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Estimation of the Alcoholic Degree in Beers through Near Infrared Spectrometry Using Machine Learning
dc.contributor.author | Galdo, Brais | |
dc.contributor.author | Rivero, Daniel | |
dc.contributor.author | Fernández-Blanco, Enrique | |
dc.date.accessioned | 2019-09-19T14:20:34Z | |
dc.date.available | 2019-09-19T14:20:34Z | |
dc.date.issued | 2019-08-13 | |
dc.identifier.citation | Galdo, 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.issn | 2504-3900 | |
dc.identifier.uri | http://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.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | M D P I AG | es_ES |
dc.relation.uri | https://doi.org/10.3390/proceedings2019021048 | es_ES |
dc.rights | Atribución 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | NIR | es_ES |
dc.subject | Electromagnetic spectrum | es_ES |
dc.subject | Neural networks | es_ES |
dc.title | Estimation of the Alcoholic Degree in Beers through Near Infrared Spectrometry Using Machine Learning | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Proceedings | es_ES |
UDC.volume | 21 | es_ES |
UDC.issue | 1 | es_ES |
UDC.startPage | 48 | es_ES |
dc.identifier.doi | 10.3390/proceedings2019021048 | |
UDC.conferenceTitle | 2nd XoveTIC Conference, A Coruña, Spain, 5–6 September 2019. | es_ES |