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Article

Evolutionary Optimisation of Beer Organoleptic Properties: A Simulation Framework

1
School of Computing & Mathematical Sciences, University of Greenwich, London SE10 9LS, UK
2
National Institute of Informatics, Tokyo 101-8430, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Maurizio Ciani
Foods 2022, 11(3), 351; https://doi.org/10.3390/foods11030351
Received: 3 November 2021 / Revised: 20 December 2021 / Accepted: 21 January 2022 / Published: 26 January 2022
(This article belongs to the Special Issue New Strategies to Improve Beer Quality)
Modern computational techniques offer new perspectives for the personalisation of food properties through the optimisation of their production process. This paper addresses the personalisation of beer properties in the specific case of craft beers where the production process is more flexible. Furthermore, this work presents a solution discovery method that could be suitable for more complex, industrial setups. An evolutionary computation technique was used to map brewers’ desired organoleptic properties to their constrained ingredients to design novel recipes tailored for specific brews. While there exist several mathematical tools, using the original mathematical and chemistry formulas, or machine learning models that deal with the process of determining beer properties based on the predetermined quantities of ingredients, this work investigates an automated quantitative ingredient-selection approach. The process, which was applied to this problem for the first time, was investigated in a number of simulations by “cloning” several commercial brands with diverse properties. Additional experiments were conducted, demonstrating the system’s ability to deal with on-the-fly changes to users’ preferences during the optimisation process. The results of the experiments pave the way for the discovery of new recipes under varying preferences, therefore facilitating the personalisation and alternative high-fidelity reproduction of existing and new products. View Full-Text
Keywords: food personalisation; beer optimisation; recipe discovery; dispersive flies optimisation food personalisation; beer optimisation; recipe discovery; dispersive flies optimisation
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MDPI and ACS Style

al-Rifaie, M.M.; Cavazza, M. Evolutionary Optimisation of Beer Organoleptic Properties: A Simulation Framework. Foods 2022, 11, 351. https://doi.org/10.3390/foods11030351

AMA Style

al-Rifaie MM, Cavazza M. Evolutionary Optimisation of Beer Organoleptic Properties: A Simulation Framework. Foods. 2022; 11(3):351. https://doi.org/10.3390/foods11030351

Chicago/Turabian Style

al-Rifaie, Mohammad Majid, and Marc Cavazza. 2022. "Evolutionary Optimisation of Beer Organoleptic Properties: A Simulation Framework" Foods 11, no. 3: 351. https://doi.org/10.3390/foods11030351

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