Bayesian D- and I-optimal designs for choice experiments involving mixtures and process variables

TL;DR

This paper develops Bayesian D- and I-optimal experimental designs for choice experiments involving mixtures of ingredients and process variables, enabling better modeling of consumer preferences for processed food products.

Contribution

It introduces a method to generate optimal designs for combined mixture-process variable choice experiments, integrating Bayesian approaches with D- and I-optimality criteria.

Findings

Comparison of D- and I-optimal designs in two examples

Method for modeling data from mixture-process variable experiments

Framework for designing choice experiments involving mixtures and processing

Abstract

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.