Beer Aroma and Quality Traits Assessment Using Artificial Intelligence

Increasing beer quality demands from consumers have put pressure on brewers to target specific steps within the beer-making process to modify beer styles and quality traits. However, this demands more robust methodologies to assess the final aroma profiles and physicochemical characteristics of beers.

Research from the Food and Wine Sciences Group at the University of Melbourne, Australia shows that low-cost robotics and sensors coupled with computer vision and machine learning modeling could help brewers in the decision-making process to target specific consumer preferences and to secure higher consumer demands.

Download the full research paper here.

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