Cumulative Neutral Loss Model for Fragment Deconvolution in Electrospray Ionization High-Resolution Mass Spectrometry Data

• Published in: Analytical chemistry (Washington) Vol. 95; no. 33; pp. 12247 - 12255
• Main Authors: van Herwerden, Denice, O’Brien, Jake W., Lege, Sascha, Pirok, Bob W. J., Thomas, Kevin V., Samanipour, Saer
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 22.08.2023
• Subjects: Algorithms; Analytical chemistry; Chemistry; Correlation analysis; Deconvolution; electrospray ionization mass spectrometry; High resolution; Ionization; Ions; Mass spectra; Mass spectrometry; Mass spectroscopy; Pesticides; Reduction; Retention time; Time domain analysis
• ISSN: 0003-2700; 1520-6882; 1520-6882
• DOI: 10.1021/acs.analchem.3c00896

Clean high-resolution mass spectra (HRMS) are essential to a successful structural elucidation of an unknown feature during nontarget analysis (NTA) workflows. This is a crucial step, particularly for the spectra generated during data-independent acquisition or during direct infusion experiments. The most commonly available tools only take advantage of the time domain for spectral cleanup. Here, we present an algorithm that combines the time domain and mass domain information to perform spectral deconvolution. The algorithm employs a probability-based cumulative neutral loss (CNL) model for fragment deconvolution. The optimized model, with a mass tolerance of 0.005 Da and a scoreCNL threshold of 0.00, was able to achieve a true positive rate (TPr) of 95.0%, a false discovery rate (FDr) of 20.6%, and a reduction rate of 35.4%. Additionally, the CNL model was extensively tested on real samples containing predominantly pesticides at different concentration levels and with matrix effects. Overall, the model was able to obtain a TPr above 88.8% with FD rates between 33 and 79% and reduction rates between 9 and 45%. Finally, the CNL model was compared with the retention time difference method and peak shape correlation analysis, showing that a combination of correlation analysis and the CNL model was the most effective for fragment deconvolution, obtaining a TPr of 84.7%, an FDr of 54.4%, and a reduction rate of 51.0%.