Non-target screening analysis of hazardous noxious substances using gas chromatography-quadrupole time-of-flight mass spectrometry
Bibliographic record
Abstract
The forensic investigation of hazardous noxious substances (HNS) is paramount for an effective response to chemical spill emergencies and other accidents. Analyzing unknown emergency samples poses a challenge due to the limited availability of background information, making the selection of appropriate sample preparation and analytical methodologies difficult. The utilization of high-resolution mass spectrometers (HRMS) in screening both target and non-target substances proves instrumental in revealing hazardous substances that may be overlooked alongside the intended analytes. In this study, a gas chromatography-quadruple time-of-flight mass spectrometer (GC-QTOF-MS) was employed to identify numerous organic compounds in an indoor dust sample. The compounds detected encompassed normal alkanes, fatty acids (saturated and unsaturated), alcohols, phenols, sterols, drugs, polycyclic aromatic hydrocarbons (PAHs), pesticides, flame retardants (such as polybrominated biphenyl ethers, PBDEs), plasticizers (such as phthalates and phosphates), among others. Notably, concentrations of n-alkanes, fatty acids, and phthalates were relatively high, while PAHs and pesticides were present at trace levels. The application of GC-QTOF-MS provides a swift and confirmative approach for analyzing target, suspect, and non-target compounds in both routine and emergency scenarios. This methodology proves invaluable in enhancing our capability to comprehensively assess and address chemical incidents, ensuring a more thorough and accurate response.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".