A fast statistical method for analyzing spectroscopic data has been developed by US researchers to allow crime scene investigators and forensic scientists to more quickly and easily obtain a post-mortem interval on recovered skeletal remains.
The flesh is weak and once it has rotted away, the skeleton remains. However, there are few precise techniques that forensic scientists can use to determine the time since death quickly and easily with only bones to hand. In hot and humid environments the problem is even worse, shortening the time between death and the skeletalization process that renders the body opaque to conventional analytical time-of-death techniques without recourse to major lab-based analysis.
Now, chemist Kenneth Busch, co-director of the Center for Analytical Spectroscopy, at Baylor University, in Waco, Texas, and his team have exploited the fact that bones lose water and the proteins within bones decompose into their constituent amino acids over time. He and his colleagues have now tracked these changes using near-infrared (NIR) reflectance spectroscopy and ultraviolet-visible (UV/Vis) emission and absorption spectroscopy and then applied a statistical regression modelling approach, to correlate the changing spectra with the post-mortem interval (PMI). Their laboratory tests, they say, have an error rate as low as four days for bones that are 90 days old.
"In forensic investigations, establishing the time of death is a key piece of evidence," explains Busch, "Forensic scientists frequently categorize human remains in terms of their post-mortem interval (PMI), which is the time elapsed since a person died." He points out that in areas like Texas that have extreme climates with high heat and high humidity, skeletalization, or "excarnation", of a body happens relatively quickly. "Under these conditions, the determination of the PMI is frequently problematic because of the rapid decomposition of the tissues routinely used to determine PMI," adds Busch.
The researchers used 28 different pig femurs that were up to three-months old and applied the various spectroscopic techniques, which are sensitive to moisture and protein content to obtain data that could be interpreted in terms of PMI. The approach is entirely non-destructive so that they can test any skeletal remains without the need to physically remove samples.
The researchers found that the diffuse reflectance spectra of bones did not follow a straight line pattern as the bones age, so they segmented the data into three sets, which were then used to construct three statistical models of the aging process. They found that this approach could reduce the prediction error still further compared with the original 90-day model. A combination of the two approaches - a discriminant analysis model followed by a segmented regression model gave the optimal results.
"We do it over a certain set of wavelengths, then take all the data from our instrument and put it in a statistics program and analyze it in various ways," explains chemistry graduate student Patricia Diamond, "No one is doing the spectroscopic work we've done."
Busch and colleagues, Diamond (who presented the results), Marianna Busch, and Jody Dogra, revealed details of their technique at the annual meeting of the Federation of Analytical Chemistry and Spectroscopy Societies in October.
"In perfect conditions in the laboratory, the method looks very encouraging," explains Busch. "Once a regression model is built from spectral data, you could find out the age of the bones in a matter of minutes, rather than taking hours or days." He adds that, "Our method isn't absolute - we can just give a range - but once a regression model is built, the time it takes to determine the age of a bone is cut down significantly."
