Predicting and improving complex beer flavor through machine learning

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…

• AI
  Agentschap Innoveren en Ondernemen
• FW
  Fonds Wetenschappelijk Onderzoek

  Award: G.0A51.15
• KL
  KU Leuven

  Award: C16/17/006
• VI
  Vlaams Instituut voor Biotechnologie