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dc.contributor.advisor Uhde-Stone, Dr. Claudia
dc.contributor.advisor Gorski, Dr. Lisa
dc.creator Kashanian, Bijan
dc.date 2017-05-19
dc.date.accessioned 2019-06-17T23:48:57Z
dc.date.available 2019-06-17T23:48:57Z
dc.date.issued 2017-06-01
dc.identifier.uri http://hdl.handle.net/10211.3/210964
dc.description.abstract The potential for FTIR to be utilized as a tool for rapid identification and serotyping is an alluring prospect with a high likelihood of utilizing online collaboration as a method of constructing and growing a global library for microbiological classification. FTIR can identify and discern S. aureus from other species of Staphylococcus (Lamprell et al., 2006), and has been utilized with ANN to rapidly serotype Liseria spp. (Romanolo et al., 2015). The two objectives of my research were to: (1) Explore the use of FTIR as an accurate method of obtaining spectra from 18 Salmonella serovars selected from a list of the “Top 20” most prevalent strains responsible for food-borne Salmonellosis (Table 1, modified from CDC, 2012) and from 24 replicates of Salmonella enterica enterica Kentucky and, (2) Determine the capacity of an Artificial Neural Network to reliably learn, recognize, and classify the spectra for each corresponding serovar. en_US
dc.language English en_US
dc.subject Salmonella en_US
dc.title Rapid Identification of Salmonella Serotypes Using FTIR and ANN Technology en_US
dc.type Thesis en_US
dc.contributor.primaryAdvisor Lauzon, Dr. Carol
thesis.degree.name Master of Science in Biological Science en_US

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