Transmission Infrared Microscopy and Machine Learning Applied to the Forensic Examination of Original Automotive Paint

Abstract

Alternate least squares (ALS) reconstructions of the infrared (IR) spectra of the individual layers from original automotive paint were analyzed using machine learning methods to improve both the accuracy and speed of a forensic automotive paint examination. Twenty-six original equipment manufacturer (OEM) paints from vehicles sold in North America between 2000 and 2006 served as a test bed to validate the ALS procedure developed in a previous study for the spectral reconstruction of each layer from IR line maps of cross-sectioned OEM paint samples. An examination of the IR spectra from an in-house library (collected with a high-pressure transmission diamond cell) and the ALS reconstructed IR spectra of the same paint samples (obtained at ambient pressure using an IR transmission microscope equipped with a BaF2 cell) showed large peak shifts (approximately 10 cm−1) with some vibrational modes in many samples comprising the cohort. These peak shifts are attributed to differences in the residual polarization of the IR beam of the transmission IR microscope and the IR spectrometer used to collect the in-house IR spectral library. To solve the problem of frequency shifts encountered with some vibrational modes, IR spectra from the in-house spectral library and the IR microscope were transformed using a correction algorithm previously developed by our laboratory to simulate ATR spectra collected on an iS-50 FT-IR spectrometer. Applying this correction algorithm to both the ALS reconstructed spectra and in-house IR library spectra, the large peak shifts previously encountered with some vibrational modes were successfully mitigated. Using machine learning methods to identify the manufacturer and the assembly plant of the vehicle from which the OEM paint sample originated, each of the twenty-six cross-sectioned automotive paint samples was correctly classified as to the “make” and model of the vehicle and was also matched to the correct paint sample in the in-house IR spectral library.

Raman Spectroscopy to Enhance Investigative Lead Information in Automotive Clearcoats

“…Previous studies have shown that restricting the analysis to the spectral region corresponding to only the binder in an OEM clearcoat is sufficient to determine the make and model of the vehicle. [9][10][11][12]37 Three specific questions were addressed in this study: (i) Can discriminants be developed from the Raman and IR spectra that differentiate the six assembly plants? (ii) Can these discriminants predict the assembly plant of an unknown paint sample?…”

Innovative Vibrational Spectroscopy Research for Forensic Application

“…Applying transmission IR microscopy for analyzing original automotive paint samples, Kwofie et al also conducted research on automobile paints. 83 Their focus was on 26 samples from 2000 to 2006 manufactured in North America. This method included cross sectioning the paint samples to see all sections at once and using IR line maps to collect spectra from each layer.…”

“…These studies have shown that IR spectral data provides significant amounts of chemical information that, when paired with statistical analyses, can be used to classify and distinguish among samples. Applying transmission IR microscopy for analyzing original automotive paint samples, Kwofie et al also conducted research on automobile paints . Their focus was on 26 samples from 2000 to 2006 manufactured in North America.…”

Interpol review of paint, tape, and glass evidence 2019–2022

“…Superior library matches were feasible with the proposed method compared to embedding the paint in epoxy for microtome cross-sectioning. In a follow-up study, the same group evaluated the FTIR and machine learning algorithms for the make/model classification of OEM automotive paints [ 34 ]. The validation dataset consisted of 4-layer paints from the RCMP collection from 26 OEM vehicles (2000–2006).…”