title: Predictive modelling of the growth and survival of Listeria in fishery products.
abstract: Predictive microbiology provides a powerful tool to aid the exposure assessment phase of 'quantitative microbial risk assessment'. Using predictive models changes in microbial populations on foods between the point of production/harvest and the point of eating can be estimated from changes in product 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 models 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 that models are, at best, only a simplified representation of reality. As such, application of model predictions should be tempered by previous experience, and used with cognisance of other microbial ecology principles that may not be included in the model. Nonetheless, it is concluded that predictive models, successfully validated in agreement with defined performance criteria, will be an essential element of exposure assessment within formal quantitative 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 limitations, 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 variability in microbial responses. The integration of models for microbial growth, growth limits or inactivation into models that can predict both increases and decreases in microbial populations over time will also improve the utility of predictive models for exposure assessment. All of these issues are the subject of ongoing research.
authors.affiliation: School of Agricultural Science, University of Tasmania, Hobart, Australia. tom.ross@utas.edu.au
authors.forename: T
authors.initials: T
authors.lastname: Ross
authors.forename: P
authors.initials: P
authors.lastname: Dalgaard
authors.forename: S
authors.initials: S
authors.lastname: Tienungoon
mesh_terms: D000818:Animals; D005396:Fish Products / Q000382:microbiology*; D005506:Food Contamination; D005511:Food Handling / Q000379:methods* / Q000592:standards; D005516:Food Microbiology; D005544:Forecasting; D006801:Humans; D008089:Listeria monocytogenes / Q000254:growth & development*; D008088:Listeriosis / Q000517:prevention & control*; D008954:Models, Biological; D018570:Risk Assessment; D013696:Temperature