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Findings from University of Melbourne Provides New Data about Robotics (Assessment of Beer Quality Based on a Robotic Pourer, Computer Vision, and...

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05/31/2018 | 04:15pm EDT

Findings from University of Melbourne Provides New Data about Robotics (Assessment of Beer Quality Based on a Robotic Pourer, Computer Vision, and Machine Learning Algorithms Using Commercial Beers)

By a News Reporter-Staff News Editor at Journal of Mathematics -- Investigators publish new report on Robotics. According to news reporting out of Melbourne, Australia, by VerticalNews editors, research stated, "Sensory attributes of beer are directly linked to perceived foam-related parameters and beer color. The aim of this study was to develop an objective predictive model using machine learning modeling to assess the intensity levels of sensory descriptors in beer using the physical measurements of color and foam-related parameters."

Our news journalists obtained a quote from the research from the University of Melbourne, "A robotic pourer (RoboBEER), was used to obtain 15 color and foam-related parameters from 22 different commercial beer samples. A sensory session using quantitative descriptive analysis (QDA ®) with trained panelists was conducted to assess the intensity of 10 beer descriptors. Results showed that the principal component analysis explained 64% of data variability with correlations found between foam-related descriptors from sensory and RoboBEER such as the positive and significant correlation between carbon dioxide and carbonation mouthfeel (R = 0.62), correlation of viscosity to sensory, and maximum volume of foam and total lifetime of foam (R = 0.75, R = 0.77, respectively)."

According to the news editors, the research concluded: "Using the RoboBEER parameters as inputs, an artificial neural network (ANN) regression model showed high correlation (R = 0.91) to predict the intensity levels of 10 related sensory descriptors such as yeast, grains and hops aromas, hops flavor, bitter, sour and sweet tastes, viscosity, carbonation, and astringency."

For more information on this research see: Assessment of Beer Quality Based on a Robotic Pourer, Computer Vision, and Machine Learning Algorithms Using Commercial Beers. Journal of Food Science, 2018;83(5):1381-1388. Journal of Food Science can be contacted at: Wiley, 111 River St, Hoboken 07030-5774, NJ, USA. (Wiley-Blackwell - www.wiley.com/; Journal of Food Science - onlinelibrary.wiley.com/journal/10.1111/(ISSN)1750-3841)

Our news journalists report that additional information may be obtained by contacting S. Fuentes, University of Melbourne, Sch Agr & Food, Fac Vet & Agr Sci, Melbourne, Vic 3010, Australia. Additional authors for this research include C.G. Viejo, D.D. Torrico, K. Howell and F.R. Dunshea.

Keywords for this news article include: Melbourne, Australia, Australia and New Zealand, Emerging Technologies, Machine Learning, Algorithms, Robotics, Cyborgs, Robots, University of Melbourne.

Our reports deliver fact-based news of research and discoveries from around the world. Copyright 2018, NewsRx LLC

(c) 2018 NewsRx LLC, source Science Newsletters

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