Variability in Growth Characteristics of Different E. coli O157:H7 Isolates, and its Implications for Predictive Microbiology

In growth experiments 75 clinical isolates of Escherichia coli O157:H7 were studied for the variability in seven growth characteristics: minimum, optimum and maximum growth temperature, minimum and optimum pH, minimum water activity and optimum specific growth rate. With these characteristics, growth can be predicted for any given set of conditions (temperature, acidity and water activity), when the “gamma model” is used as predictive microbiology model. The optimum specific growth rate of the 75 strains, as conceptually defined by the model, had a mean value of 4.71 (ln[emsp4 ](count)/h), with a standard deviation of 0.39. It could not be shown that the mean optimum specific growth rate differs significantly per strain, so the variability found will predominantly be the result of other sources of variation. In contrast, the experimental results suggest that the differences in minimum temperature of growth may be partially strain specific. As variability in growth is crucial for quantitative risk assessment, the results were implemented in the gamma model. Predictions at three sets of growth conditions were compared with predictions of the Pathogen Modeling Program (PMP) (USDA) and some published experimental results. This comparison showed that growth rates higher than those published and outside the 95% confidence interval predicted by the PMP are feasible. Although it needs further development and additional tests, our approach offers a promising strategy to predict the variability in growth.

[1]  M. Zwietering,et al.  Modelling Bacterial Growth of Lactobacillus curvatus as a Function of Acidity and Temperature , 1995, Applied and environmental microbiology.

[2]  Isabel Walls,et al.  Validation of Predictive Mathematical Models Describing the Growth of Escherichia coli O157:H7 in Raw Ground Beef. , 1996, Journal of food protection.

[3]  M H Cassin,et al.  Quantitative risk assessment for Escherichia coli O157:H7 in ground beef hamburgers. , 1998, International journal of food microbiology.

[4]  Marcel H Zwietering,et al.  A Decision Support System for Prediction of the Microbial Spoilage in Foods. , 1992, Journal of food protection.

[5]  Tom Ross,et al.  Predictive Microbiology : Theory and Application , 1993 .

[6]  Robert L. Buchanan,et al.  Effect of water activity and humectant identity on the growth kinetics ofEscherichia coliO157:H7☆☆☆ , 1997 .

[7]  M. Degroot,et al.  Probability and Statistics , 2021, Examining an Operational Approach to Teaching Probability.

[8]  T. Ross,et al.  Modelling the growth rate of Escherichia coli as a function of pH and lactic acid concentration , 1997, Applied and environmental microbiology.

[9]  F. Rombouts,et al.  Modeling of the Bacterial Growth Curve , 1990, Applied and environmental microbiology.

[10]  R. C. Whiting,et al.  Microbial modeling in foods. , 1995, Critical reviews in food science and nutrition.

[11]  S. Dundas,et al.  Escherichia coli O157 and human disease. , 1998, Current opinion in infectious diseases.

[12]  I Walls,et al.  Use of predictive microbiology in microbial food safety risk assessment. , 1997, International journal of food microbiology.

[13]  R L Buchanan,et al.  Expansion of response surface models for the growth of Escherichia coli O157:H7 to include sodium nitrite as a variable. , 1994, International journal of food microbiology.

[14]  Robert L. Buchanan,et al.  Response surface models for the growth kinetics of Escherichia coli O157:H7 , 1993 .

[15]  M H Zwietering,et al.  Application of predictive microbiology to estimate the number of Bacillus cereus in pasteurised milk at the point of consumption. , 1996, International journal of food microbiology.