Episode 309: Predicting and improving complex beer flavor through machine learning
Jun 3, 2024
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Experts Kevin Verstrepen and Michiel Schreurs discuss using chemical analytics and machine learning to predict and enhance complex beer flavors. They explore the challenges of sensory analysis, the impact of biases in beer reviews, and how machine learning models can connect chemical data to sensory data for accurate flavor predictions.
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Machine Learning in Beer Flavor Prediction
In a quest to predict sensory panel taste outcomes and enhance beer compositions, Kevin Verstrappen and Michiel Gers delve into using machine learning to link chemical profiles to flavor properties. Despite complexities in assessing thousands of aroma-driving compounds in beer, their study pioneers in mimicking sensory responses through advanced data analysis and correlation studies.
Data Analysis Challenges and Insights on Brew Flavor Characteristics
Analyzing over 250 beers unveils fruity yeast esters as predominant influencers in flavor profiles, followed by surprises like lactic acid positively impacting beer freshness. The interconnectedness of compounds poses challenges in accurately determining optimal levels, highlighting the need for nuanced interpretations beyond machine learning predictions.
Practical Applications and Spike Testing for Beer Enhancements
The spike testing of commercial beers guided by AI models offers intriguing insights into perceptible changes. While single compound additions exhibit a threshold effect on taste appreciation, combining various compounds, even sub-threshold, enhances beer favorability. These findings pave the way for potential beer refinement strategies based on chemical composition alterations.