Comparison of Compound Identification Tools Using Data Dependent and Data Independent High-Resolution Mass Spectrometry Spectra

• Published in: Metabolites 2023-06, Vol.13 (7), p.777
• Main Authors: Nijssen, Rosalie, Blokland, Marco H, Wegh, Robin S, de Lange, Erik, van Leeuwen, Stefan P J, Berendsen, Bjorn J A, van de Schans, Milou G M
• Format: Article
• Language: English
• Subjects: Acids; annotation; Artificial intelligence; Chromatography; data independent acquisition; Data mining; Evaluation; Feeds; Health care industry; Identification; Information management; Libraries; Liquid chromatography; Machine learning; Mass spectrometry; Mass spectroscopy; metabolites; Metabolomics; pesticides; Scientific imaging; Software utilities; Solvents; veterinary drugs
• ISSN: 2218-1989; 2218-1989
• DOI: 10.3390/metabo13070777

Liquid chromatography combined with high-resolution mass spectrometry (LC-HRMS) is a frequently applied technique for suspect screening (SS) and non-target screening (NTS) in metabolomics and environmental toxicology. However, correctly identifying compounds based on SS or NTS approaches remains challenging, especially when using data-independent acquisition (DIA). This study assessed the performance of four HRMS-spectra identification tools to annotate in-house generated data-dependent acquisition (DDA) and DIA HRMS spectra of 32 pesticides, veterinary drugs, and their metabolites. The identification tools were challenged with a diversity of compounds, including isomeric compounds. The identification power was evaluated in solvent standards and spiked feed extract. In DDA spectra, the mass spectral library mzCloud provided the highest success rate, with 84% and 88% of the compounds correctly identified in the top three in solvent standard and spiked feed extract, respectively. The in silico tools MSfinder, CFM-ID, and Chemdistiller also performed well in DDA data, with identification success rates above 75% for both solvent standard and spiked feed extract. MSfinder provided the highest identification success rates using DIA spectra with 72% and 75% (solvent standard and spiked feed extract, respectively), and CFM-ID performed almost similarly in solvent standard and slightly less in spiked feed extract (72% and 63%). The identification success rates for Chemdistiller (66% and 38%) and mzCloud (66% and 31%) were lower, especially in spiked feed extract. The difference in success rates between DDA and DIA is most likely caused by the higher complexity of the DIA spectra, making direct spectral matching more complex. However, this study demonstrates that DIA spectra can be used for compound annotation in certain software tools, although the success rate is lower than for DDA spectra.