logo
ResearchBunny Logo
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!

00:00
00:00
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

Related Publications

Explore these studies to deepen your understanding of the subject.

Synchronously Predicting Tea Polyphenol and Epigallocatechin Gallate in Tea Leaves Using Fourier Transform-Near-Infrared Spectroscopy and Machine Learning
Food Science and Technology

Synchronously Predicting Tea Polyphenol and Epigallocatechin Gallate in Tea Leaves Using Fourier Transform-Near-Infrared Spectroscopy and Machine Learning

S. Ye, H. Weng, et al.

Tracking and tracing water consumption for informed water sensitive intervention through machine learning approach
Environmental Studies and Forestry

Tracking and tracing water consumption for informed water sensitive intervention through machine learning approach

A. T. Abraha, T. A. Woldeamanuel, et al.

Radiogenomics for predicting p53 status, PD-L1 expression, and prognosis with machine learning in pancreatic cancer
Medicine and Health

Radiogenomics for predicting p53 status, PD-L1 expression, and prognosis with machine learning in pancreatic cancer

Y. Iwatate, I. Hoshino, et al.

StratoMod: predicting sequencing and variant calling errors with interpretable machine learning
Computer Science

StratoMod: predicting sequencing and variant calling errors with interpretable machine learning

N. Dwarshuis, P. Tonner, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny