Transmission Infrared Imaging Microscopy and Multivariate Curve Resolution Applied to the Forensic Examination of Automotive Paints

• NCJ Number: 252102
• Author(s): Francis Kwofie, Undugodage Don Nuwan Perera, Matthew D. Allen, Barry K. Lavine
• Date Published: August 2018
• Length: 8 pages
• Agencies: NIJ-Sponsored

Annotation

The imaging approach to infrared (IR) analysis of automotive paint developed and tested by this project not only saves time and eliminates the need to analyze each layer separately, but also ensures that the final spectrum of each layer is “pure” and not a mixture.

Abstract

In the forensic examination of automotive paint, each layer of paint is analyzed individually by infrared (IR) spectroscopy. Laboratories in North America typically hand section each layer and present each separated layer to the spectrometer for analysis, which is time consuming. In addition, sampling too close to the boundary between adjacent layers can pose a problem as it produces an IR spectrum that is a mixture of the two layers. Not having a “pure” spectrum of each layer will prevent a meaningful comparison between each paint layer or in searching an automotive database, will prevent the forensic paint examiner from developing an accurate hit list of potential suspects. These two problems can be addressed by collecting concatenated IR data from all paint layers in a single analysis by scanning across the cross sectioned layers of the paint sample using a FTIR imaging microscope. Decatenation of the IR data is achieved by multivariate curve resolution using a Varimax extended rotation to select the starting point (i.e., initial estimates of the reconstructed IR spectra of each layer) for the alternating least squares algorithm to obtain a pure IR spectrum of each automotive paint layer. Comparing the reconstructed IR spectrum of each layer against the IR spectral library of the PDQ database demonstrated that it is possible to identify the correct model of the vehicle from these reconstructed spectra. (Publisher abstract modified)