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 the situation of 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. This imaging approach to IR analysis of automotive paint, 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
A previously published study featuring an attenuated total reflection (ATR) simulation algorithm that mitigated distortions in ATR spectra was further investigated to evaluate its efficacy to enhance searching of infrared (IR) transmission libraries. In the present study, search prefilters were developed from transformed ATR spectra to identify the assembly plant of a vehicle from ATR spectra of the clear coat layer. A total of 456 IR transmission spectra from the Paint Data Query (PDQ) database that spanned 22 General Motors assembly plants and served as a training set cohort were transformed into ATR spectra by the simulation algorithm. These search prefilters were formulated using the fingerprint region (1500 cm-1 to 500 cm-1). Both the transformed ATR spectra (training set) and the experimental ATR spectra (validation set) were preprocessed for pattern recognition analysis using the discrete wavelet transform, which increased the signal-to-noise of the ATR spectra by concentrating the signal in specific wavelet coefficients. Attenuated total reflection spectra of 14 clear coat samples (validation set) measured with a Nicolet iS50 Fourier transform IR spectrometer were correctly classified as to assembly plant(s) of the automotive vehicle from which the paint sample originated using search prefilters developed from 456 simulated ATR spectra. The ATR simulation (transformation) algorithm successfully facilitated spectral library matching of ATR spectra against IR transmission spectra of automotive clear coats in the PDQ database.