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APPLICATION OF ARTIFICIAL INTELLIGENCE FOR PREDICTING BEER FLAVOURS FROM CHEMICAL ANALYSIS

C.I. Wilson & L.Threapleton
Coors Brewers, Technical Centre, P.O Box 12, Cross Street, Burton-on-Trent, DE14 1XH, UK

INTRODUCTION

We all work in an industry where the consumer is king. We are constantly trying to evolve our products to satisfy the consumer’s changing requirements whilst at the same time always looking for the opportunity to develop niche products for new markets. However the relationship between beer flavour and its chemical analysis is poorly understood. Should it prove possible to predict final beer flavours according to their chemical composition, then it would open up the possibility of 'tuning' such products to meet the expectations of the consumer. The challenge is “Can Beer Flavour Be Predicted From Analytical Results ?”

Substantial empirical data exists, in disparate data sources, concerning product chemical and sensory analysis. However, currently there is no mechanism for linking them to each other. Any such relationships are undoubtedly complex and highly non-linear. In order to identify such relationships we have turned our attention to the modern techniques of artificial intelligence, and specifically neural networks and genetic algorithms. The former is associated with machine learning whilst the latter is associated with biological evolution. The development of both these fields can be traced back to the 1960s. However it is only recently, with the rapid expansion in computing power combined with the availability of packaged software solutions that these techniques have been moved from the computer science laboratory into industry.

NOTE: The above is an abstract to a paper presented at the 29th European Brewing Congress in Dublin. The entire paper is available for download in Word format by clicking here.

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