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Obtaining data for improving food safety management systems is often required to assist decision making in a short timeframe, potentially allowing decisions to be made and practices to be implemented in real time. Collection, storage, and retrieval of new data regarding microbial responses in foods gain insight on the achievement of food safety management measures (i.e., food safety objectives, performance objectives), avoiding the increase of fail-dangerous events. The role of data bases in predictive microbiology has been widely demonstrated as useful tools for the development of computing software or tertiary models, which allow users to estimate growth, survival, or inactivation of food-borne pathogens and spoilage microorganisms in different food matrices. Additionally, the fast development of information and communication technologies (ICTs) has increased the software tools available in predictive microbiology. These tools, named tertiary models, are created for a wide range of applications and types of users: scientists, food operators, risk managers, etc. Although early versions were designed as standalone systems, nowadays on-line software is a major trend making tools available everywhere to everyone through the Internet. In this chapter, descriptive examples of data bases and software tools used in predictive microbiology are explained.

Food-borne pathogens associated with food products are a major concern of both industries and governments; thus, design of proper risk mitigation and elimination strategies is required. Currently, the great development showed by the scientific method and the tendency to optimize processes through its systematization has led to the necessity to unify and standardize food safety management processes. With this, it is not intended to abandon the approach that has prevailed historically, based on consultations of experts and use of ‘default values’ (conservative control limits and measures which establish the guarantee of the safety of a process or food), but complete foundations to improve its result through a structured approach based on scientific facts. In this respect, the World Trade Organization (WTO) (The WTO agreement on the application of sanitary and phytosanitary measures (SPS Agreement), 1995), according to the agreements of the General Agreement on Tariffs and Trade (GATT) and Sanitary and Phytosanitary Measures (SPS), proposes that to ensure fair and safe international trade, standards and harmonized food regulation need to be established, based on a scientific and rigorous approach, recommending for that the application of methods of risk assessment. Application of predictive models within a risk assessment framework is presented throughout this chapter.

New methodologies have been proposed to be incorporated in predictive microbiology in foods and quantitative microbial risk assessment (QMRA) to achieve more reliable models and facilitate predictive model applications. The meta-analysis is one of the proposed strategies focused on a systematic analysis of a large collection of data with the intention of generating standardized and summarized information to produce a global estimate. This data analysis approach can be applied to better understand the relationship between environmental factors and kinetic parameters or to input QMRA studies to assess the effect of a particular intervention or treatment concerning food safety. The emergence of systems biology is also affecting predictive microbiology, offering new and more mechanistic approaches to yield more reliable and robust predictive models. The so-called genomic-scale models are built on a molecular and genomic basis supported by experimental data obtained from the genomic, proteomic, and metabolomic research areas. Although the existing gene-scale models are promising regarding prediction capacity, they are still few and limited to specific model microorganisms and situations. Further research is needed, in the coming decades, to complete omics information and thus to produce more suitable models to be applied to real-world situations in food safety and quality.

Predictive models have been initially focused on the estimation of kinetic parameters, as described in the preceding chapter. However, other modeling approaches are often requested, especially when considering the transmission of a pathogen along the food chain or the probability that this pathogen can grow or survive at certain environmental conditions. This is the underlying reason why transfer and growth/no growth models presented a relevant development in predictive microbiology. These models can be effectively applied when presence/absence data are required, or in specific food processes. Alternatively, survival and transmission of microorganisms through food contact surfaces, environment and between different foods can be also estimated. Additional advantages, such as the extent of shelf life, or the effect of novel preservation methods in minimally processed foods provide a wider application of predictive microbiology. Bacterial transfer models and growth/no growth models are described in this chapter.

According to their structure, predictive models can be primary, secondary, or tertiary. This classification mainly depends on the final purpose and type of prediction generated. There has been a significant evolution in the past few years toward better understanding of microbial behavior in foods. Therefore, models that describe the biological process of microbial growth and inactivation have been subsequently developed. Also, fitting methods for linear and nonlinear regression together with goodness-of-fit indexes give us useful information about how the model is able to explain the observed data. Finally, models cannot be applied if a validation process is not previously accomplished, which typically consists of confirming the predictions experimentally by using any quantitative method. In this chapter, a comprehensive review of the most popular validation methods is provided.

One of the most critical steps when generating a predictive model is to correctly design an experiment and collect suitable microbial data. Experimental design will influence model structure and validation conditions. The survival and growth of microorganisms in foods is affected not only by the chemical composition of the food and its storage conditions but also by the food matrix. In this sense, a better quantification of the food structure effect has been studied throughout these years. Regarding the method of data collection, although plating count has been widely used (and still is used), there are rapid methods to obtain reliable and cost-effective data. These achievements were primarily based on turbidimetry, although other methods (microscopy, image analysis, flow cytometry, etc.) have arisen as novel approaches in the predictive microbiology field. These aspects are further discussed in this chapter.