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Estimating postmortem interval for human cadavers in a sub‐tropical climate using UV‐Vis‐near‐infrared Spectroscopy
Estimating postmortem interval (PMI) of surface found skeletal remains is challenging. This novel study used UV‐Vis‐NIR spectroscopy to scan soil collected from cadaver decomposition islands (CDIs) ranging from 15‐ to 963‐d postmortem and control soils. A decomposition product spectra model (DPS mod...
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Published in: | Journal of forensic sciences 2021-01, Vol.66 (1), p.190-201 |
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Main Authors: | , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Estimating postmortem interval (PMI) of surface found skeletal remains is challenging. This novel study used UV‐Vis‐NIR spectroscopy to scan soil collected from cadaver decomposition islands (CDIs) ranging from 15‐ to 963‐d postmortem and control soils. A decomposition product spectra model (DPS model) was constructed by deducting the control soil spectra from the CDI soil spectra for the estimation of postmortem indices: PMI (d), ADD4, ADD10, and ADD20. The DPS model (n = 55) was calibrated and subjected to a full cross‐validation. Calibration R2 and RPD for the DPS model ranged from 0.97 to 0.99 and from 6.1 to 9.9, respectively, for the four postmortem interval indices. Validation R2 and RPD for the DPS model ranged from 0.73 to 0.80 and from 1.9 to 2.2, respectively. The DPS model estimated postmortem intervals for three test CDIs in a clay soil under perennial grassland (test set 1; n = 3) and six CDIs in a sandy soil under a loblolly pine forest (test set 2; n = 6). Test set 1 had PMI prediction ranges from −69 to −117 days, −796 to +832 ADD4, +552 to +2672 ADD10, and −478 to −20 ADD20 of observed PMI. Test set 2 PMI prediction ranged from −198 to −65 days, −9923 to +2629 ADD4, −6724 to +1321 ADD10, and −2850 to +540 ADD20 of observed PMI. Test set 2 had poor predictions for two CDIs, for all measures of postmortem indices resulting in discussion of sampling depth, effect of body mass index (BMI), and scavenging. |
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ISSN: | 0022-1198 1556-4029 |
DOI: | 10.1111/1556-4029.14579 |