Raman Spectroscopy of Blood for Species Identification

• Published in: Analytical chemistry (Washington) Vol. 86; no. 23; pp. 11628 - 11633
• Main Authors: McLaughlin, Gregory, Doty, Kyle C., Lednev, Igor K.
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 02.12.2014
• Subjects: Analytical chemistry; Animals; Blood; Blood Chemical Analysis; blood sampling; cats; chemical composition; Classification; Differentiation; Discriminant Analysis; dogs; Forensic science; forensic sciences; Human; Human subjects; Humans; Identification; Least-Squares Analysis; Raman spectroscopy; Samples; species identification; Species Specificity; spectral analysis; Spectroscopy; Spectrum analysis; Spectrum Analysis, Raman; Statistical analysis; Statistical methods; wildlife; Wildlife conservation
• ISSN: 0003-2700; 1520-6882; 1520-6882
• DOI: 10.1021/ac5026368

The species identification of a blood stain is an important and immediate challenge for forensic science, veterinary purposes, and wildlife preservation. The current methods used to identify the species of origin of a blood stain are limited in scope and destructive to the sample. We have previously demonstrated that Raman spectroscopy can reliably differentiate blood traces of human, cat, and dog (Virkler et al. Anal. Chem. 2009, 81, 7773−7777 ) and, most recently, built a binary model for differentiating human vs animal blood for 11 species integrated with human existence (McLaughlin et al. Forensic Sci. Int. 2014, 238, 91−95 ). Here we report a satisfactory classification of blood obtained from 11 animal classes and human subjects by statistical analysis of Raman spectra. Classification of blood samples was achieved according to each sample’s species of origin, which enhanced previously observed discrimination ability. The developed approach does not require the knowledge of a specific (bio)­chemical marker for each individual class but rather relies on a spectroscopic statistical differentiation of various components. This approach results in remarkable classification ability even with intrinsically heterogeneous classes and samples. In addition, the obtained spectroscopic characteristics could potentially provide information about specific changes in the (bio)­chemical composition of samples, which are responsible for the differentiation.