Detection of exogenous substances in latent fingermarks by silver-assisted LDI imaging MS: perspectives in forensic sciences
Bibliographic record
Abstract
For over one hundred years, the fingerprint has reigned as one of the most trusted pieces of forensic evidence for suspect identification. In the last few decades, the modernization of chemical analysis technologies led scientists to explore new possibilities to further analyse fingermarks sampled from a crime scene. Indeed, the detection of chemicals a suspect has been in contact with before or during the crime can provide valuable insights into criminal investigations. In this regard, imaging mass spectrometry (IMS) has shown to be a powerful tool for the analysis of fingermarks by combining suspect identification and the detection of numerous endogenous and exogenous compounds. A novel approach developed in our laboratory, silver-assisted laser desorption ionization (AgLDI), was adopted to allow for the chemical analysis of latent fingermarks left on nonconductive surfaces (such as paper, cardboard, plastic and forensic lifting tape) with a time-of-flight mass spectrometer. In this study, we continue to evaluate the potential of AgLDI IMS to provide circumstantial evidence by detecting exogenous substances. We first demonstrate that owner-specific chemical signatures can be recovered from fingermarks based on the presence of several cosmetics and personal care products. We then show the possibility of detecting and imaging fingermarks containing three common illicit drugs, namely tetrahydrocannabinol, cocaine and heroin. Finally, we demonstrate that the methodology also allows us to successfully image bloody fingermarks after appropriate forensic enhancement treatments. Overall, we believe that AgLDI IMS has significant potential that could positively contribute to forensic investigations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".