ArticleNature communications2024
Predicting and improving complex beer flavor through machine learning.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
26 citing papers in PubMed.
- Machine-learning-assisted screening of key flavor compounds in pumpkins.Journal of the science of food and agriculture · 2026Article
- L-Proline-Mediated Modulation of Astringency in Black Chokeberry Puree: Molecular Interactions, Process Optimization, and Quality Preservation.Foods (Basel, Switzerland) · 2026Article
- Machine learning for food flavor prediction and regulation: models, data integration, and future perspectives.Journal of advanced research · 2026Review
- Artificial intelligence for food innovation.Nature food · 2026Review
- Texture Independently Drives Liking in AI-Generated Alternative Protein Burgers.Foods (Basel, Switzerland) · 2026Article
- Decoding the Flavor Structure of Jiang-Flavor Low-Alcohol Base Baijiu: A Machine Learning-Driven Approach to Reveal the Flavor Evolution Patterns and Key Quality Control Nodes.Foods (Basel, Switzerland) · 2026Article
- Volatile and Nonvolatile Contributors to Lager Beer Acceptability in High-Flavor-Liking Consumers.Journal of agricultural and food chemistry · 2026Article
- Molecular and Computational Basis of Taste Perception: A Review toward the "Digital Language of Taste".ACS omega · 2026Review
- Machine Learning and SHAP Feature Analysis: Classification Model for Aroma Components in Green Plum Wine.Foods (Basel, Switzerland) · 2026Article
- Interpretable machine learning for the grade prediction of strong flavorFood chemistry: X · 2026Article
- Key Indicator Detection and Authenticity Identification of Beer Based on Near-Infrared Spectroscopy Combined with Multi-Task Feature Extraction.Molecules (Basel, Switzerland) · 2026Article
- Machine learning unveils three layers of food complexity.NPJ science of food · 2026Review
- Predicting food taste with bound-driven optimization.Current research in food science · 2026Article
- Advances in predicting human olfactory perception: from data acquisition to computational models.RSC advances · 2025Review
- Unveiling Disparities in Beer Consumer Behavior and Key Drivers Across Regions in China.Foods (Basel, Switzerland) · 2025Article
- Machine Learning in Sensory Analysis of Mead-A Case Study: Ensembles of Classifiers.Molecules (Basel, Switzerland) · 2025Article
- A Review of Processing, Marketing, and Demographic Influence on Consumer Perception of Pulse Milk- Insights From Traditional and AI-Facilitated Research.Journal of food science · 2025Review
- Machine learning combined with GC-FID for discrimination of different categories of maotai-flavor baijiu.Food chemistry: X · 2025Article
- Comprehensive characterization of Chinese beers based on chemical composition, antioxidant activity and volatile metabolomics.Scientific reports · 2025Article
- ATR-FTIR spectroscopy combined with metabolomics to analyze the taste components ofFood chemistry: X · 2025Article
Corrections and comments
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Authors and funding
13 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.