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dc.contributor.authorGaldo, Brais
dc.contributor.authorFernández-Blanco, Enrique
dc.contributor.authorRivero, Daniel
dc.date.accessioned2022-01-12T18:40:31Z
dc.date.available2022-01-12T18:40:31Z
dc.date.issued2021
dc.identifier.citationGaldo, B.; Fernandez-Blanco, E.; Rivero, D. Detection of Chocolate Properties Using Near-Infrared Spectrophotometry. Eng. Proc. 2021, 7, 37. https://doi.org/10.3390/engproc2021007037es_ES
dc.identifier.urihttp://hdl.handle.net/2183/29369
dc.descriptionPresented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021.es_ES
dc.description.abstract[Abstract] Knowing the chemical composition of a substance provides valuable information about it. That is why numerous techniques have been developed to try to obtain it. One of them is the Near Infrared Spectrometry technique, a non-destructive technique that analyzes the electromagnetic spectrum in search of waves of a certain length. The aim of this project is to combine this technology with machine learning techniques to try to detect the presence of milk, as well as the level of cocoa present in an ounce of chocolate. This has given satisfactory results in both cases, so it is considered that the combination of these techniques offers great possibilities.es_ES
dc.description.sponsorshipThe authors would like to thank the support from RNASA-IMEDIR group.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relation.urihttps://doi.org/10.3390/engproc2021007037es_ES
dc.rightsAtribución 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectNear infrared spectroscopyes_ES
dc.subjectMachine learninges_ES
dc.subjectArtificial neural networkses_ES
dc.subjectIntensityes_ES
dc.subjectAbsorbancees_ES
dc.subjectReflectancees_ES
dc.subjectChocolatees_ES
dc.subjectMilkes_ES
dc.subjectCocoaes_ES
dc.titleDetection of Chocolate Properties Using Near-Infrared Spectrophotometry †es_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleEngineering Proceedingses_ES
UDC.volume7es_ES
UDC.issue1es_ES
UDC.startPage37es_ES
dc.identifier.doi10.3390/engproc2021007037


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