Principal Component Regression for Forensic Age Determination Using the Raman Spectra of Teeth

Author:

Osmani Aziz1,Par Matej23ORCID,Škrabić Marko45ORCID,Vodanović Marin6,Gamulin Ozren45ORCID

Affiliation:

1. Community Health Center “Kutina”, Kutina, Croatia

2. Department of Conservative and Preventive Dentistry, Center for Dental Medicine, University of Zurich, Zurich, Switzerland

3. Department of Endodontics and Restorative Dentistry, School of Dental Medicine, University of Zagreb, Zagreb, Croatia

4. Department of Physics and Biophysics, School of Medicine, University of Zagreb, Zagreb, Croatia

5. Center of Excellence for Advanced Materials and Sensing Devices, Research Unit New Functional Materials, Zagreb, Croatia

6. Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Zagreb, Croatia

Abstract

Raman spectra of mineralized tooth tissues were used to build a principal component regression (PCR) age determination model for forensic application. A sample of 71 teeth was obtained from donors aging from 11 to 76 years. No particular selection criteria were applied; teeth affected with various pathological processes were deliberately included to simulate a realistic forensic scenario. In order to comply with the nondestructive specimen handling, Raman spectra were collected from tooth surfaces without any previous preparation. Different tooth tissues were evaluated by collecting the spectra from three distinct sites: tooth crown, tooth neck, and root apex. Whole recorded spectra (3500–200 cm−1) were used for principal component analysis and building of the age determination model using PCR. The predictive capabilities of the obtained age determination models varied according to the spectra collection site. Optimal age determination was attained by using Raman spectra collected from cementum at root apex (R2 values of 0.84 and 0.71 for male and female donors, respectively). For optimal performance of that model, male and female donors had to be analyzed separately, as merging both genders into a single model considerably diminished its predictive capability (R2 = 0.29).

Publisher

SAGE Publications

Subject

Spectroscopy,Instrumentation

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