Desorption electrospray ionisation mass spectrometric analysis of chemical warfare agents from solid‐phase microextraction fibers
Bibliographic record
Abstract
Abstract Desorption electrospray ionisation mass spectrometry (DESI‐MS) was recently reported for the direct analysis of sample media without the need for additional sample handling. During the present study, direct analysis of solid‐phase microextraction (SPME) fibers by DESI‐MS/MS was evaluated with indoor office media that might be collected during a forensic investigation, including wall surfaces, office fabrics, paper products and Dacron swabs used for liquid sampling. Media spiked at the µg/g level with purified chemical warfare agents and a complex munitions grade sample of tabun, to simulate the quality of chemical warfare agent that might be used for terrorist purposes, were successfully analysed by DESI‐MS/MS. Sulfur mustard, a compound that has not been successfully analysed by electrospray mass spectrometry in the past, was also sampled using a SPME fiber and analysed for the first time by DESI‐MS/MS. Finally, the overall analytical approach involving SPME headspace sampling and DESI‐MS analysis was evaluated during a scenario‐based training live agent exercise. A sarin sample collected by the military was analysed and confirmed by DESI‐MS in a mobile laboratory under realistic field conditions. Copyright © 2007 Crown in the right of Canada. Published by John Wiley & Sons, Ltd.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".