Design of experiments (DOE) analysis of the effects of environmental conditions on bloodstain degradation using spectroscopic methods
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Experimental Protocol to Investigate Particle Aerosolization of a Product Under Abrasion and Under Environmental Weathering
Published on: September 16, 2016
Laurianne Huard1, Frank Crispino2, Cyril Muehlethaler2
1University of Quebec at Trois-Rivières, Canada; Groupe de Recherche en Science Forensique (GRSF), Trois-Rivières, Canada.
Abstract:
Blood is one of the most common types of trace found at crime scenes. However, from a forensic point of view, the potential of blood traces is still not fully exploited, as there is as yet no reliable technique for dating blood traces found at scenes. The same difficulties are cited in the literature for published research on blood traces: the mechanisms of blood degradation are known and validated, but traces found at scenes are not controlled samples. Traces therefore need to be evaluated in the light of this uncertainty and the factors that can influence the blood trace dating model. In the course of this project, we assessed the significance of the environmental effects of temperature (10-40°C), humidity (25-75 %) and radiation (none and maximum) using three different spectroscopic techniques: Raman spectroscopy, MicroNIR spectroscopy and hyperspectral imaging. The use of a climatic chamber with the addition of LED lamps (daylight and UV) enabled parameters to be controlled during a 3-day aging period for each sample. By means of a 2-level experimental screening design of the three factors, we were able to observe complementarity between the methods used. Raman spectroscopy highlighted the influence of temperature, MicroNIR spectroscopy provided information on the influence of temperature and relative humidity, and hyperspectral imaging demonstrated the influence of temperature and the presence of radiation. These results provide a better understanding of the factors that cause the blood degradation model to deviate, enabling us to develop a more comprehensive model of these factors.
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