IodoFinder: Machine Learning-Guided Recognition of Iodinated Chemicals in Nontargeted LC-MS/MS Analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".