Development of a robotic pourer constructed with ubiquitous materials, open hardware and sensors to assess beer foam quality using computer vision and pattern recognition algorithms: RoboBEER

There are currently no standardized objective measures to assess beer quality based on the most significant parameters related to the first impression from consumers, which are visual characteristics of foamability, beer color and bubble size. This study describes the development of an affordable an...

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Vydané v:Food research international Ročník 89; číslo Pt 1; s. 504 - 513
Hlavní autori: Gonzalez Viejo, Claudia, Fuentes, Sigfredo, Li, GuangJun, Collmann, Richard, Condé, Bruna, Torrico, Damir
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Canada Elsevier Ltd 01.11.2016
Predmet:
alcohols
algorithms
Artificial neural networks
Beer foam
beers
Bubble size
carbon dioxide
color
Computer vision
fermentation
foams
neural networks
principal component analysis
retail marketing
robots
sensory evaluation
Sensory panels
temperature
Computer vision
Beer foam
Bubble size
Sensory panels
Artificial neural networks
ISSN:0963-9969, 1873-7145, 1873-7145
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Shrnutí:There are currently no standardized objective measures to assess beer quality based on the most significant parameters related to the first impression from consumers, which are visual characteristics of foamability, beer color and bubble size. This study describes the development of an affordable and robust robotic beer pourer using low-cost sensors, Arduino® boards, Lego® building blocks and servo motors for prototyping. The RoboBEER is also coupled with video capture capabilities (iPhone 5S) and automated post hoc computer vision analysis algorithms to assess different parameters based on foamability, bubble size, alcohol content, temperature, carbon dioxide release and beer color. Results have shown that parameters obtained from different beers by only using the RoboBEER can be used for their classification according to quality and fermentation type. Results were compared to sensory analysis techniques using principal component analysis (PCA) and artificial neural networks (ANN) techniques. The PCA from RoboBEER data explained 73% of variability within the data. From sensory analysis, the PCA explained 67% of the variability and combining RoboBEER and Sensory data, the PCA explained only 59% of data variability. The ANN technique for pattern recognition allowed creating a classification model from the parameters obtained with RoboBEER, achieving 92.4% accuracy in the classification according to quality and fermentation type, which is consistent with the PCA results using data only from RoboBEER. The repeatability and objectivity of beer assessment offered by the RoboBEER could translate into the development of an important practical tool for food scientists, consumers and retail companies to determine differences within beers based on the specific parameters studied. [Display omitted] •A new automatic method using a robotic pourer for measuring foamability in beer is presented.•The automatic method measures foamability and color using computer vision algorithms.•The method includes sensors to measure alcohol and CO2, and to monitor temperature during pouring.•An artificial neural network model using the RoboBEER parameters was obtained to classify beers.•RoboBEER foam analysis showed to be more accurate and affordable than traditional sensory analysis.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0963-9969
1873-7145
1873-7145
DOI:10.1016/j.foodres.2016.08.045