Browsing by Subject "microspectrophotometry"

Now showing 1 - 4 of 4
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    Characterization of Automotive Paint Clear Coats by Ultraviolet Absorption Microspectrophotometry with Subsequent Chemometric Analysis
    (2010-10) Liszewski, Elisa A; Lewis, Simon W; Siegel, Jay A; Goodpaster, John V.
    Clear coats have been a staple in automobile paints for almost thirty years and are of forensic interest when comparing transferred and native paints. However, the ultraviolet (UV) absorbers in these paint layers are not typically characterized using UV microspectrophotometry, nor are the results studied using multivariate statistical methods. In this study, measurements were carried out by UV microspectrophotometry on 71 samples from American and Australian automobiles, with subsequent chemometric analysis of the absorbance spectra. Sample preparation proved to be vital in obtaining accurate absorbance spectra and a method involving peeling the clear coat layer and not using a mounting medium was preferred. Agglomerative hierarchical clustering indicated three main groups of spectra, corresponding to spectra with one, two, and three maxima. Principal components analysis confirmed this clustering and the factor loadings indicated that a substantial proportion of the variance in the data set originated from specific spectral regions (230–265 nm, 275–285 nm, and 300–370 nm). The three classes were well differentiated using discriminant analysis, where the cross-validation accuracy was 91.6% and the external validation accuracy was 81.1%. However, results showed no correlation between the make, model, and year of the automobiles.
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    Improving the confidence of “questioned versus known” fiber comparisons using microspectrophotometry and chemometrics
    (Elsevier, 2016-11) Sauzier, Georgina; Reichard, Eric; van Bronswijk, Wilhelm; Lewis, Simon W.; Goodpaster, John V.; Department of Chemistry and Chemical Biology, School of Science
    Microspectrophotometry followed by chemometric data analysis was conducted on pairs of visually similar blue acrylic fibers, simulating the “questioned versus known” scenarios often encountered in forensic casework. The relative similarity or dissimilarity of each pair was determined by employing principal component analysis, discriminant analysis and Fisher’s exact test. Comparison of fibers from within each set resulted in a correct inclusion result in 10 out of 11 scenarios, with the one false exclusion attributed to a lack of reproducibility in the spectra. Comparison of fibers from different sets resulted in a correct exclusion result in 108 of 110 scenarios, with two sets that shared identical dye combinations being indistinguishable. Although the presented methods are not infallible, they may nonetheless provide a path forward for forensic fiber examiners that has a more scientifically rigorous basis on which to support their findings in a court of law.
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    Instrumental and Chemometric Analysis of Automotive Clear Coat Paints by Micro Laser Raman and UV Microspectrophotometry
    (2012-07-19) Mendlein, Alexandra Nicole; Siegel, Jay A.; Goodpaster, John V. (John Vincent); Li, Lei
    Automotive paints have used an ultraviolet (UV) absorbing clear coat system for nearly thirty years. These clear coats have become of forensic interest when comparing paint transfers and paint samples from suspect vehicles. Clear coat samples and their ultraviolet absorbers are not typically examined or characterized using Raman spectroscopy or microspectrophotometry (MSP), however some past research has been done using MSP. Chemometric methods are also not typically used for this characterization. In this study, Raman and MSP spectra were collected from the clear coats of 245 American and Australian automobiles. Chemometric analysis was subsequently performed on the measurements. Sample preparation was simple and involved peeling the clear coat layer and placing the peel on a foil-covered microscope slide for Raman or a quartz slide with no cover slip for MSP. Agglomerative hierarchical clustering suggested three classes of spectra, and principal component analysis confirmed this. Factor loadings for the Raman data illustrated that much of the variance between spectra came from specific regions (400 – 465 cm-1, 600 – 660 cm-1, 820 – 885 cm-1, 950 – 1050 cm-1, 1740 – 1780 cm-1, and 1865 – 1900 cm-1). For MSP, the regions of highest variance were between 230 – 270 nm and 290 – 370 nm. Discriminant analysis showed that the three classes were well-differentiated with a cross-validation accuracy of 92.92% for Raman and 91.98% for MSP. Analysis of variance attributed differentiability of the classes to the regions between 400 – 430 cm-1, 615 – 640 cm-1, 825 – 880 cm-1, 1760 – 1780 cm-1, and 1860 – 1900 cm-1 for Raman spectroscopy. For MSP, these regions were between 240 – 285 nm and 300 – 370 nm. External validation results were poor due to excessively noisy spectra, with a prediction accuracy of 51.72% for Raman and 50.00% for MSP. No correlation was found between the make, model, and year of the vehicles using either method of analysis.
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    Microspectrophotometric Analysis of Yellow Polyester Fiber Dye Loadings with Chemometric Techniques
    (Elsevier, 2017-03) Reichard, Eric J.; Bartick, Edward G.; Morgan, Stephen L.; Goodpaster, John V.; Chemistry and Chemical Biology, School of Science
    Microspectrophotometry is a quick, accurate, and reproducible method to compare colored fibers for forensic purposes. Applying chemometric techniques to spectroscopic data can provide valuable information, especially when looking at a complex dataset. In this study, background subtracted and normalized visible spectra from ten yellow polyester exemplars dyed with different concentrations of the same dye ranging from 0.1% to 3.5% (w/w), were analyzed by agglomerative hierarchical clustering (AHC), principal component analysis (PCA), and discriminant analysis (DA). Systematic changes in the wavelength of maximum absorption, peak shape and signal-to-background ratio were noted as dye loading increased. In general, classifying the samples into ten groups (one for each exemplar) had poor accuracy (i.e., 51%). However, classification was much more accurate (i.e., 96%) using three classes of fibers that were identified by AHC as having low (0.10–0.20 wt%), medium (0.40–0.75 wt%), and high (1.5–3.5 wt%) dye loadings. An external validation with additional fibers and data generated by independent analysts confirmed these findings. Lastly, it was also possible to discriminating pairs of exemplars with small differences in dye loadings as a simulation of questioned (Q) versus known (K) comparisons.