Analysis of sexual assault evidence: statistical classification of condoms by ambient mass spectrometry
Bibliographic record
Abstract
Desorption electrospray ionization mass spectrometry (DESI-MS) and easy ambient sonic-spray ionization mass spectrometry (EASI-MS) are employed here in the forensic analysis of chemical compounds found in condoms and relative traces, and their analytical performances are compared. Statistical analysis of data obtained from mass spectra only was applied in order to obtain classification rules for distinguishing ten types of condoms. In particular, two supervised chemometric techniques [linear discriminant analysis (LDA) and soft independent modeling of class analogy (SIMCA)] were carried out on absolute and relative intensity values to test the performances of statistical models in terms of predictive capacity. The achieved classification of samples was excellent because of the high prediction percentages of the method used both for DESI and EASI mass spectrometry analyses, confirming these two as potential ambient ionization techniques for forensic analyses in case of sexual assault crimes. EASI-MS showed 99% prediction ability for LDA using relative data and 100% prediction ability for SIMCA using both absolute and relative ones, while DESI showed 94% prediction ability for both LDA and SIMCA. The absence of any sample preparation technique gives advantages in terms of sample preservation and reduced contamination, allowing successive analyses to be performed on the same sample by other techniques.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".