Identification of semen traces at a crime scene through Raman spectroscopy and machine learning

Biological fluid stains can be instrumental in solving crimes. Identification of semen can help reconstruct events in sexual assault cases and identify suspects via DNA profiling. Current methods for semen identification suffer from limitations, including destruction of the sample and potential false positives. One of the main unsolved issues is the elimination of underlying substrate interference. In this paper, chemometric approaches were developed to isolate and identify a biofluid stain on interfering substrates using Raman spectroscopy. The first approach, called Multivariate Curve Resolution with the Addition Method, combines the standard addition method with multivariate curve resolution. The second one uses a criterion based on reducing the spectrum complexity when a spectral component is removed from a Raman spectrum of a multi-component sample entirely. The results demonstrate the superiority of the first approach relative to the second for both small volume fraction of the fluid stain compared to the substrate and random noise.