Risk assessment models can be characterized as deterministic or stochastic with regard to how input variables are handled (Vose 2008). In the first approach, point-estimate values are used to describe the variables of the model, and only individual scenarios are analysed. Since the worst-case scenario is typically reflected, deterministic approaches are usually unrealistic or "overcautious", and the outcomes are not representative of real situations (Tennant 2012; Perez-Rodriguez and Valero 2013). In the second approach, variables are defined with probability distributions that encompass all possible scenarios, taking into account uncertainty and/or variability in those variables (Cummins 2017). Hence, stochastic approaches are more reflective of real-life scenarios.


These components are related to the level of knowledge on risk model inputs. Briefly, uncertainty is the lack of knowledge, for instance, regarding a quantity (Membre and Boue 2018). As uncertainty is usually related to analytical limitations or low precision of measurement methods, it should be minimized whenever possible by further study, for example by increasing the number of samples analysed or by improving measurement methods. The prevalence of a pathogen in a food commodity can be used to illustrate uncertainty: to ascertain prevalence with 100% certainty, 100% of the food products might be tested for the presence of the pathogen, which is not feasible. Hence, we have to rely upon available prevalence data to estimate the prevalence of the whole population, and the greater the number of samples, the higher is our degree of certainty regarding the estimate. On the other hand, variability represents the true heterogeneity in a population (Membre and Boue 2018). For instance, the ability to metabolize or detoxify chemicals can vary from person to person. This variability is not reducible by further study since it is related to natural randomness. Uncertainty and variability are described in more detail in Chapters 7 and 16.


A multidisciplinary team that supplies the variety of knowledge to handle the available scientific information is required to carry out a risk assessment. It includes professionals from different fields, such as microbiology, mathematics, epidemiology, food technology and social sciences, among others (Membre and Boue 2018). The complexity derived from this multidisciplinary approach represents a big challenge when performing a risk assessment. The lack of guides or protocols to develop risk assessments and the lack of harmonization in vocabulary or terms employed are also big limitations of the field, since the employment of a common structure would be crucial to compare hazards, risks, management measures, etc. between autonomous regions, and ideally between countries, and over time. Finally, practical guidelines to translate risk-based food safety management for operational use, as well as instructional and training resources to assist in building skills for risk assessments, must be created (Membre and Boue 2018).


The incorporation of omics technology in the exposure assessment component will move towards the next generation of microbiological risk assessment. With this technology, the behaviour of microorganisms in relation to food preservation treatments and environmental conditions will be described with mechanistic cellular information (den Besten et al. 2017; Brul et al. 2012). Njage and Buys (2017) included the potential of gene transfer between strains into the exposure to Escherichia coli due to the consumption of lettuce. Fritsch et al. (2018) worked on the refinements of a Listeria monocytogenes QMRA by integrating genomic data and considering phenogenotype associations for its hazardous properties, such as growth ability at low temperature and virulence. In addition, whole genome sequencing (WGS) has been frequently used to refine the hazard identification component of MRA (Membre and Guillou 2016).

The QMRA community has invested great effort and time to develop a rich variety of data, databases, models and software (Membre and Guillou 2016; Tenenhaus-Aziza and Ellouze 2015). However, their reusability and the information exchange between the software and the databases may currently be difficult and time consuming (Plaza-Rodriguez et al., 2017). This situation represents an obstacle to the performance of risk assessment using the most up-to-date knowledge. A recent initiative aims to establish a new community resource called Risk Assessment Modelling and Knowledge Integration Platform (RAKIP). This platform will facilitate the sharing and execution of curated QMRA and PM models using a harmonized metadata schema and information exchange format. The aim of RAKIP is to promote knowledge reusability and high-quality information exchange between stakeholders within QMRA and PM modelling (Haberbeck et al. 2018; Plaza-Rodriguez et al. 2017). Chapter 9 describes the principle of knowledge exchange in more detail.

The approaches of chemical and microbiological risk assessment and the nutritional aspects of food consumption are integrated into one of the most recent risk-based methods, the so-called risk-benefit assessments (RBAs). Currently, most RBAs integrate chemical and nutritional assessments; microbial risk is occasionally assessed, and mostly qualitatively (Boue et al. 2015). Some recent examples are the studies of Berjia et al. (2012) that integrated microbiological risks and nutritional benefits in cold smoked salmon and those of Boue et al. (2017) that integrated microbiological and chemical risks with nutritional benefits in infant feeding. RBAs are discussed in detail in Chapter 4.


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