Estimation of the late postmortem interval using FTIR spectroscopy and chemometrics in human skeletal remains

  • Qi Wang
  • , Yinming Zhang
  • , Hancheng Lin
  • , Shuai Zha
  • , Ruoxi Fang
  • , Xin Wei
  • , Shuanliang Fan
  • , Zhenyuan Wang

Research output: Contribution to journalArticlepeer-review

60 Scopus citations

Abstract

Due to a lack of reliable and accurate methods, determining the postmortem interval (PMI) of human skeletal remains is one of the most important and challenging tasks in forensic medicine. In this paper, we studied the changes to bone chemistry with increasing PMI in two different experimental conditions using Fourier transform infrared (FTIR) spectroscopy in conjunction with chemometrics methods Paired bone samples collected from 56 human corpses were buried (placed in soil) and unburied (exposed to the air) for intervals between 76 and 552 days. The results of principle component analysis (PCA) showed the chemical differences of these two cases had a significant influence on the rate of decomposition of the remains. Meanwhile, satisfactory predictions were performed by the genetic algorithm combined with partial least-squares (GA-PLS) with the root mean square errors of prediction (RMSEP) of 50.93 days for buried bones and 71.03 days for unburied bones. Moreover, the amide I region of proteins and the area around 1390 cm−1, which is associated with fatty acids, were identified with regular changes by GA-PLS and played an important role in estimating PMI. This study illustrates the feasibility of utilizing FTIR spectroscopy and chemometrics as an attractive alternative for estimating PMI of human remains and the great potential of these techniques in real forensic cases with natural conditions.

Original languageEnglish
Pages (from-to)113-120
Number of pages8
JournalForensic Science International
Volume281
DOIs
StatePublished - Dec 2017

Keywords

  • Bones
  • Fourier transform infrared spectroscopy
  • Genetic algorithm
  • Late postmortem interval
  • Partial least-squares
  • Principle component analysis

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