Browsing by Author "Picard, Christine Johanna"

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    Advances in Solid Phase Microextraction for the Analysis of Volatile Compounds in Explosives, Tire Treatments, and Entomological Specimens
    (2016-05) Kranz, William D.; Goodpaster, John V.; Manicke, Nick; Sardar, Rajesh; Picard, Christine Johanna; Long, Eric C.
    Solid phase micro-extraction is a powerful and versatile technique, well-suited to the analysis of numerous samples of forensic interest. The exceptional sensitivity of the SPME platform, combined with its adaptability to traditional GC-MS systems and its ability to extract samples with minimal work-up, make it appropriate to applications in forensic laboratories. In a series of research projects, solid phase micro-extraction was employed for the analysis of explosives, commercial tire treatments, and entomological specimens. In the first project, the volatile organic compounds emanating from two brands of pseudo-explosive training aids for use in detector dog imprinting were determined by SPME-GC-MS, and the efficacy of these training materials was tested in live canine trials. In the second project, the headspace above various plasticizers was analyzed comparative to that of Composition C-4 in order to draw conclusions about the odor compound, 2- ethyl-1-hexnaol, with an eye toward the design of future training aids. In the third, automobile tires which had participated in professional race events were analyzed for the presence of illicit tire treatments, and in the fourth, a novel SPME-GC-MS method was developed for the analysis of blowfly (Diptera) liquid extracts. In the fifth and final project, the new method was put to the task of performing a chemotaxonomic analysis on pupa specimens, seeking to chemically characterize them according to their age, generation, and species.
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    Classifying the unknown: Insect identification with deep hierarchical Bayesian learning
    (Wiley, 2023) Badirli, Sarkhan; Picard, Christine Johanna; Mohler, George; Richert, Frannie; Akata, Zeynep; Dundar, Murat
    1. Classifying insect species involves a tedious process of identifying distinctive morphological insect characters by taxonomic experts. Machine learning can harness the power of computers to potentially create an accurate and efficient method for performing this task at scale, given that its analytical processing can be more sensitive to subtle physical differences in insects, which experts may not perceive. However, existing machine learning methods are designed to only classify insect samples into described species, thus failing to identify samples from undescribed species. 2. We propose a novel deep hierarchical Bayesian model for insect classification, given the taxonomic hierarchy inherent in insects. This model can classify samples of both described and undescribed species; described samples are assigned a species while undescribed samples are assigned a genus, which is a pivotal advancement over just identifying them as outliers. We demonstrated this proof of concept on a new database containing paired insect image and DNA barcode data from four insect orders, including 1040 species, which far exceeds the number of species used in existing work. A quarter of the species were excluded from the training set to simulate undescribed species. 3. With the proposed classification framework using combined image and DNA data in the model, species classification accuracy for described species was 96.66% and genus classification accuracy for undescribed species was 81.39%. Including both data sources in the model resulted in significant improvement over including image data only (39.11% accuracy for described species and 35.88% genus accuracy for undescribed species), and modest improvement over including DNA data only (73.39% genus accuracy for undescribed species). 4. Unlike current machine learning methods, the proposed deep hierarchical Bayesian learning approach can simultaneously classify samples of both described and undescribed species, a functionality that could become instrumental in biodiversity monitoring across the globe. This framework can be customized for any taxonomic classification problem for which image and DNA data can be obtained, thus making it relevant for use across all biological kingdoms.
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    Development of Total Vaporization Solid Phase Microextraction and Its Application to Explosives and Automotive Racing
    (2015) Bors, Dana E.; Goodpaster, John V.; Picard, Christine Johanna; Shepson, Paul; Cooks, Graham; Long, Eric C.
    Pipe bombs are a common form of improvised explosive device, due in part to their ease of construction. Despite their simplistic nature, the lethality of pipe bombs should not be dismissed. Due to the risk of harm and their commonality, research into the pipe bomb deflagration process and subsequent chemical analysis is necessary. The laboratory examination of pipe bomb fragments begins with a visual examination. While this is presumptive in nature, hypotheses formed here can lead to subsequent confirmatory exams. The purpose of this study was to measure the mass and velocity of pipe bomb fragments using high speed video. These values were used to discern any trends in container type (PVC or black/galvanized steel), energetic filler (Pyrodex or double base smokeless powder), and ambient temperature (13°C and -8°C). The results show patterns based on container type, energetic filler, and temperature. The second stage of a laboratory exam is chemical analysis to identify any explosive that may be present. Legality calls for identification only, not quantitation. The purpose of this study is to quantitate the amount of explosive residue on post-blast pipe bomb fragments. By doing so, the instrumental sensitivities required for this type of analysis will be known. Additionally, a distribution of the residue will be mapped to provide insight into the deflagration process of a device. This project used a novel sampling technique called total vaporization solid phase microextraction. The method was optimized for nitroglycerin, the main energetic in double base smokeless powder. Detection limits are in the part per billion range. Results show that the concentration of residue is not uniform, and the highest concentration is located on the endcaps regardless of container type. Total vaporization solid phase microextraction was also applied to automotive racing samples of interest to the National Hot Rod Association. The purpose of this project is two-fold; safety of the race teams in the form of dragstrip adhesive consistency and monitoring in the form of fuel testing for illegal adulteration. A suite of analyses, including gas chromatography mass spectrometry, infrared spectroscopy, and evaporation rate, were developed for the testing of dragstrip adhesives. Gas chromatography mass spectrometry methods were developed for both nitromethane based fuel as well as racing gasolines. Analyses of fuel from post-race cars were able to detect evidence of adulteration. Not only was a novel technique developed and optimized, but it was successfully implemented in the analysis of two different analytes, explosive residue and racing gasoline. TV-SPME shows tremendous promise for the future in its ability to analyze a broad spectrum of analytes.