Sub-parts per billion detection of trace volatile chemicals in human breath using Selected Ion Flow Tube Mass Spectrometry
Bibliographic record
Abstract
BACKGROUND: Selected ion flow tube mass spectrometry (SIFT-MS) allows the real time quantification of trace gases in air. Due to its tolerance of high humidity levels the technique is particularly suited to the chemical analysis of breath. The detection limit of SIFT-MS has previously reported to be approximately 5 - 10 PPBV which is insufficient for the measurement of some low abundance constituents of breath. Recent developments in the design of SIFT-MS instruments have increased the ion precursor count rates. It is, however, unclear as to how these advances will affect instrument sensitivity for breath analysis. FINDINGS: Standard gases were prepared by adding known quantities of compounds present at zero or very low levels in breath (xylene and toluene) to either humidified bottled air or actual human breath. These were then analysed by SIFT-MS to calculate the limits of detection for each compound under conditions which mimic a single breath exhalation. For xylene and toluene the limits of detection was approximately 0.5 PPBV per 10 seconds of analysis time. Results gained using this level of sensitivity suggested the presence of low levels of the compounds indole and methylindole in human alveolar and static oral air, although further studies are necessary to confirm these findings. CONCLUSION: Recent advances in SIFT-MS have increased the techniques sensitivity for breath analysis into the sub PPBV range enabling the real time quantification of low level trace gases in human breath.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".