Browsing by Subject "Chemometrics"

Now showing 1 - 5 of 5
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    Chemometric Comparison Of GC-MS And GC-VUV For The Trace Analysis Of Methamphetamine
    (2024) Lyle, Grant; Goodpaster, John; Sardar, Rajesh; Manicke, Nicholas
    Chemometrics, the application of mathematical or statistical algorithms to make inferences on the state of a chemical system from physical measurements of it, is a powerful tool that can be used to re-read what previously was observed as ‘noise’ in analytical measurements. Instruments such as spectrophotometers can take thousands of measurements over a predefined interval, but the spectra are only of great use when reference libraries exist, or if large trends occur that allow for visual matching, such as with a particular functional group. Application of statistical techniques to these data, such as principal component analysis (PCA) and linear discriminant analysis (LDA), can help to spot underlying variances, and differentiate between similar spectra by using linear combinations of these variables for classification. Methamphetamine (MA) is a member of the phenethylamines, a group of compounds that act as central nervous system stimulants, which are highly addictive and often the subject of law enforcement efforts at the local and federal level. Use of derivatization agents in analysis of seized narcotics is common practice, as it increases volatility/thermal stability of analytes, and improves peak shape for chromatographic resolution. In this analysis, we looked to investigate the difference in instrumental response for MA in its native form, as well as derivatized with two common agents, acetic anhydride and trifluoroacetic anhydride. These three forms were analyzed both on a gas chromatograph- mass spectrometer (GC-MS) and a gas chromatograph- vacuum ultraviolet spectrometer (GC-VUV). The raw GC-MS and GC-VUV data were separately normalized, and the dimensionality of the data was reduced through PCA, which uses orthogonal linear transformations of the data to capture most of the variance between datasets while simultaneously reducing the dimensionality for further analysis. Linear discriminant analysis was utilized to look at the principal components from PCA, and a classification model was built for use in discriminating between forms of methamphetamine from compressed datasets.
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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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    Instrumental and Statistical Methods for the Comparison of Class Evidence
    (2011-03-09) Liszewski, Elisa Anne; Goodpaster, John V. (John Vincent); Siegel, Jay A.; Deo, Sapna K.
    Trace evidence is a major field within forensic science. Association of trace evidence samples can be problematic due to sample heterogeneity and a lack of quantitative criteria for comparing spectra or chromatograms. The aim of this study is to evaluate different types of instrumentation for their ability to discriminate among samples of various types of trace evidence. Chemometric analysis, including techniques such as Agglomerative Hierarchical Clustering, Principal Components Analysis, and Discriminant Analysis, was employed to evaluate instrumental data. First, automotive clear coats were analyzed by using microspectrophotometry to collect UV absorption data. In total, 71 samples were analyzed with classification accuracy of 91.61%. An external validation was performed, resulting in a prediction accuracy of 81.11%. Next, fiber dyes were analyzed using UV-Visible microspectrophotometry. While several physical characteristics of cotton fiber can be identified and compared, fiber color is considered to be an excellent source of variation, and thus was examined in this study. Twelve dyes were employed, some being visually indistinguishable. Several different analyses and comparisons were done, including an inter-laboratory comparison and external validations. Lastly, common plastic samples and other polymers were analyzed using pyrolysis-gas chromatography/mass spectrometry, and their pyrolysis products were then analyzed using multivariate statistics. The classification accuracy varied dependent upon the number of classes chosen, but the plastics were grouped based on composition. The polymers were used as an external validation and misclassifications occurred with chlorinated samples all being placed into the category containing PVC.
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    Monitoring, characterizing, and preventing microbial degradation of ignitable liquids on soil
    (2013) Turner, Dee Ann; Goodpaster, John V. (John Vincent); Michalski, Greg M.; Blacklock, Brenda J.; Siegel, Jay A.; Long, Eric C. (Eric Charles)
    Organic-rich substrates such as soil provide an excellent carbon source for bacteria. However, hydrocarbons such as those found in various ignitable liquids can also serve as a source of carbon to support bacterial growth. This is problematic for fire debris analysis as samples may be stored at room temperature for extended periods before they are analyzed due to case backlog. As a result, selective loss of key components due to bacterial metabolism can make identifying and classifying ignitable liquid residues by their chemical composition and boiling point range very difficult. The ultimate goal of this project is to preserve ignitable liquid residues against microbial degradation as efficiently and quickly as possible. Field and laboratory studies were conducted to monitor microbial degradation of gasoline and other ignitable liquids in soil samples. In addition to monitoring degradation in potting soil, as a worst case scenario, the effect of soil type and season were also studied. The effect of microbial action was also compared to the effect of weathering by evaporation (under nitrogen in the laboratory and by the passive headspace analysis of the glass fragments from the incendiary devices in the field studies). All studies showed that microbial degradation resulted in the significant loss of n-alkanes and lesser substituted alkylbenzenes predominantly and quickly, while more highly substituted alkanes and aromatics were not significantly affected. Additionally, the residential soil during the fall season showed the most significant loss of these compounds over the course of 30 days. To combat this problem, a chemical solution is to be immediately applied to the samples as they are collected. Various household and commercial products were tested for their efficacy at low concentrations to eliminate all living bacteria in the soil. Triclosan (2% (w/v) in NaOH) proved to be the most effective at preserving ignitable liquid residues for at least 30 days.
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    Spectroscopic and chemometric analysis of automotive clear coat paints by micro fourier transform infrared spectroscopy
    (2014) Osborne Jr., James D.; Goodpaster, John V. (John Vincent); Manicke, Nicholas; Minto, Robert; Picard, Christine
    Clear coats have been part of automotive field paint finishes for several decades. Originally a layer of paint with no pigment, they have evolved into a protective layer important to the appearance and longevity of the vehicle's finish. These clear coats have been studied previously using infrared spectroscopy and other spectroscopic techniques. Previous studies focused on either all the layers of an automobile finish or on chemometric analysis of clear coats using other analytical techniques. For this study, chemometric analysis was performed on preprocessed spectra averaged from five separate samples. Samples were analyzed on a Thermo-Nicolet Nexus 670 connected to a Continuμm™ FT-IR microscope. Two unsupervised chemometric techniques, Agglomerative Hierarchical Clustering (AHC) and Principal Component Analysis (PCA), were used to evaluate the data set. Discriminant analysis, a supervised technique, was evaluated using several known qualifiers; these included cluster group from AHC, make, model, and year. Although discriminant analysis confirmed the AHC and PCA results, no correlation to make, model, or year was indicated.