Impact of method parameters on the performance of suspect screening for the identification of trace organic contaminants in surface waters
Bibliographic record
Abstract
The performance of a suspect screening method to detect diverse small-molecule trace organic contaminants (TOCs) was systematically evaluated using a set of 39 model compounds. Experiments showed that ionization efficiency, ion transfer parameters, and chromatography could affect the detection of TOCs. As expected, compounds with low ionization yields and poorly retained compounds in chromatographic columns are more difficult to identify in the samples at environmental concentrations. Similarly, TOCs with large deviations from the average mass of the compounds screened were not transmitted efficiently in the mass spectrometer thus negatively affecting their detection. The suspect screening method was validated in terms of recovery and limits of identification of the model compounds using three different types of solid-phase extraction cartridges (reversed phase with polar groups, mixed-mode anion exchange, and mixed mode cation exchange). Experiments showed that more than two-thirds of the model compounds had recoveries >75% with each of the three cartridges, and comparison of limits of identification showed that more than one-half of the model compounds could be identified at concentrations between 6 and 100 ng L−1. However, it was observed that the amount of co-extracted compounds was higher in mixed-mode ion exchangers compared with the reversed-phase cartridge. Application of the suspect screening method using the three different cartridges to surface water samples showed that between 0 to 3% of the positive matches found by the peak identification algorithm were classified as probable structures. Solutions to improve suspect screening of TOCs are proposed and discussed.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".