
Researchers have optimized a headspace sorptive extraction (HSSE) method coupled with gas chromatography-mass spectrometry (GC-MS) to analyze human scent traces left on clothing. By extracting volatile and semi-volatile organic compounds and applying supervised machine learning algorithms, such as support vector machines (SVM) and partial least squares-discriminant analysis (PLS-DA), to the GC-MS data, the team achieved 100% accuracy in gender discrimination. This combination of optimized extraction, GC-MS analysis, and machine learning provides a rapid and automated forensic screening technique to help narrow down suspect or victim profiles based on trace evidence.




























