Adaptive Machine Learning for Electronic Nose-Based Forensic VOC Classification.

ABSTRACT: Odor detection as forensic evidence is an emerging and transformative approach for identifying individuals in cases where conventional techniques- such as DNA profiling, fingerprints, or gas chromatography-mass spectroscopy- are insufficient. Key challenges include distinguishing between living and deceased individuals, differentiating human from animal remains, and estimating postmortem intervals. While specially trained dogs remain the gold standard in some contexts, there is growing demand for faster, scalable, and cost-effective alternatives. Here, a bio-inspired electronic nose comprising 32 metal-oxide sensors, integrated with machine learning, is presented as a non-invasive, real-time, and reliable tool for volatile organic compound profiling. The system accurately classifies postmo

SUBMITTER: Shtepliuk I 

PROVIDER: S-EPMC12463043 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature