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Evolutionary Optimisation of Beer Organoleptic Properties: A Simulation Framework.

Mohammad Majid Al-Rifaie¹, Marc Cavazza²

¹School of Computing & Mathematical Sciences, University of Greenwich, London SE10 9LS, UK.

Foods (Basel, Switzerland)

|February 15, 2022

Summary

Computational techniques enable personalized craft beer by optimizing ingredient selection for desired taste profiles. This automated approach allows for novel recipe discovery and high-fidelity reproduction of existing beers.

Keywords:

beer optimisation dispersive flies optimisation food personalisation recipe discovery

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Area of Science:

Food Science
Computational Chemistry
Process Engineering

Background:

Personalization of food properties is increasingly achievable through advanced computational methods.
Craft beer production offers flexibility for optimizing processes to meet specific consumer demands.

Purpose of the Study:

To present a novel solution discovery method for automated quantitative ingredient selection in craft beer production.
To tailor beer recipes to desired organoleptic properties using evolutionary computation.

Main Methods:

An evolutionary computation technique was employed to map desired organoleptic properties to ingredient constraints.
Simulations were conducted by "cloning" commercial beer brands to validate the ingredient-selection approach.
Experiments demonstrated the system's adaptability to real-time changes in user preferences.

Main Results:

The study successfully demonstrated an automated quantitative ingredient-selection process for personalized beer recipes.
The system effectively mapped desired taste profiles to ingredient combinations.
The method proved capable of handling dynamic adjustments to consumer preferences during optimization.

Conclusions:

The developed system facilitates the discovery of new beer recipes and the high-fidelity reproduction of existing ones.
This approach enables personalized food product development, particularly in flexible production environments like craft brewing.
The findings pave the way for advanced, data-driven recipe design in the food industry.