“…These results are similar to those obtained in previous studies of General Motors search prefilters for automotive clear coats. 19–23 Figure 2.Principal component plot of the two largest PCs of the 456 simulated ATR spectra and 1178 wavelet coefficients comprising the training set. Each sample is represented as a point in the plot.…”
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 to 500 cm). 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.
“…These results are similar to those obtained in previous studies of General Motors search prefilters for automotive clear coats. 19–23 Figure 2.Principal component plot of the two largest PCs of the 456 simulated ATR spectra and 1178 wavelet coefficients comprising the training set. Each sample is represented as a point in the plot.…”
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 to 500 cm). 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.
“…Cluster analysis and PCA performed on the resultant data revealed three distinct groupings; however, no correlation could be made between the groupings as a function of the make, model or year of the corresponding vehicles, thus limiting the applicability of the statistical model for procuring investigative leads . Lavine and co‐workers have demonstrated the utility of pattern recognition techniques in conjunction with the PDQ database to successfully extract information from FTIR spectra in the database to aid in the generation of investigative leads . A recent study conducted by the authors used attenuated total reflectance (ATR) IR spectroscopy to characterise clear coats of over 100 vehicles and then used a combination of PCA and LDA to examine the diversity in the sample population.…”
Section: Introductionmentioning
confidence: 99%
“…[31] Lavine and co-workers have demonstrated the utility of pattern recognition techniques in conjunction with the PDQ database to successfully extract information from FTIR spectra in the database to aid in the generation of investigative leads. [35][36][37][38][39][40][41] A recent study conducted by the authors used attenuated total reflectance (ATR) IR spectroscopy to characterise clear coats of over 100 vehicles and then used a combination of PCA and LDA to examine the diversity in the sample population. The authors concluded that the ATR model is capable of differentiating between samples based upon the origin, manufacturer and in some instances the model and year of manufacture of the vehicle.…”
The clear coats from a collection of automotive paint samples of 139 vehicles, covering a range of Australian and international vehicle manufacturers and sold in Western Australia, were characterised using FT-Raman spectroscopy. Principal component analysis (PCA) revealed 19 distinct classes that were associated with the vehicles' manufacturer and model, and in the case of Australian manufacturers, the years of manufacture. Linear discriminant analysis based on the PCA groupings gave excellent discrimination between the groups with 96.9% of the calibration set and 97.6% of the validation set being correctly classified. Although the sample set comprised only vehicles available in Australia, the methodology used is universal and hence applicable in any jurisdiction that is willing and able to generate a statistically significant data set and maintain and update it as new vehicles appear on the market. A FT-Raman spectroscopy-based database would rapidly provide information regarding vehicle origin and manufacture and hence generate investigative leads for questioned paint samples found at incident sites.
“…However, these search prefilters [4][5][6][7][8][9][10] were limited to a single manufacturer To perform spectral library searching, the cross correlated spectra were divided into windows. For the forward search, the original spectra were also divided into three regions.…”
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