PowerSorb® for forensic investigation of VOC traces: Application on perfume traces
Why this work is in the frame
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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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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it