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Listen to this siteFriday 30 April 2004
This project aims to improve the Food MicroModel database as well as the mathematical models developed and validated within the database.
Study Duration : April 2001 to March 2002
Contractor : Institute of Food Research (IFR)
The Food MicroModel database managed by the Institute of Food Research (IFR) is one of the largest systematically organised databases on bacterial responses to food environments. It stores growth, survival and death data generated under a former Government-funded research and kinetic parameters collected from scientific literature. The database needs regular maintenance and improvement to keep pace with the fast developments in IT and computation, as well as servicing its computer programs.
The aim of this project is to improve the Food MicroModel database as well as the mathematical models developed and validated within the database.
The project objectives are to:
1. Access-Excel database-prediction connection
Connect the Food MicroModel Access database with an Excel add-in, developed by the IFR, which can carry out the predicting procedures. This would enable users to obtain more sophisticated predictions (e.g. under changing in temperatures) and it would also facilitate comparisons of predicted curves with raw data.
2. Implement dynamic models for growth
Take account of the previous history of the cells (e.g. temperature profile) by introducing the physiological state parameter. Estimate lag by means of the parameter. The physiological state parameter quantifies the history effect (e.g. the effect of processing of the food before growth).
3. Estimate errors of prediction
To provide for Food MicroModel users with a measure of reliability of predictions. A mathematically well-established method for error estimation will be added to the predictions.
4. Provide indicators for model acceptability
There are no agreed standard measures to indicate when a predictive model is mathematically and biologically acceptable. Appropriate user-friendly indicators (bias and precision, robustness, etc) would greatly improve the credibility of predictive models and the Food MicroModel software. Those indicators would be calculated when remodelling the data and validating the improved and any new models.
5. Improve model capability to predict no-growth
Current Food MicroModels have been developed using growth data only from experiments carried out in broth. This has resulted in models that tend to estimate the 'worst-case' scenario – growth that is faster than is likely to occur in foods. Such models are especially conservative around the growth/no-growth boundary. The unified database IFR created (containing both the original experimental and literature data) can be used to develop a model that coincides with the validated growth model in the growth region and zero growth in the no-growth region. This requires new effort to model the interface between growth and no-growth regions.
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