Natural traces in forensic investigations: Integrating biological trace analysis and artificial intelligence in forensic investigations
Publish Date: 2026-07-09 09:37:00
Source Domain: www.openaccessgovernment.org
The Natural Traces Consortium is transforming forensic science by expanding the range of evidence sources to include biological and environmental materials across 11 key areas
Estimating time since death using forensic entomology (Goethe University Frankfurt)
Insects can be important indicators of post-mortem processes, providing valuable information on body movement, the potential cause of death, and the period during which a person may have been neglected prior to death. Most importantly, they serve as a reliable tool for estimating the time since death, even when discovery occurs days, weeks, or even later. We combine advanced methods in biology, ecology, and chemistry to reconstruct colonisation timelines and generate accurate estimates of post- mortem intervals. These approaches are applied to the analysis of 100–150 insect-infested bodies annually.
Identifying mixed biological traces using eDNA and long- read metagenomics (Eötvös Loránd University, Budapest)
Non-human DNA traces, such as environmental DNA, are increasingly used to analyse biological evidence with mixed sources, but their interpretation remains challenging due to complex compositions and limitations in current methodologies. We develop new sampling and molecular approaches based on long-read metagenomics to improve the identification and characterisation of mixed biological traces deposited in the environment. The resulting genetic data are used to support the development of likelihood ratio–based statistical frameworks for evaluating the evidential weight of mixed-source biological samples.
Using likelihood ratio and AI systems for standardised and interpretable evidence evaluation (Universidad Autonoma de Madrid)
Forensic interpretation increasingly relies on likelihood ratio (LR) frameworks aligned with Standard ISO 21043, while rapidly evolving artificial intelligence (AI) technologies are being integrated into evidence evaluation. However, the complexity and diversity of…
