Predictive modeling of thermal inactivation of non-O157 Shiga toxin-producing Escherichia coli in ground beef with varying fat contents

A mathematical model to predict the thermal inactivation of non-O157 Shiga toxin-producing Escherichia coli (STEC) in ground beef was developed, with temperature and fat content of ground beef as controlling factors. Survival curves for a cocktail of non-O157 STEC strains in ground beef at four temperatures (55, 60, 65, and 68 C) and six fat levels (5, 10, 15, 20, 25, and 30%) were generated. Nine primary models-log-linear, log-linear with tail, biphasic, sigmoidal, four-factor sigmoidal, Baranyi, Weibull, mixed Weibull, and Gompertz-were tested for fitting the survival curves. Primary modeling analysis showed the Weibull model had the highest accuracy factor and Akaike's weight, making it the best-fitting model. The parameters of the Weibull model were estimated using a nonlinear mixed, and response surface modeling was used to develop a second-order poly-nomial regression to estimate the impact of fat in ground beef and cooking temperature on the heat resistance of non-O157 STEC strains. The secondary model was successfully validated by comparing predicted lethality (log10 CFU/g) with the observed values for ground beef containing 10 and 27% fat at 58 and 62 C-degrees. Process lethality obtained from experimental data was within the prediction interval of the predictive model. The developed model will assist the food industry in estimating the appropriate time and temperature required for cooking ground beef to provide adequate protection against STEC contaminants.

Automated summary

A mathematical model was developed to predict the thermal inactivation of non-O157 Shiga toxin-producing Escherichia coli (STEC) in ground beef. The model assists the food industry in estimating the appropriate time and temperature required for cooking ground beef to provide adequate protection against STEC contaminants.