Raman Spectroscopy to Enhance Investigative Lead Information in Automotive Clearcoats.

A new method to determine the make and model of a vehicle from an automotive paint sample recovered at the crime scene of a vehicle-related fatality such as a hit-and-run using Raman microscopy has been developed. Raman spectra were collected from 118 automotive paint samples from six General Motors (GM) vehicle assembly plants to investigate the discrimination power of Raman spectroscopy for automotive clearcoats using a genetic algorithm for pattern recognition that incorporates model inference and sample error in the variable selection process. Each vehicle assembly plant pertained to a specific vehicle model. The spectral region between 1802 and 697 cm-1 was found to be supportive of the discrimination of these six GM assembly plants. By comparison, only one of the six automotive assembly plants could be differentiated from the other five assembly plants using Fourier transform infrared spectroscopy (FT-IR), which is the most widely used analytical method for the examination of automotive paint) and the genetic algorithm for pattern recognition. The results of this study indicate that Raman spectroscopy in combination with pattern recognition methods offers distinct advantages over FT-IR for the identification and discrimination of automotive clearcoats.

Analysis of Automotive Paint Smears Using Attenuated Total Reflection Infrared Microscopy.

Paint smears represent a type of automotive paint sample found at a crime scene that is problematic for forensic automotive paint examiners to analyze as there are no reference materials present in automotive paint databases to generate hit-lists of potential suspect vehicles. Realistic paint smears are difficult to create in a laboratory and have also proven challenging to analyze because of the mixing of the various automotive paint layers. A procedure based on an impact tester has been developed to create smears to simulate paint transfer between vehicles during a collision. Data collected from 24 original equipment manufacturer (OEM) paints in simulated collisions using an impact tester with a steel (inert) substrate to simulate vehicle to vehicle collisions shows that attenuated total reflection infrared microscopy can isolate individual paint layers. For each OEM paint sample, the corresponding smear obtained was dependent upon the conditions used. By varying these conditions, the number of distinct layers obtained could be tuned for each of the OEM paints investigated. Furthermore, the IR spectrum of each layer extracted from the paint smear using alternating least squares was found to compare favorably to an in-house OEM paint infrared spectral library for each layer as the correct match (make and model of the vehicle from which the smear originated) was always found as a top five hit in the hit-list. The results of this study indicate that paint smears developed using an impactor can serve as the basis of realistic proficiency tests for forensic laboratories.

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

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.