Predictive microbial modelling is a promising and rapidly evolving discipline of food microbiology, and continues to be underpinned by state-of-the-art science without losing sight of practical considerations including validation in foods, and specific purposes supporting food safety management approaches or evaluating traditional and innovative food processing techniques. It has evolved over the last 35 years and it has been accepted by industry and regulatory authorities as a preferable approach to predict safety and shelf-life of foods.

The course aims to provide an overview of the predictive microbiology models used to represent and predict the responses of microbial populations including pathogens to environmental conditions and intrinsic food properties that may or may not change during food production. Phases of bacterial growth, phases of bacterial inactivation, factors affecting those kinetics and types of models will be taught as well as use and interpretation of the predictive models and predicted outcomes for untested conditions. Understanding of the predictive microbiology approaches will be reinforced by a series of illustrations of model fitting using the R software. Demonstrations will show the performance of the predictive models as well as the uncertainty associated with those predictions. The workshop will describe and demonstrate how the current computer programmes of the U.S. Department of Agriculture, Agricultural Research Service, can be used to predict the behaviour of the pathogens in foods. The programs include:

• Predictive Microbiology Information Portal (PMIP);
• Integrated Pathogen Modeling Program (IPMP);
• ComBase.

By participating in this workshop, attendees will better understand how to use these tools to enhance the safety of food. These skills can be used also in the interest of better product formulation, experimental design and improved risk assessment. At the end of the course, the participant will be able to:

• Understand the quantitative microbial ecology of foods, and develop interfaces with other disciplines to apply the knowledge containing in predictive models.
• Fit and construct a variety of models to characterise microbial growth, survival and inactivation as affected by environmental conditions using the R software.
• Assess the effect of intrinsic barriers in food formulation and evaluate intervention strategies during processing to ensure food safety.
• Apply predictive microbiology modelling in the major systems of food safety management: HACCP and quantitative microbial risk assessment.
• Address diverse food safety problems using the ready-to-use online tools of predictive microbiology in foods.