Knowing the time since deposition (TSD) of an evidentiary bloodstain is highly desired in forensics, yet it can be extremely complicated to accurately determine in practice. Although there have been numerous attempts to solve this problem using a variety of different techniques, currently, no established, well-accepted method exists. Here, a Raman spectroscopic approach was developed for determining the age of bloodstains up to 1 week old. Raman spectroscopy, along with two-dimensional correlation spectroscopy (2D CoS) and statistical modeling, was used to analyze fresh bloodstains at ten time points under ambient conditions. The 2D CoS results indicate a high correlation between several Raman bands and the age of a bloodstain. A regression model was built to provide quantitative predictions of the TSD, with cross-validated root mean squared error and R (2) values of 0.13 and 0.97, respectively. It was determined that a "new" (1 h) bloodstain could be easily distinguished from older bloodstains, which is very important for forensic science in helping to establish the relevant association of multiple bloodstains. Additionally, all bloodstains were confirmatively identified as blood by comparing the experimentally measured spectra to multidimensional body fluid spectroscopic signatures of blood, saliva, semen, sweat, and vaginal fluid. These results demonstrate that Raman spectroscopy can be used as a nondestructive analytical tool for discriminating between bloodstains on the scale of hours to days. This approach shows promise for immediate practical use in the field to predict the TSD with a high degree of accuracy. Graphical Abstract Bloodstain aging over time illustrating naturally ocurring processes.

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For decades Raman spectroscopy has been used as a specialized tool in a variety of fields. Forensic science is one discipline in particular that has greatly benefited from the application of Raman spectroscopic research. Here, studies using Raman spectroscopy for analyzing criminalistic evidence, including body fluids, human tissue, bones, gunshot residue, fibers, and hair, are reviewed. Many of these methods demonstrate the significance of incorporating chemometrics for data analysis to provide more reliable and quantitative results with associated errors. Currently, there are only a few branches of forensic science that use Raman spectroscopy in practice, despite its wide range of advantages. However, many more applications have been developed in the research setting and are presented here. These studies showcase the versatility and dependability of Raman spectroscopy, specifically for criminalistic purposes. The advantages of developing and transitioning these techniques to be used in practice are also discussed, including the tremendous benefits of portable instrumentation. Raman spectroscopy can easily become an established analytical technique for many criminalistics disciplines because it allows for obtaining real‐time, rapid, impartial, statistically confident results. Consequently, the improvements to forensic science as a whole will be revolutionary. Copyright © 2015 John Wiley & Sons, Ltd.

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.

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