DETERMINISTIC VERSUS STOCHASTIC RISK ASSESSMENT
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.
UNCERTAINTY AND VARIABILITY IN RISK ASSESSMENTS
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.
LIMITATIONS AND CHALLENGES OF RISK ASSESSMENT IN FOODS
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).
CURRENT DEVELOPMENTS AND FUTURE PERSPECTIVES
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.
Berjia, F. L., R. Andersen, J. Hoekstra, M. Poulsen, and M. Nauta. 2012. "Risk Benefit Assessment of Cold-Smoked Salmon: Microbial Risk Versus Nutritional Benefit." European Journal of Food Research & Review 2: 49-68.
Boue, G., E. Cummins, S. Guillou, J. R Antignac, B. Le Bizec, and J. M. Membre. 2017. "Development and Application of a Probabilistic Risk-Benefit Assessment Model for Infant Feeding Integrating Microbiological, Nutritional, and Chemical Components." Risk Analysis 37(12): 2360-88.
Boue, G., S. Guillou, J. P. Antignac, B. Le Bizec, and J. M. Membre. 2015. "Public Health Risk-Benefit Assessment Associated with Food Consumption — A Review." European Journal of Nutrition & Food Safety 5(1): 32-58.
Brimer, Leon. 2011. Chemical Food Safety. Edited by Leon Brimer. 1st ed. Cambridge: Cambridge University Press.
Brul, S., J. Bassett, P. Cook, S. Kathariou, P. McClure, P. R. Jasti, and R. Betts. 2012. "'Omics' Technologies in Quantitative Microbial Risk Assessment." Trends in Food Science and Technology 27(31): 12-24.
Codex Alimentarius Commission. 1999. "Principles and Guidelines for the Conduct of Microbiological Risk Assessment." In: Joint FAO/WHO Food Standards Programme (Ed.), CAC/GL-30, Rome.
Cummins, Enda. 2017. "Fundamental Principles of Risk Assessment." In: Quantitative Tools for Sustainable Food and Energy in the Food Chain, edited by V. P. Valdramidis, E. Cummins, and J. Van Impe, 151-72. Ostend, Belgium: Eurosis-ETI.
den Besten, Heidy M. W., Alejandro Amezquita, Sara Bover-Cid, Stephane Dagnas, Mariem Ellouze, Sandrine Guillou, George Nychas, Cian O'Mahony, Fernando Perez-Rodriguez, and Jeanne Marie Membre. 2017 "Next Generation of Microbiological Risk Assessment: Potential of Omics Data for Exposure Assessment." International Journal of Food Microbiology 287: 1-10. doi:10.1016/j.ijfoodmicro.2017.10.006.
Dennis, S. В., K. Kause, M. Losikoff, D. L. Engeljohn, and R. L. Buchanan. 2008. "Using Risk Analysis for Microbial Food Safety Regulatory Decision Making." In: Microbial Risk Analysis of Foods, edited by D. W. Schaffner, 137- 75. Washington, DC: ASM Press.
EFSA. 2018. "Chemicals in Food." http://www.efsa.europa.eu/en/topics/topic/ chemicals-food. Accessed in 02/10/2018.
FAO. 2004. "Globalization of Food Systems in Developing Countries: Impact on Food Security and Nutrition." FAO Food and Nutrition Paper 83: 107. doi:10.1186/1475-2891-10-104.
FAO/WHO. 2003. "Hazard Characterization for Pathogens in Food and Water: Guidelines." Microbiological Risk Assessment Series 3: 61.
FAO/WHO. 2008. "Exposure Assessment of Microbiological Hazards in Food: Guidelines." Microbiological Risk Assessment Series 7(61): 102.
FAO/WHO. 2009. "Principles and Methods for the Risk Assessment of Chemicals in Food." Environmental Health Criteria 240. http://www.who.int/foodsafety/ publications/chemical-food/en/.
FAO/WHO. 2013. Codex Alimentarius Commission Procedural Manual. 21st ed. Rome. http://www.fao.Org/3/a-i3243e.pdf.
Food and Drug Administration (FDA). 2005. "Quantitative Risk Assessment on the Public Health Impact of Pathogenic Vibrio parahaemolyticus in Raw Oysters." Center for Food Safety and Applied Nutrition, Food and Drug Administration, U.S. Department of Health and Human Services.
