In this thesis a new probabilistic model in predictive microbiology, the NPMPM, is presented. It is based on a new approach for including variability and uncertainty. The NPMPM was developed for risk assessment of bacterial contaminations in the food supply chain. It is introduced by the example of a contamination of the milk supply chain with Listeria monocytogenes. Human illness that results from the consumption of contaminated food may be caused by bacteria and their toxins. Hence, assessment of growth and tenacity of bacteria during food production processes and storage is vital for food security. Predictive modelling is used to forecast the development of microorganisms in food, depending on different influence factors. Getting the desired information solely by means of laboratory experiments is time and cost intensive. Biological processes like growth and death are highly variable. Existing approaches that take into account variability and uncertainty either assume parameters to follow probability distributions, or model a biological process as stochastic process. The NPMPM follows a newly developed approach. It calculates samples of possible bacterial concentrations by means of deterministic models. Such a sample is used for estimation of the probability distribution of the corresponding population. This thesis is an interdisciplinary one. In the first chapter the research approach is motivated, and an overview of the organisation of this thesis is given. The second chapter describes causes and impact of foodborne diseases. Chapter three discusses influencing factors on growth and survival kinetics of bacterial populations. In chapter four dairy manufacturing in Germany is summarised. The fifth chapter presents existing models in predictive microbiology. Chapter six gives an overview of data and methods used, and a discussion of model assumptions. In chapter seven the NPMPM is introduced. The model is validated in chapter eight, and results are discussed. Finally, in chapter nine the contributions to the field of predictive microbiology are summarised, and some future work is suggested.