Characterization of Volatile Organic Compounds from Human Analogue Decomposition Using Thermal Desorption Coupled to Comprehensive Two-Dimensional Gas Chromatography–Time-of-Flight Mass Spectrometry
Bibliographic record
Abstract
Complex processes of decomposition produce a variety of chemicals as soft tissues, and their component parts are broken down. Among others, these decomposition byproducts include volatile organic compounds (VOCs) responsible for the odor of decomposition. Human remains detection (HRD) canines utilize this odor signature to locate human remains during police investigations and recovery missions in the event of a mass disaster. Currently, it is unknown what compounds or combinations of compounds are recognized by the HRD canines. Furthermore, a comprehensive decomposition VOC profile remains elusive. This is likely due to difficulties associated with the nontarget analysis of complex samples. In this study, cadaveric VOCs were collected from the decomposition headspace of pig carcasses and were further analyzed using thermal desorption coupled to comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (TD-GC × GC-TOFMS). Along with an advanced data handling methodology, this approach allowed for enhanced characterization of these complex samples. The additional peak capacity of GC × GC, the spectral deconvolution algorithms applied to unskewed mass spectral data, and the use of a robust data mining strategy generated a characteristic profile of decomposition VOCs across the various stages of soft-tissue decomposition. The profile was comprised of numerous chemical families, particularly alcohols, carboxylic acids, aromatics, and sulfides. Characteristic compounds identified in this study, e.g., 1-butanol, 1-octen-3-ol, 2-and 3-methyl butanoic acid, hexanoic acid, octanal, indole, phenol, benzaldehyde, dimethyl disulfide, and trisulfide, are potential target compounds of decomposition odor. This approach will facilitate the comparison of complex odor profiles and produce a comprehensive VOC profile for decomposition.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".