Determination of Ethanol and Aromatics in Blood by Headspace Portable Gas Chromatography-Mass Spectrometry (HS-PGC-MS)
Bibliographic record
Abstract
Ethanol abuse is increasingly becoming a global issue, adversely affecting both society and families. In blood alcohol concentration (BAC) detection, traditional techniques have long experimental cycles and harsh experimental conditions and often fail to produce timely results. Consequently, achieving swift and precise on-site detection of ethanol poisoning in humans poses a formidable challenge in analytical science. We designed a novel assay for swift on-site analysis of ethanol and BTEX (benzene, toluene, ethylbenzene, and xylene) poisoning in humans using a custom portable headspace injector (portable HS) and portable gas chromatography-mass spectrometry (portable GC-MS). This setup, known for its lightweight design and speed, allows on-site sampling and delivers rapid results. We established a calibration curve (R2 > 0.99) for ethanol in blood using portable gas chromatography-mass spectrometry (portable GC-MS). This on-site blood alcohol concentration (BAC) assay proves simpler than traditional methods, enabling rapid analysis of each sample within a short timeframe and requiring only a small blood volume (0.1 ml). In addition to our investigation on ethanol, we developed a portable method for detecting BTEX in blood. This method provides a favorable coefficient of determination (R2 > 0.99) for various substances, thereby broadening the utility of portable mass spectrometry in areas such as traffic inspection and forensic identification.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".