An Analyte-Specific LC-IMS-HRMS Framework for Enhanced Identification Confidence in Target Screening of Contaminants...

Reliable identification of contaminants in complex matrices remains hampered by low-abundance signals, complex spectra, and coeluting isobars in liquid chromatography-high-resolution mass spectrometry (LC-HRMS) analyses with data-independent acquisitions. Moreover, wide-scope target screening workflows covering hundreds to thousands of known compounds from reference standards analyses often fail to fully exploit the acquired data, leading to false positives and false negatives. Herein, trapped ion mobility spectrometry (TIMS) was integrated into LC-HRMS, and an enriched database and an analyte-specific framework were introduced for enhanced identification confidence. The database including 1948 contaminants incorporated all MS and MS/MS qualifier ions together with their CCS values and mobility filtering windows, alongside the principal ion. Specific qualifiers were designated as mandatory for the first time by evaluating their relative intensity compared to the principal ion (≥50%), with the established identification points systems being refined accordingly to increase confidence. Regarding TIMS data, ∼2500 CCS values were determined, exhibiting high repeatability (RSD ≤ 0.70%) and interinstrument reproducibility (|ΔCCS|≤ 2%). Comparison with literature data across different IMS-HRMS platforms showed CCS accuracy within 2% for 89% of the ions. In matrix spiking experiments (raptor's eggs, human urine, wastewater), the cleaner mobility-filtered spectra resulted in improved selectivity for several contaminants' qualifiers, which improved detection at low concentrations, minimizing false negatives. Additionally, both differentiation based on CCS values (e.g., atenolol-practolol) and mandatory detection of predefined qualifiers (e.g., prometryn-terbutryn) decreased false positives. By incorporating the TIMS dimension and mandatory qualifiers as additional identification information, this work provides a robust framework for high-throughput environmental monitoring and human exposure assessment.