This work introduces a speci c application of Bayesian nonparamet-
ric statistics to the food risk analysis framework. The goal was to determine the
cocktails of pesticide residues to which the French population is simultaneously
exposed through its current diet in order to study their possible combined e ects
on health through toxicological experiments. To do this, the joint distribution of
exposures to a large number of pesticides, which we called the co-exposure distri-
bution, was assessed from the available consumption data and food contamination
analyses. We propose modelling the co-exposure using a Dirichlet process mixture
based on a multivariate Gaussian kernel so as to determine groups of individuals
with similar co-exposure patterns. Posterior distributions and optimal partition
were computed through a Gibbs sampler based on stick-breaking priors. The study
of the correlation matrix of the sub-population co-exposures will be used to de ne
the cocktails of pesticides to which they are jointly exposed at high doses. To
reduce the computational burden due to the high data dimensionality, a random-
block sampling approach was used. In addition, we propose to account for the
uncertainty of food contamination through the introduction of an additional level
of hierarchy in the model. The results of both speci cations are described and
compared.