Spectral Counting Approach to Measure Selectivity of High-Resolution LC–MS Methods for Environmental Analysis
Bibliographic record
Abstract
Advances in high-resolution mass spectrometers have allowed for the development of nontargeted screening methods, where data sets can be archived and retrospectively mined as new environmental contaminants are identified. We have developed a spectral counting approach to calculate the selectivities of LC-MS acquisition modes taking mass accuracy, sample matrix, and the analyte properties into account. The selectivities of high-resolution MS (HRMS) alone or in combination with all-ion-fragmentation (AIF), data-independent-acquisition (DIA), and data-dependent-acquisition (DDA) modes, performed on a Q-Exactive Orbitrap were compared by retrospectively screening surface water samples for 95 pharmaceuticals. Samples were reanalyzed using targeted LC-MS/MS to confirm the accuracy of each acquisition method and to quantitate the 29 putatively detected drugs. LC-HRMS provided the lowest calculated selectivities and accordingly produced the highest number of false positives (6). In contrast, DDA provided the highest selectivities, yielding only one false positive; however, it was bias toward the most intense signals resulting in the detection of only 10 compounds. AIF had lower selectivities than traditional LC-MS/MS, produced one false positive and did not detect 6 confirmed compounds. Because of the high-quality archived data, DIA selectivities were better than traditional LC-MS/MS, showed no bias toward the most intense signals, achieved low limits of detection, and confidently detected the greatest number of pharmaceuticals (22) with only one false positive. This spectral counting method can be used across different instrument platforms or samples and provides a robust and empirical estimation of selectivities to give more confident detection of trace analytes.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".