Date of Award


Degree Type


Degree Name

Doctor of Philosophy (PhD)


Food Science

First Advisor

Manpreet Singh

Committee Chair

Manpreet Singh

Committee Member 1

Bruce M. Applegate

Committee Member 2

Arun Bhunia

Committee Member 3

Jolena Waddell


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 Escherichia coli (STEC) in ground beef was developed. Six strains of non-O157 STEC; E. coli O26:H1, E. coli O45:H2, E. coli O103:H2, E. coli O111:H8, E. coli O121:H9, and E. coli O145: nonmotile, has similar pathogenicity as E. coli 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.

Included in

Food Science Commons