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Predicting and improving complex beer flavor through machine learning

Food Science and Technology

Predicting and improving complex beer flavor through machine learning

M. Schreurs, S. Piampongsant, et al.

This fascinating study by Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni and colleagues delves into the world of beer, analyzing 250 varieties to unveil the secrets behind flavor and consumer appreciation. Utilizing gradient boosting machine learning models, it not only predicts complex flavor features but also identifies key chemicals that enhance beer variants. Cheers to science!

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Playback language: English
Abstract
This study combines chemical and sensory analyses of 250 beers to train machine learning models predicting flavor and consumer appreciation. Over 200 chemical properties were measured for each beer, along with quantitative descriptive sensory analysis and data from over 180,000 consumer reviews. Gradient Boosting yielded the best-performing model, significantly outperforming conventional statistics in accurately predicting complex flavor features and consumer appreciation. Model dissection identified specific compounds driving flavor and appreciation, leading to improved commercial beer variants.
Publisher
Nature Communications
Published On
Mar 26, 2024
Authors
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, Kevin J. Verstrepen
Tags
beer
machine learning
flavor prediction
consumer appreciation
chemical analysis
gradient boosting
sensory analysis

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