Raman spectroscopy for forensic bloodstain identification: Method validation vs. environmental interferences

Abstract

The identification of body fluid (BF) stains at a crime scene is an important step of a crime scene investigation since traces of BFs are typically the main source of DNA evidence. Raman spectroscopy is a new emerging method for identifying and differentiating between BF stains. As any new analytical method developed for identification and differentiation of classes of materials, it needs to be evaluated for potential environmental interferences (EIs). This study specifically focused on proving that Raman spectroscopy is not susceptible to false positive (FP) assignments from EIs specific to blood. Twenty-four EI substances that may be misclassified as blood due to either their appearance, or known to provide a FP result with forensic crime scene tests for blood identification were analyzed in this study. The spectra of the twenty-four substances were tested against a recently developed support vector machines discriminant analysis (SVMDA) classification model used for BF differentiation. The SVMDA model identified all substances as not peripheral blood, but some of the substances were predicted as one of the other four BFs. Therefore, random forests (RF) analysis was employed to build a new, more robust, classification method to overcome this limitation. Using this approach, complete separation was achieved based on a classification probability threshold of 70%. This method can also be used in the future to test potential EI substances specific to other common BFs.