PowerSorb® for forensic investigation of VOC traces: Application on perfume traces
Bibliographic record
Abstract
This study aims to assess the potential of PowerSorb®, a crime scene easy-to-use polydimethylsiloxane-based adsorbent, for the extraction of volatile organic compounds (VOCs) from olfactory (scent) traces. The PowerSorb®’s capacity for VOC collection is tested through increasingly complex extraction scenarios, using three commercial perfumes. Four scenarios were considered: (1) Direct analysis of liquid perfumes; (2) extraction of VOCs from liquid perfumes using PowerSorb®; (3) extraction of VOCs from polyester fabrics impregnated with perfume using PowerSorb®; and (4) extraction after cross-transfer between fabrics treated with different perfumes using PowerSorb®. Headspace Gas Chromatography coupled with a mass spectrometer (HS-GC/MS) has been used for the analysis. The results support that PowerSorb® does allow the adsorption and thermal desorption of VOCs. While measurement of uncertainties increases with the growing complexity of the transfer, PowerSorb® appears to be an efficient and easy-to-use tool for VOCs collection and the perfume’s identification, when compared to more traditional sorbent phases, such as solid phase microextraction (SPME), which is hardly suitable for real-case scenarios. Olfactory traces remain challenging in cross-transfer scenarios, and further studies should be developed to assess the different perfume’s dynamics (transfer, persistence, background, evaporation, and degradation). • PowerSorb® is a suitable adsorbent for perfume VOCs’ collection. • PowerSorb® is an efficient and easy-to-use adsorbent for complex forensic scenarios. • Headspace Gas Chromatography/Mass Spectrometry for perfume’s VOCs analysis. • Perfume’s discrimination, from textile extraction, was possible using PowerSorb®. • Fragrance’s trace remains a simplified model of olfactory traces.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".