Development and Validation of a Method for the Simultaneous Quantification of 21 Microbial Volatile Organic Compounds in Ambient and Exhaled Air by Thermal Desorption and Gas Chromatography–Mass Spectrometry
Bibliographic record
Abstract
Microbial volatile organic compounds (mVOCs) are metabolites developed by indoor molds responsible for several health effects. Their detection may be an alternative approach for fungal exposure assessment, given that the classical methods have limitations. The goal of this study was to develop and validate an analytical method to quantify 21 mVOCs in ambient and exhaled air using active sampling on sorbent tubes followed by thermal desorption and gas chromatography–mass spectrometry analysis. Tenax/Carbograph sorbent was selected for its extraction/desorption efficiency. Reliable linearity was obtained over the concentration range of mVOCs with low limits of detection (≥1.76 ng/m3) and quantification (≥5.32 ng/m3). Furthermore, accuracy and precision in the percentage recoveries ranged between 80–118% with coefficients of variations lower than 4.35% for all mVOCs. Feasibility tests with ambient air of different places (toxicology laboratory, office, and mold contaminated bedroom) showed that variations between settings were observable and that the highest mVOCs concentrations in the bedroom. Consequently, concentrations of 17 mVOCs were higher in the volunteer’s exhalate after exposure in the bedroom than in the laboratory. In conclusion, this method allows the detection of mVOCs in a new matrix, i.e., exhaled air and targeting the contaminated environment and, therefore, intervening for the protection of human health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".