Beer Organoleptic Optimisation: Utilising Swarm Intelligence and Evolutionary Computation Methods

Abstract

Customisation in food properties is a challenging task involving optimisation of the production process with the demand to support computational creativity which is geared towards ensuring the presence of alternatives. This paper addresses the personalisation of beer properties in the specific case of craft beers where the production process is more flexible. We investigate the problem by using three swarm intelligence and evolutionary computation techniques that enable brewers to map physico-chemical properties to target organoleptic properties to design a specific brew. While there are several tools, using the original mathematical and chemistry formulas, or machine learning models that deal with the process of determining beer properties based on the pre-determined quantities of ingredients, the next step is to investigate an automated quantitative ingredient selection approach. The process is illustrated by a number of experiments designing craft beers where the results are investigated by "cloning" popular commercial brands based on their known properties. Algorithms performance is evaluated using accuracy, efficiency, reliability, population-diversity, iteration-based improvements and solution diversity. The proposed approach allows for the discovery of new recipes, personalisation and alternative high-fidelity reproduction of existing ones.