  Many food products involve mixtures of ingredients, where the mixtures can be
expressed as combinations of ingredient proportions. In many cases, the quality
and the consumer preference may also depend on the way in which the mixtures
are processed. The processing is generally defined by the settings of one or
more process variables. Experimental designs studying the joint impact of the
mixture ingredient proportions and the settings of the process variables are
called mixture-process variable experiments. In this article, we show how to
combine mixture-process variable experiments and discrete choice experiments,
to quantify and model consumer preferences for food products that can be viewed
as processed mixtures. First, we describe the modeling of data from such
combined experiments. Next, we describe how to generate D- and I-optimal
designs for choice experiments involving mixtures and process variables, and we
compare the two kinds of designs using two examples.

Methodology;Applications