Blood is an extremely valuable form of evidence in forensic investigations, so proper analysis is critical. Because potentially miniscule amounts of blood traces can be found at a crime scene, having a method that is nondestructive and provides a substantial amount of information about the sample is ideal. In this study, Raman spectroscopy was applied with advanced statistical analysis to discriminate between Caucasian (CA) and African-American (AA) donors based on dried peripheral blood traces. Spectra were collected from 20 donors varying in gender and age. Support vector machines-discriminant analysis (SVM-DA) was used for differentiation of the two races. An outer loop subject-wise cross-validation (CV) method evaluated the performance of the SVM classifier for each individual donor from the training data set. The performance of SVM-DA, evaluated by the area under the curve (AUC) metric, showed 83 percent probability of correct classification for both races, and a specificity and sensitivity of 80 percent. This preliminary study shows promise for distinguishing between different races as contributors of human blood. The method has potential for real crime-scene investigation, providing rapid and reliable results, with no sample preparation, destruction, or consumption. (Publisher abstract modified)
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