Predicting the Time of the Crime: Bloodstain Aging Estimation for Up to Two Years

• NCJ Number: 252636
• Journal: Forensic Chemistry Volume: 5 Dated: September 2017 Pages: 1-7
• Author(s): Kyle C. Doty; Claire K. Muro; Igor K. Lednev
• Date Published: September 2017
• Length: 7 pages
• Grant Number(s): 2014-DN-BX-K016; 2015-R2-CX-0021
• Agencies: NIJ-Sponsored

Annotation

This article presents research on blood chemistry and the biomolecular changes that occur as bloodstains age.

Abstract

The authors highlight: 1) actual bloodstain age correctly predicted with 70% accuracy for up to two years; 2) bloodstain age can be distinguished between hours, days, weeks, months or years; 3) spectral changes are well-correlated to known, natural, kinetic biochemical processes; and 4) under the same conditions, healthy donors blood ages in similar way, regardless of sex. The chemistry of blood and the biomolecular changes that occur as a bloodstain ages are both inherently intricate, but can be monitored using specific analytical techniques. It has been shown that bloodstains are a rich source of information, which can be used to help in solving crimes. Particularly, the time since deposition (TSD) can be estimated by analyzing bloodstains and extracting information related to the natural chemical processes that occur as bloodstains age. This work summarizes a proof-of-concept study demonstrating the effectiveness of using Raman spectroscopy to nondestructively analyze bloodstains, and probe for specific kinetic changes in aged bloodstains for up to two years. As an initial step, bloodstains were identified as blood, and not a different body fluid, using a recently developed classification modeling technique. An overall success rate of 89% was demonstrated for predicting the identity of all stains as blood, with 100% correct blood identification for stains aged up to one month. The observed changes in the spectra over time were consistent with the known biochemical processes occurring as blood ages naturally, and those variations were sufficient enough to allow for differentiation and TSD predictions on the scale of hours to years. Specifically, TSD predictions were performed using partial least squares regression (PLSR) and principal component regression (PCR) analyses; where root mean squared errors of prediction of 0.29 and 0.31, respectively, were obtained. These errors correspond to an overall accuracy of ­70% for the models to correctly predict the TSD at each time point.