Two complementary measures are proposed as simple indices of the performance of models in predictive food microbiology. The indices assess the level of confidence one can have in the predictions of the model and whether the model displays any bias which could lead to 'fail-dangerous' predictions. The use of the indices is demonstrated using data collated from independent and published literature. This analysis supports previous reports that evaluation of predictive models by comparison to published microbial growth rate data may be inappropriate because of limitations in that data. The indices may fail to reveal some forms of systematic deviation between observed and predicted behaviour. It is concluded, however, that the indices provide an objective and readily interpreted summary of model performance and may serve as a first step towards the development of an objective and useful definition of the term 'validated model' in predictive food microbiology.

Indices for performance evaluation of predictive models in food microbiology

ComBase: a common database on microbial responses to food environments.

Relation between the generation time and the lag time of bacterial growth kinetics.

Meat products are perishable foods and unless stored under proper conditions spoil quickly. In addition, if pathogens are present, meat products may become hazardous for consumers. Pathogens such as Listeria monocytogenes, Escherichia coli O157:H7, and Salmonella spp. can grow and cause illness by the ingestion of the bacterial cells, therefore, assurance of meat safety and quality is of utmost importance. The emergence of low infectious dose pathogens, i.e. those that may cause disease at 1–10 organisms ingested, presents a significant challenge to predictive microbiology. In order to become a better tool for the meat industry and consumers, the mathematical models that form the basis for predicting microbial growth should (1) be validated in the actual food rather than in lab media, (2) take into account the cumulative effect of any temperature fluctuation that regularly occurs in distribution, and (3) keep in mind that pathogen initial count is usually unknown, and may be below the detection limit. This review presents some background on how to address these challenges.

/pdf/modeling-pathogen-growth-in-meat-products-future-challenges-2w06cfqfc6.pdf

Modeling pathogen growth in meat products: Future challenges

Estimating the shelf life and safety of any food product is an important part of food product development. Predictive food microbiology reduces the time and expense associated with conventional challenge and shelf life testing. The purpose of this study was to characterize and model germination, outgrowth, and lag (GOL) time and the exponential growth rate (EGR) of Bacillus stearothermophilus in salty carrot medium (SCM) as a function of pH, temperature, and NaCl concentration. B. stearothermophilus is a spore-forming thermophilic organism associated with flat sour spoilage of canned foods. A split-split plot design was used to measure the effects and interactions of pH (5.5 to 7.0), temperature (45 to 60(deg)C), and NaCl (0 to 1%) on the growth kinetics of B. stearothermophilus in SCM. A total of 96 experiments were analyzed, with individual curve parameters determined by using the Gompertz equation. Quadratic polynomial models for GOL time and EGR of B. stearothermophilus in terms of temperature, pH, and NaCl were generated by response surface analysis. The r(sup2) values for the GOL time and EGR models were 0.917 and 0.916, respectively. These models provide an estimate of bacterial growth in response to combinations of the variables studied within the specified ranges. The models were used to predict GOL times and EGRs for additional experimental conditions. The accuracy of these predictions validated the model's predictive ability in SCM.

Mathematical Models for the Effects of pH, Temperature, and Sodium Chloride on the Growth of Bacillus stearothermophilus in Salty Carrots.

Response surface model of the effect of pH, sodium chloride and sodium nitrite on growth of Yersinia enterocolitica at low temperatures

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Response surface model of the effect of pH, sodium chloride and sodium nitrite on growth of Yersinia enterocolitica at low temperatures