Human and non-human bone identification using FTIR spectroscopy

Int J Legal Med. 2019 Jan;133(1):269-276. doi: 10.1007/s00414-018-1822-8. Epub 2018 Mar 16.

Abstract

Human and non-human identification of unknown skeletal remains is of great importance in forensic and anthropologic contexts. However, the traditional morphological methods for bone species identification are subjective or time-consuming. Here, we utilized Fourier transform infrared (FTIR) spectroscopy and chemometric methods to determinate the spectral variances between human and non-human (i.e., pig, goat, and cow) bones. To simulate real forensic situations as much as possible, fresh, boiled, and decomposed bones were included in this study. Principal component analysis (PCA) results illustrated pig bones were more sensitive to the environmental and external factors than other species studied in this work. Thus, pig bone might not be a suitable proxy for human bone in the study of postmortem changes. More importantly, score plots of PCA results showed clear separation with a slight overlap between the human and non-human fresh bones, but it failed to distinguish the boiled and decomposed bones. Then, partial least squares discriminant analysis (PLS-DA) was employed, and both internal and external validations were conducted to assess its classification ability, which resulted in 99.72 and 99.53% accuracy, respectively. According to the loading plots of PCA and PLS-DA, the spectral diversity was mainly due to the inorganic portion (i.e., carbonates and phosphates), which can remain relatively stable under various conditions. As such, our results illustrate that FTIR spectroscopy could serve as a reliable tool to assist in bone species determination and also has great potential in real forensic cases with natural conditions.

Keywords: Chemometrics; FTIR spectroscopy; Human bone identification; PCA; PLS-DA.

MeSH terms

  • Animals
  • Bone and Bones / chemistry*
  • Cattle
  • Discriminant Analysis
  • Forensic Anthropology
  • Goats
  • Humans
  • Principal Component Analysis
  • Species Specificity
  • Spectroscopy, Fourier Transform Infrared*
  • Swine