Predictive microbiology

Predictive microbiology using growth models should be implemented in order to follow the microbial behavior in fruit osmotically dehydrated/ impregnated and to compute their shelf life as a function of process variables, such as concentration of osmotic medium, initial contamination of the solution, and fruit storage temperature. 

In order to reliably calculate microbial behavior, predictive microbiology requires a reliable combination of mathematical and statistical considerations (Roberts, 1995). It is, however, often inappropriate to extrapolate mathematical models used in different applications. In Table 10.1 the differences between mathematical characterization of bacterial growth in food microbiology and mathematical modeling techniques used in biotechnology are stipulated. 

It is evident that evaluation of the effectiveness of an organic acid for a specific application will require a much better understanding of general as well as specific stress response potentials of foodborne pathogens (Ricke, 2003). Predictive microbiology may be a handy tool in achieving this via mathematical models to describe the behavior of foodborne microorganisms. The concept has developed very rapidly over the past two decades... 

Growth models that describe the dependence of primary model parameters on environmental factors (i.e., temperature, water activity, pH, and organic acids) are referred to as secondary growth models. A number of different secondary model types exist in predictive microbiology and are discussed in the following section (Wilson et al., 2002). 

Baranyi, J. and Roberts, T.A. 2004. Predictive microbiology—Quantitative microbial ecology. Culture, 25 14-16. 

McMeekin, T.A. 2003. An essay on the unrealized potential of predictive microbiology. In Modelling Microbial Responses in Food, McKellar, R.C. and Lu, X. (Eds.), pp. 231-235. Boca Raton, FL CRC Press. 

McMeekin, T. A., OlleyJ.N., Ross, T., and RatkowskyD. A. 1993. Predictive Microbiology Theory and Application. Taunton, Somerset, UK Research Studies Press. 

Ross, T. and McMeekin, T.A. 1994. Predictive microbiology. International Journal of Food Microbiology 23 241-264. 

Ross, T. and Olley, J. 1997. Problems and solutions in the application of predictive microbiology. In F. Shahidi, Y. Jones, and D.D. Kitt (Eds.), Seafood Safety, Processing and Biotechnology, pp. 101-118. Lancaster, PA Technomic. 

The full Exposure Assessment model combines the food pathway characteristics, the predictive microbiological models for growth and reduction of Listeria and the probabilistic models in each step of food chain studied, which has been built as a spreadsheet model in Microsoft Excel with add on Risk 5.0 (Palisade Newfield). For one iteration of the Monte Carlo model, 350 batch of milk are simulated. Per simulation 10000 iterations are run using Latin Hypercube sampling, representing 10000 independent industrial batches and 105.000.000 packages of one litre of milk per year. 

Walls 1. Scott V. (1997). Use of predictive microbiology in microbial food safety risk assessment. Int. J. Food Microbiology. 36, pp. 97-102. 

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