DescriptionPredictive models of microbial growth are important tools for quantitative microbial risk assessment (QMRA) (Nauta, 2000). However, for reliable predictions, models must be validated for the product of concern and realistic conditions of contamination and handling (Ross et al., 2000).
As a simplification of reality, models have a limited predictive ability (Ross et al., 2000), and the growth settings covered by a model represent one of the factors that may contribute to inaccurate predictions.
Many studies have shown that a change in experimental settings, such as the bacterial strain or the growth media, leads to different estimates of growth parameters or to different 'performance' of the same model. Consequently, stochastic integration of variability of microbial growth is indispensable for the application of predictive models in QMRA. Variability can be defined when model validation is performed under well-controlled conditions, such as challenge studies: sources of variability are more difficult to define when a model is validated against literature data. In this case, to characterize variability or uncertainty associated to predictions, it is crucial to determine the effect of data-related factors on model performance.
Here we assess how different growth settings inherent to literature datasets affect the performance of a growth model compared to its performance with the data used to generate it.
|17 Sept 2013
|8th International Conference on Predictive Modelling in Food