Future Trends and Perspectives

Abstract

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.