2019
DOI: 10.3390/s19112646
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Abstract: Due to the emergence of new microbreweries in the Brazilian market, there is a need to construct equipment to quickly and accurately identify the alcohol content in beverages, together with a reduced marketing cost. Towards this purpose, the electronic noses prove to be the most suitable equipment for this situation. In this work, a prototype was developed to detect the concentration of ethanol in a high spectrum of beers presents in the market. It was used cheap and easy-to-acquire 13 gas sensors made with a … Show more

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Cited by 36 publications
(28 citation statements)
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References 40 publications
(43 reference statements)
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“…In recent years, non-destructive testing technology has been receiving more attention as an emerging technology. The E-nose technology is designed to simulate the olfactory system of mammals by detecting the odor status of a specific location in real time through gas sensing array and response pattern [ 21 ]. It has attracted attention due to its advantages of simple sample processing, short response time, good recognition effect and inherent non-damage analysis [ 22 ].…”
Section: Introductionmentioning
confidence: 99%
“…In recent years, non-destructive testing technology has been receiving more attention as an emerging technology. The E-nose technology is designed to simulate the olfactory system of mammals by detecting the odor status of a specific location in real time through gas sensing array and response pattern [ 21 ]. It has attracted attention due to its advantages of simple sample processing, short response time, good recognition effect and inherent non-damage analysis [ 22 ].…”
Section: Introductionmentioning
confidence: 99%
“…Santos and Lozano [113] used an electronic nose (e-nose), which consists of a device with an array of gas sensors that may be a metal oxide or polymer semiconductors capable of mimicking the olfactory system [114] to analyze two main beer off-odors, acetaldehyde and ethyl acetate; the authors used the output values from the e-nose as inputs to develop a probabilistic neural network model with 94% accuracy in the validation stage to predict whether those compounds fell above the threshold, which are considered as defects in beer. Voss et al [115] developed an e-nose with 13 different gas sensors to analyze beers, and used these responses as inputs in an extreme learning machine model to predict alcohol content with RMSE = 0.63 in validation and RMSE = 0.33 in the testing stage; however, the authors did not mention the correlation or determination coefficient values for this method. Zhang et al [116] used SVM to construct a model using the fermentation parameters to predict the acetic acid (vinegar) content in the beer; however, the reported validation accuracy was low (R < 0.60).…”
Section: Machine Learning In Alcoholic Beveragesmentioning
confidence: 99%
“…An e-nose system usually consists of: a sensor array including multiple sensors that react in some repeatable way when exposed to volatile substances released by analytes, a data acquisition (DAQ) system for measuring and collecting the responses of sensor array with a computer program which analysis results. This powerful tool has assisted many fields in food analysis [ 7 , 8 ], such as beer quality inspection [ 9 , 10 ], quality level identification of tea [ 11 ], characterization of juices [ 12 ], and differentiation of aromatic flowers [ 13 ]. Additionally, e-nose can be applied to the monitoring of air quality [ 14 , 15 , 16 , 17 , 18 , 19 ] of gases emitted by the soil [ 20 , 21 ].…”
Section: Introductionmentioning
confidence: 99%