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Open AccessArticle

Low-Cost Methods to Assess Beer Quality Using Artificial Intelligence Involving Robotics, an Electronic Nose, and Machine Learning

Digital Agriculture, Food and Wine Sciences Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne VIC 3010, Australia
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Fermentation 2020, 6(4), 104; https://doi.org/10.3390/fermentation6040104
Received: 8 October 2020 / Revised: 26 October 2020 / Accepted: 30 October 2020 / Published: 31 October 2020
(This article belongs to the Special Issue Implementation of Digital Technologies on Beverage Fermentation)
Beer quality is a difficult concept to describe and assess by physicochemical and sensory analysis due to the complexity of beer appreciation and acceptability by consumers, which can be dynamic and related to changes in climate affecting raw materials, consumer preference, and rising quality requirements. Artificial intelligence (AI) may offer unique capabilities based on the integration of sensor technology, robotics, and data analysis using machine learning (ML) to identify specific quality traits and process modifications to produce quality beers. This research presented the integration and implementation of AI technology based on low-cost sensor networks in the form of an electronic nose (e-nose), robotics, and ML. Results of ML showed high accuracy (97%) in the identification of fermentation type (Model 1) based on e-nose data; prediction of consumer acceptability from near-infrared (Model 2; R = 0.90) and e-nose data (Model 3; R = 0.95), and physicochemical and colorimetry of beers from e-nose data. The use of the RoboBEER coupled with the e-nose and AI could be used by brewers to assess the fermentation process, quality of beers, detection of faults, traceability, and authentication purposes in an affordable, user-friendly, and accurate manner. View Full-Text
Keywords: sensor networks; automation; beer acceptability; beer fermentation; RoboBEER sensor networks; automation; beer acceptability; beer fermentation; RoboBEER
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MDPI and ACS Style

Gonzalez Viejo, C.; Fuentes, S. Low-Cost Methods to Assess Beer Quality Using Artificial Intelligence Involving Robotics, an Electronic Nose, and Machine Learning. Fermentation 2020, 6, 104. https://doi.org/10.3390/fermentation6040104

AMA Style

Gonzalez Viejo C, Fuentes S. Low-Cost Methods to Assess Beer Quality Using Artificial Intelligence Involving Robotics, an Electronic Nose, and Machine Learning. Fermentation. 2020; 6(4):104. https://doi.org/10.3390/fermentation6040104

Chicago/Turabian Style

Gonzalez Viejo, Claudia; Fuentes, Sigfredo. 2020. "Low-Cost Methods to Assess Beer Quality Using Artificial Intelligence Involving Robotics, an Electronic Nose, and Machine Learning" Fermentation 6, no. 4: 104. https://doi.org/10.3390/fermentation6040104

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