Sensitivity analysis in quantitative microbial risk assessment.

The occurrence of foodborne disease remains a widespread problem in both the developing and the developed world. A systematic and quantitative evaluation of food safety is important to control the risk of foodborne diseases. World-wide, many initiatives are being taken to develop quantitative risk analysis. However, the quantitative evaluation of food safety in all its aspects is very complex, especially since in many cases specific parameter values are not available. Often many variables have large statistical variability while the quantitative effect of various phenomena is unknown. Therefore, sensitivity analysis can be a useful tool to determine the main risk-determining phenomena, as well as the aspects that mainly determine the inaccuracy in the risk estimate. This paper presents three stages of sensitivity analysis. First, deterministic analysis selects the most relevant determinants for risk. Overlooking of exceptional, but relevant cases is prevented by a second, worst-case analysis. This analysis finds relevant process steps in worst-case situations, and shows the relevance of variations of factors for risk. The third, stochastic analysis, studies the effects of variations of factors for the variability of risk estimates. Care must be taken that the assumptions made as well as the results are clearly communicated. Stochastic risk estimates are, like deterministic ones, just as good (or bad) as the available data, and the stochastic analysis must not be used to mask lack of information. Sensitivity analysis is a valuable tool in quantitative risk assessment by determining critical aspects and effects of variations.

Stepwise quantitative risk assessment as a tool for characterization of microbiological food safety.

This paper describes a system for the microbiological quantitative risk assessment for food products and their production processes. The system applies a stepwise risk assessment, allowing the main problems to be addressed before focusing on less important problems. First, risks are assessed broadly, using order of magnitude estimates. Characteristic numbers are used to quantitatively characterize microbial behaviour during the production process. These numbers help to highlight the major risk-determining phenomena, and to find negligible aspects. Second, the risk-determining phenomena are studied in more detail. Both general and/or specific models can be used for this and varying situations can be simulated to quantitatively describe the risk-determining phenomena. Third, even more detailed studies can be performed where necessary, for instance by using stochastic variables. The system for quantitative risk assessment has been implemented as a decision supporting expert system called SIEFE: Stepwise and Interactive Evaluation of Food safety by an Expert System. SIEFE performs bacterial risk assessments in a structured manner, using various information sources. Because all steps are transparent, every step can easily be scrutinized. In the current study the effectiveness of SIEFE is shown for a cheese spread. With this product, quantitative data concerning the major risk-determining factors were not completely available to carry out a full detailed assessment. However, this did not necessarily hamper adequate risk estimation. Using ranges of values instead helped identifying the quantitatively most important parameters and the magnitude of their impact. This example shows that SIEFE provides quantitative insights into production processes and their risk-determining factors to both risk assessors and decision makers, and highlights critical gaps in knowledge.