title: Determining Gender by Raman Spectroscopy of a Bloodstain.
abstract: The development of novel methods for forensic science is a constantly growing area of modern analytical chemistry. Raman spectroscopy is one of a few analytical techniques capable of nondestructive and nearly instantaneous analysis of a wide variety of forensic evidence, including body fluid stains, at the scene of a crime. In this proof-of-concept study, Raman microspectroscopy was utilized for gender identification based on dry bloodstains. Raman spectra were acquired in mapping mode from multiple spots on a bloodstain to account for intrinsic sample heterogeneity. The obtained Raman spectroscopic data showed highly similar spectroscopic features for female and male blood samples. Nevertheless, support vector machines (SVM) and artificial neuron network (ANN) statistical methods applied to the spectroscopic data allowed for differentiating between male and female bloodstains with high confidence. More specifically, the statistical approach based on a genetic algorithm (GA) coupled with an ANN classification showed approximately 98% gender differentiation accuracy for individual bloodstains. These results demonstrate the great potential of the developed method for forensic applications, although more work is needed for method validation. When this method is fully developed, a portable Raman instrument could be used for the infield identification of traces of body fluids and to obtain phenotypic information about the donor, including gender and race, as well as for the analysis of a variety of other types of forensic evidence.
authors.affiliation: Department of Chemistry, University at Albany, SUNY , 1400 Washington Avenue, Albany, New York 12222, United States.
authors.forename: Aliaksandra
authors.initials: A
authors.lastname: Sikirzhytskaya
authors.affiliation: Department of Chemistry, University at Albany, SUNY , 1400 Washington Avenue, Albany, New York 12222, United States.
authors.forename: Vitali
authors.initials: V
authors.lastname: Sikirzhytski
authors.affiliation: Department of Chemistry, University at Albany, SUNY , 1400 Washington Avenue, Albany, New York 12222, United States.
authors.forename: Igor K
authors.identifier: 0000-0002-6504-531X
authors.initials: IK
authors.lastname: Lednev
mesh_terms: D001826:Body Fluids / Q000737:chemistry*; D016000:Cluster Analysis; D005260:Female; D005554:Forensic Medicine; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D013059:Spectrum Analysis, Raman; D060388:Support Vector Machine