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dc.contributor.authorGaldo, Brais
dc.contributor.authorRivero, Daniel
dc.contributor.authorFernández-Blanco, Enrique
dc.date.accessioned2019-09-19T14:20:34Z
dc.date.available2019-09-19T14:20:34Z
dc.date.issued2019-08-13
dc.identifier.citationGaldo, B.; Rivero, D.; Fernandez-Blanco, E. Estimation of the Alcoholic Degree in Beers through Near Infrared Spectrometry Using Machine Learning. Proceedings 2019, 21, 48.es_ES
dc.identifier.issn2504-3900
dc.identifier.urihttp://hdl.handle.net/2183/23954
dc.description.abstract[Abstract] It is a fact that, non-destructive measurement technologies have gain a lot of attention over the years. Among those technologies, NIR technology is the one which allows the analysis of electromagnetic spectrum looking for carbon-link interactions. This technology analyzes the electromagnetic spectrum in the band between 700 nm and 2500 nm, a band very close to the visible spectrum. Traditionally, the devices used to measure are utterly expensive and enormously bulky. That is why this project was focused on a portable spectrophotometer to make measures. This device is smaller and cheaper than the common spectrophotometer, although at the cost of a lower resolution. In this work, that device in combination with the use of machine learning was used to detect if a beer contains alcohol or it can be labeled as non-alcoholic drink.es_ES
dc.description.sponsorshipXunta de Galicia; ED431G/01es_ES
dc.language.isoenges_ES
dc.publisherM D P I AGes_ES
dc.relation.urihttps://doi.org/10.3390/proceedings2019021048es_ES
dc.rightsAtribución 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectNIRes_ES
dc.subjectElectromagnetic spectrumes_ES
dc.subjectNeural networkses_ES
dc.titleEstimation of the Alcoholic Degree in Beers through Near Infrared Spectrometry Using Machine Learninges_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleProceedingses_ES
UDC.volume21es_ES
UDC.issue1es_ES
UDC.startPage48es_ES
dc.identifier.doi10.3390/proceedings2019021048
UDC.conferenceTitle2nd XoveTIC Conference, A Coruña, Spain, 5–6 September 2019.es_ES


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