Fritsch, L., L. Guillier, and J. C. Augustin. 2018. "Next Generation Quantitative Microbiological Risk Assessment: Refinement of the Cold Smoked Salmon- Related Listeriosis Risk Model by Integrating Genomic Data." Microbial Risk Analysis 10: 20-27
Haberbeck, L. U., C. Plaza-Rodriguez, V. Desvignes, P. Dalgaard, M. Sanaa, L. Guillier, M. Nauta, and M. Filter. 2018. "Harmonized Terms, Concepts and Metadata for Microbiological Risk Assessment Models: The Basis for Knowledge Integration and Exchange." Microbial Risk Analysis 10:3-12.
IPCS. 2004. "IPCS Risk Assessment Terminology." Geneva: World Health Organization, International Programme on Chemical Safety (Harmonization Project Document, No. 1). http://www.who.int/ipcs/methods/harmoniz ation/areas/ ipcsterminologypartsland2.pdf.
Kavlock, Robert J., Tina Bahadori, Tara S. Barton-Maclaren, Maureen R. Gwinn, Mike Rasenberg, and Russell S. Thomas. 2018. "Accelerating the Pace of Chemical Risk Assessment." Review-Article. Chemical Research in Toxicology 31(5). American Chemical Society: 287-90. doi:10.1021/acs. chemrestox.7b00339.
Lammerding, Anna M., and Aamir Fazil. 2000. "Hazard Identification and Exposure Assessment for Microbial Food Safety Risk Assessment." International Journal of Food Microbiology 58(3): 147-57. doi:10.1016/S0168-1605(00)00269-5.
Membre, Jeanne Marie. 2016. "Microbiological Risk Assessments in Food Industry". In Food Hygiene and Toxicology in Ready-to-Eat Foods, edited by P. Kotzekidou, 337-50, 1st ed. Cambridge, USA: Academic Press. doi:10.1016/ B978-0-12-801916-0.00019-4.
Membre, Jeanne Marie, and Geraldine Boue. 2018. "Quantitative Microbiological Risk Assessment in Food Industry: Theory and Practical Application." Food Research International 106(August 2017). Elsevier: 1132-39. doi:10.1016/j. foodres.201711.025.
Membre, Jeanne Marie, and Sandrine Guillou. 2016. "Lastest Developments in Foodborne Pathogen Risk Assessment." Current Opinion in Food Science 8: 120-26. doi:10.1016/j.cofs.2016.04.011.
Nauta, Maarten J. 2008. "The Modular Process Risk Model (MPRM): A Structured Approach to Food Chain Exposure Assessment." In: Microbial Risk Analysis of Foods, edited by D. W. Schaffner, 99-136. Washington, DC: ASM Press.
Njage, P. M. K., and E. M. Buys. 2017. "Quantitative Assessment of Fluman Exposure to Extended Spectrum and AmpC В-Lactamases Bearing E. coli in Lettuce Attributable to Irrigation Water and Subsequent Horizontal Gene Transfer." International Journal of Food Microbiology 240:141-51.
Perez-Rodriguez, Fernando, Elena Carrasco, Sara Bover-Cid, Anna Jofre, and Antonio Valero. 2017. "Listeria monocytogenes Risk Assessment Model for Three Ready-to-Eat Food Categories in the EU." doi:10.5281/zenodo.822350.
Perez-Rodriguez, Fernando, and Antonio Valero. 2013. Predictive Microbiology in Foods. New York, USA: Springer.
Plaza-Rodriguez, C, L. U. Haberbeck, V. Desvignes, P. Dalgaard, M. Sanaa, M. Nauta, M. Filter, and L. Guillier. 2017. "Towards Transparent and Consistent Exchange of Knowledge for Improved Microbiological Food Safety." Current Opinion in Food Science 19:129-37.
Tenenhaus-Aziza, Fanny, and Mariem Ellouze. 2015. "Software for Predictive Microbiology and Risk Assessment: A Description and Comparison of Tools Presented at the ICPMF8 Software Fair." Food Microbiology 45(February). Academic Press: 290-99. doi:10.1016/J.FM.2014.06.026.
Tennant, D. R. 2012. Food Chemical Risk Analysis. Edited by D. R. Tennant. 2nd ed. London: Blackie Academic and Professional.
Vose, D. 2008. Risk Analysis: A Quantitative Guide. 3rd ed. West Sussex, England: John Wiley & Sons, Ltd.