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Open AccessArticle

A Digital Approach to Model Quality and Sensory Traits of Beers Fermented under Sonication Based on Chemical Fingerprinting

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(3), 73; https://doi.org/10.3390/fermentation6030073
Received: 19 June 2020 / Revised: 6 July 2020 / Accepted: 20 July 2020 / Published: 21 July 2020
The development of digital tools based on artificial intelligence can produce affordable and accurate methodologies to assess quality traits and sensory analysis of beers. These new and emerging technologies can also assess new products in a near real-time fashion through virtual simulations before the brewing process. This research was based on the development of specific digital tools (four models) to assess quality traits and sensory profiles of beers produced using sonication and traditional brewing techniques. Results showed that models developed using supervised machine learning (ML) regression algorithms based on near-infrared spectroscopy (NIR) were highly accurate in the estimation of physicochemical parameters (Model 1; R = 0.94; b = 0.91). Outputs from Model 1 were then used as inputs to obtain estimations of the intensity of sensory descriptors (Model 2; R = 0.99; b = 0.98), liking of sensory attributes (Model 3; R = 0.97; b = 0.99), and the classification of fermentation treatments using supervised classification ML algorithms (Model 4; 96% accuracy). These new digital tools can aid craft brewing companies for product development at lower costs and maintain specific quality traits and sensory profiles, creating original styles of beers to get positioned in the market. View Full-Text
Keywords: artificial intelligence; beer quality; machine learning; robotics; sonication artificial intelligence; beer quality; machine learning; robotics; sonication
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MDPI and ACS Style

Gonzalez Viejo, C.; Fuentes, S. A Digital Approach to Model Quality and Sensory Traits of Beers Fermented under Sonication Based on Chemical Fingerprinting. Fermentation 2020, 6, 73. https://doi.org/10.3390/fermentation6030073

AMA Style

Gonzalez Viejo C, Fuentes S. A Digital Approach to Model Quality and Sensory Traits of Beers Fermented under Sonication Based on Chemical Fingerprinting. Fermentation. 2020; 6(3):73. https://doi.org/10.3390/fermentation6030073

Chicago/Turabian Style

Gonzalez Viejo, Claudia; Fuentes, Sigfredo. 2020. "A Digital Approach to Model Quality and Sensory Traits of Beers Fermented under Sonication Based on Chemical Fingerprinting" Fermentation 6, no. 3: 73. https://doi.org/10.3390/fermentation6030073

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