Predictive modelling of the growth and survival of Listeria in fishery products

Predictive microbiology provides a powerful tool to aid the exposure assess ment phase of 'quantitative microbial risk assessment'. Using predictive mo dels changes in microbial populations on foods between the point of product ion/harvest and the point of eating can be estimated from changes in produc t parameters (temperature, storage atmosphere, pH, salt/water activity, etc .). Thus, it is possible to infer exposure to Listeria monocytogenes at the time of consumption from the initial microbiological condition of the food and its history from production to consumption. Predictive microbiology mo dels have immediate practical application to improve microbial food safety and quality, and are leading to development of a quantitative understanding of the microbial ecology of foods. While models are very useful decision-support tools it must be remembered t hat models are, at best, only a simplified representation of reality. As su ch, application of model predictions should be tempered by previous experie nce, and used with cognisance of other microbial ecology principles that ma y not be included in the model. Nonetheless, it is concluded that predictiv e models, successfully validated in agreement with defined performance crit eria, will be an essential element of exposure assessment within formal qua ntitative risk assessment. Sources of data and models relevant to assessment of the human health risk of L. monocytogenes in seafoods are identified. Limitations of the current generation of predictive microbiology models are also discussed. These limi tations, and their consequences, must be recognised and overtly considered so that the risk assessment process remains transparent. Furthermore, there is a need to characterise and incorporate into models the extent of variab ility in microbial responses. The integration of models for microbial growt h, growth limits or inactivation into models that can predict both increase s and decreases in microbial populations over time will also improve the ut ility of predictive models for exposure assessment. All of these issues are the subject of ongoing research. (C) 2000 Elsevier Science B.V. All rights reserved.