Forensic analysis of latent fingermarks by silver‐assisted LDI imaging MS on nonconductive surfaces
Bibliographic record
Abstract
Silver-assisted laser desorption ionization (AgLDI) imaging mass spectrometry (IMS) has been demonstrated to be a useful technology for fingermark analysis allowing for the detection of several classes of endogenous as well as exogenous compounds. Ideally, in IMS analyses, the fingermarks are deposited under controlled conditions on metallized conductive target slides. However, in forensic investigations, fingermarks are often found on a variety of nonconductive surfaces. A sputtered silver layer renders the target surface conductive, which allows the analyses of insulating surfaces by time-of-flight IMS. Ultimately, the major consideration when developing analytical methods for the analysis of latent fingermarks is their capability to be incorporated within forensic standard operational procedures. To demonstrate the potential of AgLDI IMS for forensic applications, fingermarks deposited on nonconductive surfaces commonly found during an investigation, including paper, cardboard, plastic bags and lifting tape, were first revealed by the Sûreté du Québec by using forensic enhancement techniques prior to the IMS analyses. Numerous endogenous compounds including fatty acids, cholesterol, squalene, wax esters, triglycerides and several exogenous substances were detected and imaged. Here, we show that silver sputtering can provide visual enhancements of fingerprint patterns after FET procedures through different scenarios in which AgLDI IMS can contribute to forensic investigations. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".