Designing Food with Bayesian Belief Networks

The food industry is highly competitive, and in order to survive, manufacturers must constantly innovate and match the ever changing tastes of consumers. A recent survey [1] found that 90% of the 13,000 new food products launched each year in the US fail within one year. Food companies are therefore changing the way new products are developed and launched, and this includes the use of intelligent computer systems. This paper provides an overview of one particular technique, namely Bayesian Belief networks, and its application to a typical food design problem. The characteristics of an "ideal" product are derived from a small data set.