Exposure Assessment based on a combination of event and fault tree analyses and predictive modelling

Predictive modelling is a scientific discipline that permits assessment of the impact of process stage deviations when integrated in a stage of the food chain. Predictive modelling is traditionally used to assess the exposure of consumers to the presence of hazards, e.g. Listeria monocytogenes due to the occurrence of deviations in the food chain. However, failures related to food safety can occur through the food chain and are not captured by predictive models, e.g. failure of process conditions, incorrect inspections or analyses, etc. Therefore, to address both deviations and failures, predictive modelling must be combined with other techniques. This paper presents a new approach based on a combination of traditional predictive modelling, and event/fault tree analysis techniques, which allow the representation of normal and abnormal (i.e. failures) variations of parameters throughout the food chain for a better estimation of the real impact of such deviations and failures on consumer safety. A combination of event tree and fault tree analysis techniques is adopted to represent a failure anywhere in the food chain, also including failures in the processing parameters in the food industry. For the sake of clarity in the introduction of this approach, an application example is presented considering pasteurized milk, in which human exposure to L. monocytogenes is assessed.