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

Clean high-resolution mass spectra (HRMS) are essentialto a successfulstructural elucidation of an unknown feature during nontarget analysis(NTA) workflows. This is a crucial step, particularly for the spectragenerated during data-independent acquisition or during direct infusionexperiments. The most commonly available tools only take advantageof the time domain for spectral cleanup. Here, we present an algorithmthat combines the time domain and mass domain information to performspectral deconvolution. The algorithm employs a probability-basedcumulative neutral loss (CNL) model for fragment deconvolution. Theoptimized model, with a mass tolerance of 0.005 Da and a score(CNL) threshold of 0.00, was able to achieve a true positiverate (TPr) of 95.0%, a false discovery rate (FDr) of 20.6%, and areduction rate of 35.4%. Additionally, the CNL model was extensivelytested on real samples containing predominantly pesticides at differentconcentration levels and with matrix effects. Overall, the model wasable 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 wascompared with the retention time difference method and peak shapecorrelation analysis, showing that a combination of correlation analysisand 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 of51.0%.