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Open AccessArticle

Beer Aroma and Quality Traits Assessment Using Artificial Intelligence

Digital Agriculture, Food and Wine Sciences Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia
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Fermentation 2020, 6(2), 56; https://doi.org/10.3390/fermentation6020056
Received: 30 April 2020 / Revised: 23 May 2020 / Accepted: 27 May 2020 / Published: 28 May 2020
(This article belongs to the Special Issue Industrial Fermentation)
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. This research shows the construction of artificial intelligence (AI) models based on aroma profiles, chemometrics, and chemical fingerprinting using near-infrared spectroscopy (NIR) obtained from 20 commercial beers used as targets. Results showed that machine learning models obtained using NIR from beers as inputs were accurate and robust in the prediction of six important aromas for beer (Model 1; R = 0.91; b = 0.87) and chemometrics (Model 2; R = 0.93; b = 0.90). Additionally, two more accurate models were obtained from robotics (RoboBEER) to obtain the same aroma profiles (Model 3; R = 0.99; b = 1.00) and chemometrics (Model 4; R = 0.98; b = 1.00). 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. View Full-Text
Keywords: computer vision; machine learning; robotics; near-infrared spectroscopy; chemometrics computer vision; machine learning; robotics; near-infrared spectroscopy; chemometrics
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MDPI and ACS Style

Gonzalez Viejo, C.; Fuentes, S. Beer Aroma and Quality Traits Assessment Using Artificial Intelligence. Fermentation 2020, 6, 56. https://doi.org/10.3390/fermentation6020056

AMA Style

Gonzalez Viejo C, Fuentes S. Beer Aroma and Quality Traits Assessment Using Artificial Intelligence. Fermentation. 2020; 6(2):56. https://doi.org/10.3390/fermentation6020056

Chicago/Turabian Style

Gonzalez Viejo, Claudia; Fuentes, Sigfredo. 2020. "Beer Aroma and Quality Traits Assessment Using Artificial Intelligence" Fermentation 6, no. 2: 56. https://doi.org/10.3390/fermentation6020056

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