Annotation of complex mass spectra by multi-layered analysis
Bibliographic record
Abstract
Annotating electrospray small molecule mass spectra remains a challenging problem due to the multiple processes occurring during ionization. Although an [M+H]+ is often present, ions can be formed by reactions with other cations, background compounds and co-eluting species, and by in-source fragmentation. Even single analytes can produce multiple ion forms, many of which remain unidentified and may appear to be different species, affecting reproducibility, quantification and precursor selection in DDA experiments. Annotation usually compares differences between peaks to known adducts and losses but fails if key peaks are missing or if the peaks are from unexpected adducts. Further, isotopes are often assumed to be due to 13C and removed prior to analysis which can leave ‘orphan’ peaks if unusual elements are present. Here we describe an alternative multi-layered approach (MLA) which successively matches spectra to calculated target ion lists and reprocesses the residual ions. This allows the analyst to focus on the unknown ions and to progressively increase target list complexity since explained ions are removed. Target ion lists can be calculated from expected or observed masses and potential adducts or can be pre-defined lists, for example common contaminants. Using this approach on spectra of known standards we identified adducts with Ca, Al, Fe, Ba and possibly Mg and Sr. We also detected several compounds and adducts in a spectrum of co-eluting species from an LC-MS analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".