Abstract
The beginnings of predictive microbiology date back to 1920 when Bigelow developed a logarithmic-linear dependence of kinetics on the death of microorganisms. Predictive microbiology is a sub-discipline of food microbiology, whose task is to predict the behavior of microorganisms in food using mathematical models. The predictive model for microbiology is usually a simplified description of the correlation between the observed reactions and the factors responsible for the occurrence of these reactions. There are several main conceptual models (empirical vs. mechanistic, stochastic vs. deterministic, dynamic vs. static), in which there are model divisions depending on the type of examined microorganism or the nature of the problems caused by microbes (kinetic vs. probabilistic), described variables (first, secondary and tertiary) or the influence of environmental factors on microbial populations (growth, survival, inactivation). The new generations of models include molecular and genomic models, transfer models, Artificial Neural Network, interactions between species, and single cell models.
The process of creating a mathematical model requires coordination of work and the knowledge of: microbiology, statistics, mathematics, chemistry, process engineering and computer and web science. It also requires appropriate hardware and software. There are four stages in the construction of a mathematical model: planning; data collection and analysis; mathematical description; validation and storage of data.
In recent years, numerous computer software programs have been developed: FISHMAP, FSSP, Dairy Product Safety Predictor, Symbiosis, GroPIN, Listeria Meat FDA-iRISK, TRiMiCri, Microbial Responses, GlnaFiT, FILTREX, PMM-Lab. ComBase database, on the other hand, is a pioneering achievement as an on-line tool. Some programs meet the requirements for creating Food Safety Model Repositories (FSMR).
1. Introduction. 2. The idea of predictive microbiology. 3. Historical background of predictive microbiology. 4. The concept of a model and modeling concepts in food microbiology. 4.1. Concept 1: empirical vs. mechanistic models. 4.2. Concept 2: static vs. dynamic models. 4.3. Concept 3: stochastic vs. deterministic models. 5. Breakdowns of prognostic models. 5.1. Neural networks. 5.2. A new generation of predictive models. 6. The construction of the predictive model. 6.1. Planning the experiment. 6.2. Collection of data. 6.3. Data analysis. 6.4. Model validation. 7. Predictive microbiology in risk analysis. 8. Summary