Predicting and improving complex beer flavor through machine learning

Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni, Lloyd Cool, Beatriz Herrera-Malaver, Christophe Vanderaa, Florian A. Theßeling, Łukasz Kreft, Alexander Botzki, Philippe Malcorps, Luk Daenen, Tom Wenseleers and Kevin J. Verstrepen ()
Additional contact information
Michiel Schreurs: VIB—KU Leuven Center for Microbiology
Supinya Piampongsant: VIB—KU Leuven Center for Microbiology
Miguel Roncoroni: VIB—KU Leuven Center for Microbiology
Lloyd Cool: VIB—KU Leuven Center for Microbiology
Beatriz Herrera-Malaver: VIB—KU Leuven Center for Microbiology
Christophe Vanderaa: KU Leuven
Florian A. Theßeling: VIB—KU Leuven Center for Microbiology
Łukasz Kreft: VIB Bioinformatics Core, VIB
Alexander Botzki: VIB Bioinformatics Core, VIB
Philippe Malcorps: AB InBev SA/NV
Luk Daenen: AB InBev SA/NV
Tom Wenseleers: KU Leuven
Kevin J. Verstrepen: VIB—KU Leuven Center for Microbiology

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: Abstract The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors.

Date: 2024
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DOI: 10.1038/s41467-024-46346-0

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