Determining time since deposition of latent fingerprints on forensic adhesive tape using ultrafast DESI-MS and machine learning

Nora Rajs, Yinon Harush-Brosh, Ron Raisch, Ravit Yakobi Arancibia, Amani Zoabi, Guy Nevet Golan, Moshe Shpitzen, Sarena Wiesner, Michal Levin-Elad, Tommy Kaplan, Katherine Margulis

Research output: Contribution to journalArticlepeer-review

Abstract

Fingerprints provide indisputable forensic evidence for establishing identity. Latent fingerprints, often visualized with black magnetic powder and recovered with adhesive tape, can be matched to police databases for identification. However, determining the time since deposition (TSD) is crucial to temporally tie the fingerprints to the crime. Despite extensive efforts, no reliable method exists for determining TSD. This study presents a workflow for directly dating fingerprints using ultrafast 2-dimensional desorption electrospray ionization mass spectrometry (DESI-MS). The fingerprints are analyzed directly from a forensic tape after development with magnetic powder. This method aims to enable dating of fingerprints collected from virtually any non-porous surface. The study involved 744 fingerprints from 330 volunteers, aged up to 15 days under various conditions. Data analysis using the XGBoost and SMOTE algorithms achieved a correlation of 0.54 (p-value < 1e−5) between TSD prediction and true TSD, achieving 83.3% accuracy in distinguishing between 0-4 days and 10–15 days old prints. Key imaging parameters, such as DESI-MS scan rate, mass range, scan area, spatial resolution, and imaging mode, were optimized to enhance age determination precision and support rapid processing within forensic workflows. This research, conducted in collaboration between police forensic units and an academic institution, integrates seamlessly into practical forensic applications.

Original languageEnglish
Article number18413
JournalScientific Reports
Volume15
Issue number1
DOIs
StatePublished - Dec 2025

All Science Journal Classification (ASJC) codes

  • General

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