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Abstract Automobile paint chips are a crucial piece of trace evidence for forensic investigators. This is because automotive paints are composed of multiple layers, including the primer, basecoat, and clearcoat, each of which has its own chemical composition that can vary by vehicle make, model, year, and manufacturing plant. Thus, Fourier‐transform infrared (FTIR) spectral databases for automobile paint systems have been established to aid law enforcement in, for example, narrowing search parameters for a suspect's vehicle. Recently, car manufacturers have implemented primers on plastic substrates that are much thinner (~5 μm) than those on metal substrates, making it more difficult to manually separate for analyses. Here, we evaluated FTIR microspectroscopy (μ‐FTIR) and optical photothermal infrared spectroscopy (O‐PTIR) to chemically image cross sections of paint chips without manually separating the layers. For μ‐FTIR, transmission and transflection modes provided the highest quality spectra compared to reflection and μ‐ATR analyses. Point analysis was preferable to chemical imaging, as peaks were identified in the point (MCT) detector's lower spectral range that was below the imaging (FPA) detector's cutoff, such as those associated with titanium dioxide. Reduced spectral range can lead to a similar issue in O‐PTIR analyses depending on instrument configuration. However, its complementary Raman spectra showed strong titanium dioxide peaks, providing an alternate means of identification. Both techniques are likely to become more relevant as they are non‐destructive and avoid manual separation of the layers. O‐PTIR is particularly well‐suited for analysis of the thin primer layer due to its superior spatial resolution.
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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–1was 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.
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- Award ID(s):
- 2003867
- PAR ID:
- 10437524
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Applied Spectroscopy
- Volume:
- 77
- Issue:
- 9
- ISSN:
- 0003-7028
- Format(s):
- Medium: X Size: p. 1064-1072
- Size(s):
- p. 1064-1072
- Sponsoring Org:
- National Science Foundation
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