The hazard control in international trade becomes an important economic issue in the context of globalization and new challenges are addressed to food industrials to produce safe ready-to-eat foods. In quantitative HACCP plan (ILSI, 2004), identifying the physico-chemical and process parameters having an impact on the safety of the products is necessary and setting the interval of these parameters that meet the FSO is worth interesting. Quantitative Microbiological Risk Assessment models (QMRA) provide an interesting tool for this issue and one of the objectives of these models is to help the quality manager selecting the interval of these parameters values that would contribute to respect a fixed FSO. In this paper, we propose a methodology for establishing the link between process parameters and FSO in the case of a complex stochastic dynamic QMRA models with many uncertain parameters. Multivariate Sensitivity Analysis, MSA (Lamboni et al., 2010) for parameters screening applied to a risk output and an extended Generalized Likelihood Uncertainty Estimation (Beven and Binley, 1992) are combined to identify the Critical Control Points of L. monocytogenes control in the cheese food chain and to set the process parameters distributions that meet the FSO.
MSA extends the classical Sensitivity Analysis (Saltelli et al., 2008) to deal with multivariate or dynamics aspect of the model by using inertia as the importance measure. MSA decomposes inertia like a part of inertia explained by all input factors and their interactions. Extended GLUE give a raw joint pdf distribution and marginal distributions are computed by using the kernel density estimation.
K. Beven and Binley. Future of distributed modeling. Hydrology Processes, 6:253-254, 1992.
ILSI. A simple guide to understanding and applying the hazard analysis critical control point concept. concise monograph series third edition, ILSI Europe, Avenue E. Mounier 83 Box 6, B-1200 Bruxels, 2004.
M. Lamboni, H. Monod, and D. Makowski. Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models. Reliability Engineering and System Safety, doi:10.1016/j.ress.2010.12.002, 2010.
A. Saltelli, M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli, M. Saisana, and S. Tarantola. Global Sensitivity Analysis: The Primer. Wiley, 2008.
Keywords: Multivariate Sensitivity Analysis; GLUE; Kernel density estimation; LHS
Biography: Dr M. Lamboni; researcher at University Paris Descartes/EDF of France. Research topics: multivariate sensitivity Analysis.