IodoFinder: Machine Learning-Guided Recognition of Iodinated Chemicals in Nontargeted LC-MS/MS Analysis

Iodinated disinfection byproducts (I-DBPs) pose significant health concerns due to their high toxicity. Current approaches to recognize unknown I-DBPs in mass spectrometry (MS) analysis rely on negative ionization mode, in which the characteristic I^- fragment can be observed in tandem mass spectra (MS/MS). Still, many I-DBPs ionize exclusively in positive ionization mode, where the I^- fragment is absent. To address this gap, this work developed a machine learning-based strategy to recognize iodinated compounds (I-compounds) from their MS/MS in both electrospray positive (ESI+) and negative ionization (ESI-) modes. Investigating over 6000 MS/MS spectra of 381 I-compounds, we first identified five characteristic I-containing neutral losses and one diagnostic I^- fragment in ESI+ and ESI- modes, respectively. We then trained Random Forest models and integrated them into IodoFinder, a Python program, to streamline the recognition of I-compounds from raw LC-MS data. IodoFinder accurately recognized over 96% of the 161 I-compound standards in both ionization modes. In its application to DBP mixtures, IodoFinder discovered 19 I-DBPs with annotated structures and an additional 17 with assigned formulas, including 12 novel and 3 confirmed I-DBPs. We envision that IodoFinder will advance the identification of both known and unknown I-compounds in exposome studies.

exposome; fragmentation pattern; high-resolution mass spectrometry; iodinated compound; iodinated disinfection byproducts; machine learning; nontargeted analysis.