The human body is an important piece of evidence in forensic death investigations and it is necessary for law enforcement authorities to have tools capable of locating and evaluating it. In cases where the body is concealed from view, like when it is buried underground, investigators must be able to efficiently and reliably search for and recover it. The body provides clues about the post-mortem interval, or time since death, which can inform and greatly impact investigations. Many different solutions to these problems have been proposed. This paper proposes the use of biogenic amines, specifically putrescine and cadaverine, as indicators of decomposition to locate corpses. Secondarily, the use of these chemicals as indicators of PMI is discussed. Biogenic amines are byproducts of the decomposition of proteins by bacterial putrefaction and are associated with the scent of death. This paper analyzes the current literature and published data regarding the patterns of putrescine and cadaverine production with respect to post-mortem interval and temperature. This analysis produced potential regression models using these variables to calculate the expected concentration of putrescine or cadaverine. The models show a range of R2 from 68.34-81.72% and standard error ranging from 130.391 μg/g to 184.314 μg/g. After removing outlier samples, the more accurate models produced a mean percent error of 811.84% for putrescine concentration and 272.61% for cadaverine concentration, with higher error seen at low concentrations. Despite these high error rates, these models are informative about the patterns of putrescine and cadaverine production during decomposition and the potential sources of error, including non-homogeneous data sources. These models may be useful for estimating biogenic amine concentrations for detecting corpses; however, the model is not accurate enough to predict PMI. Overall, this study produced promising results for the use of biogenic amines for detecting decomposition despite the limitations presented by the nonstandardized data and supports further work on this area to improve the predictive capabilities of the models.
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