  Shifting from traditional hazard-based food safety management toward
risk-based management requires statistical methods for evaluating intermediate
targets in food production, such as microbiological criteria (MC), in terms of
their effects on human risk of illness. A fully risk-based evaluation of MC
involves several uncertainties that are related to both the underlying
Quantitative Microbiological Risk Assessment (QMRA) model and the
production-specific sample data on the prevalence and concentrations of
microbes in production batches. We used Bayesian modeling for statistical
inference and evidence synthesis of two sample data sets. Thus, parameter
uncertainty was represented by a joint posterior distribution, which we then
used to predict the risk and to evaluate the criteria for acceptance of
production batches. We also applied the Bayesian model to compare alternative
criteria, accounting for the statistical uncertainty of parameters, conditional
on the data sets. Comparison of the posterior mean relative risk,
$E(\mathit{RR}|\mathrm{data})=E(P(\mathrm{illness}|\mathrm{criterion is
met})/P(\mathrm{illness})|\mathrm{data})$, and relative posterior risk,
$\mathit{RPR}=P(\mathrm{illness}|\mathrm{data, criterion is
met})/P(\mathrm{illness}|\mathrm{data})$, showed very similar results, but
computing is more efficient for RPR. Based on the sample data, together with
the QMRA model, one could achieve a relative risk of 0.4 by insisting that the
default criterion be fulfilled for acceptance of each batch.
