Abstract

Hair and fibers are common forms of trace evidence found at crime scenes. The current methodology of microscopic examination of potential hair evidence is absent of statistical measures of performance, and examiner results for identification can be subjective. Here, attenuated total reflection (ATR) Fourier transform-infrared (FT-IR) spectroscopy was used to analyze synthetic fibers and natural hairs of human, cat, and dog origin. Chemometric analysis was used to differentiate hair spectra from the three different species, and to predict unknown hairs to their proper species class, with a high degree of certainty. A species-specific partial least squares discriminant analysis (PLSDA) model was constructed to discriminate human hair from cat and dog hairs. This model was successful in distinguishing between the three classes and, more importantly, all human samples were correctly predicted as human. An external validation resulted in zero false positive and false negative assignments for the human class. From a forensic perspective, this technique would be complementary to microscopic hair examination, and in no way replace it. As such, this methodology is able to provide a statistical measure of confidence to the identification of a sample of human, cat, and dog hair, which was called for in the 2009 National Academy of Sciences report. More importantly, this approach is non-destructive, rapid, can provide reliable results, and requires no sample preparation, making it of ample importance to the field of forensic science.

© 2016 The Author(s)

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