  Metabolomic based approaches have gained much attention in recent years due
to their promising potential to deliver objective tools for assessment of food
intake. In particular, multiple biomarkers have emerged for single foods.
However, there is a lack of statistical tools available for combining multiple
biomarkers to infer food intake. Furthermore, there is a paucity of approaches
for estimating the uncertainty around biomarker based prediction of intake.
  Here, to facilitate inference on the relationship between multiple
metabolomic biomarkers and food intake in an intervention study conducted under
the A-DIET research programme, a latent variable model, multiMarker, is
proposed. The proposed model draws on factor analytic and mixture of experts
models, describing intake as a continuous latent variable whose value gives
raise to the observed biomarker values. We employ a mixture of Gaussian
distributions to flexibly model the latent variable. A Bayesian hierarchical
modelling framework provides flexibility to adapt to different biomarker
distributions and facilitates prediction of the latent intake along with its
associated uncertainty.
  Simulation studies are conducted to assess the performance of the proposed
multiMarker framework, prior to its application to the motivating application
of quantifying apple intake.
