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Pattern recognition techniques have been developed to search the infrared (IR) spectral libraries of the paint data query (PDQ) database to differentiate between similar but nonidentical IR clear coat paint spectra. The library search system consists of two separate but interrelated components: search prefilters to reduce the size of the IR library to a specific assembly plant or plants corresponding to the unknown paint sample and a cross-correlation searching algorithm to identify IR spectra most similar to the unknown in the subset of spectra identified by the prefilters. To develop search prefilters with the necessary degree of accuracy, IR spectra from the PDQ database were preprocessed using wavelets to enhance subtle but significant features in the data. Wavelet coefficients characteristic of the assembly plant of the vehicle were identified using a genetic algorithm for pattern recognition and feature selection. A search algorithm was then used to cross-correlate the unknown with each IR spectrum in the subset of library spectra identified by the search prefilters. Each cross-correlated IR spectrum was simultaneously compared to an autocorrelated IR spectrum of the unknown using several spectral windows that span different regions of the cross-correlated and autocorrelated data from the midpoint. The top five hits identified in each search window are compiled, and a histogram is computed that summarizes the frequency of occurrence for each selected library sample. The five library samples with the highest frequency of occurrence are selected as potential hits. Even in challenging trials where the clear coat paint samples evaluated were all the same make (e.g., General Motors) within a limited production year range, the model of the automobile from which the unknown paint sample was obtained could be identified from its IR spectrum.

By using stacked partial least squares classifiers and genetic algorithms for feature selection and classification, it is demonstrated that search prefilters can be developed to extract investigative lead information from clear coat paint smears. The results obtained in this study also show that identifying specific wavelengths or wavelet coefficients in IR spectral data is superior to identifying informative wavelength windows when applying pattern recognition techniques to IR spectra from the paint data query (PDQ) database when differentiating paint samples by assembly plant. Search prefilters developed using specific wavelengths or wavelet coefficients outperformed search prefilters that utilized spectral regions. Clear coat paint spectra from the PDQ database may not be well suited for stacking as there are few spectral intervals that can reliably distinguish the different sample groups (i.e., assembly plants) in the data. The information contained in the IR spectra about assembly plant may not be highly compartmentalized in an interval, which also works against stacking. The similarity of the IR spectra within a plant group and the noise present in the IR spectra may also be obscuring information present in spectral intervals.

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Attenuated total reflection (ATR) is a widely used sampling technique in infrared (IR) spectroscopy because minimal sample preparation is required. Since the penetration depth of the ATR analysis beam is quite shallow, the outer layers of a laminate or multilayered paint sample can be preferentially analyzed with the entire sample intact. For this reason, forensic laboratories are taking advantage of ATR to collect IR spectra of automotive paint systems that may consist of three or more layers. However, the IR spectrum of a paint sample obtained by ATR will exhibit distortions, e.g., band broadening and lower relative intensities at higher wavenumbers, compared with its transmission counterpart. This hinders library searching because most library spectra are measured in transmission mode. Furthermore, the angle of incidence for the internal reflection element, the refractive index of the clear coat, and surface contamination due to inorganic contaminants can profoundly influence the quality of the ATR spectrum obtained for automotive paints. A correction algorithm to allow ATR spectra to be searched using IR transmission spectra of the paint data query (PDQ) automotive database is presented. The proposed correction algorithm to convert transmission spectra from the PDQ library to ATR spectra is able to address distortion issues such as the relative intensities and broadening of the bands, and the introduction of wavelength shifts at lower frequencies, which prevent library searching of ATR spectra using archived IR transmission data.

Copper nanoparticles (Cu NPs) were made by electroless deposition on Ge disks as substrates for surface-enhanced infrared absorption (SEIRA). Previous X-ray photoelectron spectra had shown that elemental copper is deposited on the Ge substrate and that the nanoparticulate film remains resistant to oxidation even after several days of air exposure at room temperature. SEIRA spectra of p-nitrothiophenol (p-NTP) adsorbed on the copper nanoparticles were measured. Freshly made substrates made by electroless deposition gave higher enhancements than both the 12-day-old oxidized substrates and substrates made by physical vapor deposition. The intensity of the antisymmetric NO(2) stretching band of p-NTP relative to that of the symmetric stretch was significantly higher for p-NTP adsorbed on copper than on silver nanofilms, indicating that the C(2) axis of the aromatic ring is tilted with respect to the copper surface.

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