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Application of Infrared Imaging and Chemometrics to Facilitate the Forensic Examination of Automotive Paints

NCJ Number
252775
Date Published
August 2018
Length
11 pages
Author(s)
Barry K. Lavine; Undugodage Don Nuwan Perera; Frances Kwofie; Kaushalya Dahal
Agencies
NIJ-Sponsored
Annotation
This is the final summary overview of a report on a project that sought to minimize the time necessary for forensic data collection on modern automotive paint systems that consist of multiple layers of paint, i.e., a clear coat over a color coat, which in turn is over a surface primer and e-coat layer.
Abstract
Since forensic laboratories analyze each layer of paint individually by Fourier Transform Infrared (FTIR) spectroscopy, time must be spent to hand-section each layer and then present each separated layer in the spectrometer for analysis. Sampling too close to the boundary between adjacent layers can produce an IR spectrum that is a mixture of two layers. Searching an automotive paint database without having a "pure spectrum" of each layer prevents a forensic paint examiner from developing an accurate hit list of potential suspects. The current project sought to improve current approaches to forensic automotive paint analysis by decreasing data collection times compared to current practices and aid in evidential significance assessment, both at the investigative-lead state and at the stage of courtroom testimony. The current project minimized the time necessary for data collection by collecting IR data from all layers in a single analysis by scanning across the cross-sectional layers of the paint sample using a FTIR microscope equipped with an imaging detector. Once the data has been collected, it can then undergo decatenation by using chemometrics to obtain a "pure" IR spectrum of each layer. This approach not only eliminates the need to analyze each layer separately, resulting in a considerable time savings, but can also ensure that the final spectrum of each layer is "pure" and not a mixture. The current project correctly classified 32 unembedded paint samples as to the manufacturer, line, and model of the vehicle from which the paint sample originated. 12 references

Date Created: April 15, 2019