A new classification system of beer categories and styles based on large-scale data mining and self-organizing maps of beer recipes
This provides brewers, researchers, and educators with a reproducible and objective framework for recipe analysis and beer development, though it is incremental as it builds on existing classification methods.
The researchers tackled the problem of classifying beer categories and styles by analyzing 62,121 beer recipes using data mining and self-organizing maps, resulting in a new taxonomy with four major superclusters based on ingredient and fermentation patterns.
A data-driven quantitative approach was used to develop a novel classification system for beer categories and styles. Sixty-two thousand one hundred twenty-one beer recipes were mined and analyzed, considering ingredient profiles, fermentation parameters, and recipe vital statistics. Statistical analyses combined with self-organizing maps (SOMs) identified four major superclusters that showed distinctive malt and hop usage patterns, style characteristics, and historical brewing traditions. Cold fermented styles showed a conservative grain and hop composition, whereas hot fermented beers exhibited high heterogeneity, reflecting regional preferences and innovation. This new taxonomy offers a reproducible and objective framework beyond traditional sensory-based classifications, providing brewers, researchers, and educators with a scalable tool for recipe analysis and beer development. The findings in this work provide an understanding of beer diversity and open avenues for linking ingredient usage with fermentation profiles and flavor outcomes.