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Experimental validation of a predictive model for Salmonella growth In raw ground beef under dynamic temperatures
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McConnell, Jennifer A.. Experimental validation of a predictive model for Salmonella growth In raw ground beef under dynamic temperatures. Retrieved from https://doi.org/doi:10.7282/T37942Q4

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TitleExperimental validation of a predictive model for Salmonella
growth In raw ground beef under dynamic temperatures
NameMcConnell, Jennifer A. (author); Schaffner, Donald W. (chair); Duffy, Siobian (internal member); Matthews, Karl R. (outside member); Rutgers University; Graduate School - New Brunswick
Date Created2013
Other Date2013-10 (degree)
SubjectMicrobial Biology, Beef--Quality, Salmonella, Temperature control
Extentvi, 40 p. : ill.
DescriptionWhen food is transported at ambient temperatures for extended periods of time, or when power is lost during natural disasters; foodborne pathogens can multiply. Current US Food and Drug Administration (FDA) Model Food Code guidelines state that food can be kept out of temperature control for up to 4 h or up to 6 h if the food product starts at an initial 41°F (5°C) and the temperature does not exceed 70°F (21°C). This project validates existing ComBase computer models for Salmonella spp. growth under changing temperature conditions in raw ground beef as model system, using scenarios that would exceed Food Code guidelines. This thesis is separated into a literature review describing FDA Model Food Code guidelines, Salmonella prevalence and concentration in ground beef, and dynamic models for bacterial growth (I) and experimental validation of ComBase computer models for Salmonella spp. growth in raw ground beef (II). The growth rate of a 5-strain cocktail of Salmonella spp. meat isolates was inoculated in 20% fat ground beef at a concentration of 4-log CFU/g. Inoculated ground beef samples were temperature abused for different lengths of time and to different maximum temperatures. The temperature profiles represent loss of proper refrigeration, warming and then cooling following a linear temperature gradient. A total of 9 different conditions were studied. Results show that when maximum temperatures were low, there was generally good agreement between the ComBase models and experiments. When maximum temperatures were closer to the optimum growth temperature for Salmonella (37°C), predictive models were fail-safe. It appears that faster cooling times limit the growth of Salmonella, so rapidly cooling foods (e.g. in a freezer) after extended temperature abuse can work as a risk mitigation measure. Validation of these models will be useful to extension professionals advising consumers, restaurateurs transporting food in unrefrigerated vehicles, and retailers facing a power outage. These finding may also be useful to those seeking to improve the science base of the FDA Model Food Code.
NoteM.S.
NoteIncludes bibliographical references
Noteby Jennifer A. McConnell
Genretheses, ETD graduate
Persistent URLhttps://doi.org/doi:10.7282/T37942Q4
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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