The workshop is structured in theoretical sessions encompassing mainly topics on microbial growth, inactivation/survival and likelihood of growth/inactivation as a function of intrinsic and extrinsic factors of foods.The practical sessions consist of model fitting exercises to be conducted using the R software. The workshop will also describe and demonstrate the current ready-to-use computer applications developed to predict the dynamics of microorganisms in foods. At the end of the workshop, if requested, the instructors will give advice to the participants on their particular problems related to food microbiological safety and modelling.

The four-day sessions are:

Day 1: 9h00 – 12h00

Theory:     Dynamics of microorganisms in foods, intrinsic and extrinsic factors affecting microbial dynamics. Predictive microbiology. Underlying principles of predictive microbiology. Experimental design and data collection. Regulatory perspective on the use of predictive microbiology

Day 1: 14h00 – 17h00

Theory:     Type of models: empirical and mechanistic; primary, secondary and tertiary. Primary growth models: first-order linear model, D-value, non-linear modelling. Lag-phase.
Practice:    Getting acquainted with R environment. Fitting linear and nonlinear growth models using R. Evaluation of model fitting quality and interpretation

Day 2: 9h00 – 12h00

Theory:    Secondary growth models: Arrhenius, square-root, gamma concept, polynomial models. Lag-phase.
Practice:    Fitting secondary growth models for growth rate and lag-phase using R. Evaluation of model fitting quality and interpretation. Performance of the predictive models and uncertainty.

Day 2: 14h00 – 17h00

Theory:    Primary inactivation models: first-order and nonlinear inactivation models
Practice:    Fitting primary inactivation models using R. Evaluation of model fitting quality and interpretation. Performance of the predictive models and uncertainty.

Day 3: 9h00 – 13h00

Theory:    Secondary inactivation models: Innovative methods in predictive microbiology. Omnibus non-linear modelling. Predictive models in dynamic conditions.
Practice:    Fitting secondary inactivation models and omnibus modelling. Modelling fate studies.

Day 3:Afternoon

Social gathering: Visit to cultural centres.

Day 4: 9h00 – 13h00

Theory:     Tertiary models: Ready-to-use computer applications for predictive microbiology (PMIP, PMP, ComBase).
Practice:    Hands-on demonstrations of the tools. Real food scenarios. Solving particular problems of the attendees.

 

The participants will get a (voluntary) homework assignment that they can send to the teachers no later than four weeks after the course.

In case of a positive assessment of the homework assignment, the participants will get a certificate that they passed this MSc-level course and that it had a work load of 70 hours, which corresponds to 2.5 ECTS credit points.

PhD students can alternatively choose to write a short paper, where they analyse their own data set with the methods that they learned in the course. If they send the paper to the teacher no later than three months after the course and their paper is positively assessed, they will get a certificate that they passed this PhD-level course and that it had a work load of 170 hours, which corresponds to 6 ECTS credit points.