Modeling for thermal resistance of non-O157 shiga toxin producing Escherichia coli in ground beef

<p>Predictive models in microbiology are used for estimating the growth or survival of microorganism in a set of environmental conditions. A validated predictive model provides an alternative to extensive survival and shelf life studies. In this study, a predictive inactivation model for non-O157 shiga toxin producing <em>Escherichia coli</em> (STEC) in ground beef was developed. Six strains of non-O157 STEC; <em>E. coli</em> O26:H1, <em>E. coli</em> O45:H2, <em>E. coli</em> O103:H2, <em>E. coli </em>O111:H8, <em>E. coli</em> O121:H9, and <em>E. coli</em> O145: nonmotile, has similar pathogenicity as <em>E. coli</em> O157:H7 and can cause serious food borne illnesses. These pathogens are considered as an adulterant in meat products. The thermal behavior these non-O157 STECs was studied in laboratory media as well as in ground beef with varying fat content. There was no significant difference in the heat resistance among the strains, therefore, a cocktail of the strains was used for ground beef study. Ground beef fat content levels of 5, 10, 15, 20, 25, and 30% were used. Survival curves were generated between surviving population against time during heat treatment at five temperatures 55, 60, 65, 68, 71.1ºC. The shape of survival curves was analyzed by statistical analysis software (SAS®) to identify the best fitting primary model. The survival of these pathogens was modeled as a second order polynomial function of fat content of ground beef and temperature of cooking. The accuracy factor of the developed model was 11.43%, which is in the acceptable limit of 25%. The model was successfully validated for predicting process lethality in ground beef obtained from three grocery stores.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Food Science

Year

2016

Author dc:creator

• Brar, Jagpinder S.

Contributors dc:contributor

• Manpreet Singh
• Bruce M. Applegate
• Arun Bhunia
• Jolena Waddell

• Biological sciences
• D-value
• Food safety
• Heat resistance
• Non-o157 stec
• Pathogen modeling
• Predictive modeling
• Food Science

Repository record dc:identifier

https://docs.lib.purdue.edu/open_access_dissertations/946

OAI identifier oai:identifier

oai:docs.lib.purdue.edu:open_access_dissertations-2125