Interactive software for visualization of nontargeted mass spectrometry data—FluoroMatch visualizer
Bibliographic record
Abstract
Abstract There are thousands of different per- and polyfluoroalkyl substances (PFAS) in everyday products and in the environment. Discerning the abundance and diversity of PFAS is essential for understanding sources, fate, exposure routes, and the associated health impacts of PFAS. While comprehensive detection of PFAS requires the use of nontargeted mass spectrometry, data processing is time intensive and prone to error. While automated approaches can compile all mass spectrometric evidence (e.g., retention time, isotopic pattern, fragmentation, and accurate mass) and provide ranking or scoring metrics for annotations, confident assignment of structure often still requires extensive manual review of the data. To aid this process, we present FluoroMatch Visualizer, an open-source free software which was developed to provide interactive visualizations which include normalized mass defect plots, retention time versus accurate mass plots, MS/MS fragmentation spectra, and tables of annotations and metadata. All graphs and tables are interactive and have cross-filtering such that when a user selects a feature, all other visuals highlight the feature of interest. Several filtering options have been integrated into this novel data visualization tool, specifically with the capability to filter by PFAS chemical series, fragment(s), assignment confidence, and MS/MS file(s). FluoroMatch Visualizer is part of FluoroMatch Suite, which consists of FluoroMatch Modular, FluoroMatch Flow, and FluoroMatch Generator. FluoroMatch Visualizer enables annotations to be extensively validated, increasing annotation confidence. The resulting visualizations and datasets can be shared online in an interactive format for community-based PFAS discovery. FluoroMatch visualizer holds potential to promote harmonization of nontargeted data processing and interpretation throughout the PFAS scientific community.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".