Adaptive Machine Learning for Electronic Nose-Based Forensic VOC Classification

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 postmortem versus antemortem human biosamples (98.1%), discriminates human from animal tissue (97.2%), and estimates postmortem intervals with high temporal resolution. This study introduces a robust AI-driven olfactory platform for forensic scent detection, highlighting its potential to complement or replace traditional methods. By coupling volatilome analysis with sensor miniaturization and algorithmic refinement, this approach lays the groundwork for next-generation forensic diagnostics and odor-based biomarker discovery.