Doctor of Philosophy (PhD)
First Advisor's Name
Kenneth G Furton
First Advisor's Committee Title
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Fourth Advisor's Name
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Fifth Advisor's Committee Title
Canine detection, laurel wilt, avocado, HS-SPME-GC-MS, Rafaelea lauricola
Date of Defense
Standoff detection of targets using volatiles is essential when considering substances that are hazardous or dangerous, or for which the presence or location is unknown. For many invasive biological threats, their presence is often not realized until they have begun visibly affecting and spreading through crops or forests. The fungus Raffaelea lauricola is a biothreat vectored by the invasive beetle Xyleborus glabratus, or redbay ambrosia beetle (RAB), whose presence in avocado groves is currently detectable by visual inspection. Once visually identified, the affected trees must be removed and destroyed to protect those remaining trees. However, if the fungus is identified via standoff volatile detection, there is anecdotal evidence that it can be treated with propiconazole and saved from progression to the fatal laurel wilt disease. As a result of the rapid spread of R. lauricola and the quick death of trees, early detection through standoff methods is essential. The only current method of pre-symptomatic identification is canine detection. Canines are sensitive and selective biological detectors that can trace odors to their source, despite the presence of a variety of background odors. The present research evaluated the volatile organic compounds (VOCs) of the laurel wilt disease and R. lauricola using headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). Additionally, a new method for odor collection and presentation to trained detection canines was developed. Knowledge of the disease and standoff volatile detection capabilities are improved using this information.
Simon, Alison G., "The Detection of an Invasive Pathogen through Chemical and Biological Means for the Protection of Commercial Crops" (2017). FIU Electronic Theses and Dissertations. 3558.
Available for download on Saturday, November 30, 2019
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