound classification (35.73%∼36.63%). Totally, 229 compounds, including eight potential new compounds, were characterized. Among them, seven isomeric pairs were discriminated with the integration of IM-separation. Using SSN-CCSPIF, we can achieve high-efficient analysis of complex IM-MS data and comprehensive chemical profiling of complex matrix to reveal their material basis. [Display omitted] • Multi-molecules were classified by fingerprints-based structural similarity networking (SSN). • A Python software was built for IM-MS feature filtering by CCS prediction interval (CCSPIF). • Each classification of potential compounds was rapidly filtered from IM-MS data of GBE and GBDP. • Totally, 229 compounds were identified by performing SSN-CCSPIF on UHPLC-IM-QTOF MS. • Eight potential new compounds without previous reports were tentatively characterized. [ABSTRACT FROM AUTHOR]