Experimental Design for Predictive Models in Microbiology Depending on Environmental Variables
Paper Science Python
doi.org/10.1007/978-1-0716-4112-5_4
The aim of predictive microbiology is the provision of tools and methods for predicting the growth, survival, and death of microorganisms in different food matrices under a range of environmental conditions. The parametrized mathematical models need to be calibrated using dedicated experimental data. In order to efficiently plan experiments, model-based experimental design is used. In this chapter, we explain model-based experimental design and provide step-by-step instructions for finding the optimal design using the well-known Baranyi-Roberts growth model as an example. We provide the Python software eDPM for Ordinary Differential Equation (ODE)-based models, such that the reader can apply model-based experimental design in their research context.