In this study, regression and classification chemometrical algorithms were combined to achieve effective discrimination of pure body fluids from their binary mixtures.
Near-infrared (NIR) Raman spectroscopy has become widespread in various areas of the analytical study of materials, including forensic science. The nondestructive character, high selectivity, and possibility to explore a variety of materials make NIR Raman a valuable tool for the identification of body-fluid traces. The current study combined regression and classification chemometrical algorithms to achieve effective discrimination of pure body fluids from their binary mixtures. Raman spectra of dried blood, semen, and their mixtures in different ratios, collected in an automatic mapping manner, were used as a model system. The established detection limit for minor contributors was as low as a few percent. The proposed methodology takes into account the intrinsic heterogeneity of blood and semen and their variations between donors, and it potentially can be applied on other mixtures, including those which are of interest to forensic specialists. (publisher abstract modified)
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