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Digital image processing combined with machine learning: A new strategy for brown sugar classification

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dc.contributor.authorAlves, Vandressa
dc.contributor.authorSantos, Jeferson M. dos
dc.contributor.authorPinto, Edgar
dc.contributor.authorFerreira, Isabel M.P.L.V.O.
dc.contributor.authorLima, Vanderlei Aparecido
dc.contributor.authorFelsner, Maria L.
dc.date.accessioned2024-04-09T15:55:21Z
dc.date.embargo2026-01-01
dc.date.issued2024
dc.description.abstractThe coloring of foods is one of the main attributes of importance for consumers and it can be decisive for a consumer to accept or reject the product. Models that explore brown sugar coloring are scarce in scientific research. So, a new strategy for brown sugar classification through the combination of digital image processing, machine learning and physicochemical composition data was proposed. RGB channel intensities and color histogram data, obtained from digital image processing, in combination with some physicochemical characteristics (sucrose, Ca, Fe, ICUMSA color and total phenolic compounds (TPC)) were used as training and external validation datasets in the creation of classification models by RF algorithm. Excellent performance of classification models was observed by high overall accuracy rates for ICUMSA color (92.6 %), Ca and sucrose (100 %), Fe (94.9 %), and TPC (97.6 %). Thus, classifying brown sugar based on its color can be a valuable strategy for the beverage and food industries, allowing for greater diversification and meeting consumer needs while enhancing the quality and consistency of products.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAlves, V., dos Santos, J. M., Pinto, E., Ferreira, I. M. P. L. V. O., Lima, V. A., & Felsner, M. L. (2024). Digital image processing combined with machine learning: A new strategy for brown sugar classification. Microchemical Journal, 196, 109604. https://doi.org/10.1016/j.microc.2023.109604pt_PT
dc.identifier.doi10.1016/j.microc.2023.109604pt_PT
dc.identifier.eissn1095-9149
dc.identifier.issn0026-265X
dc.identifier.urihttp://hdl.handle.net/10400.22/25323
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0026265X23012237?via%3Dihubpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectBrown sugarpt_PT
dc.subjectIdentity and quality standardspt_PT
dc.subjectSugar compositionpt_PT
dc.subjectClassificationpt_PT
dc.subjectDigital image processingpt_PT
dc.subjectColor analysispt_PT
dc.titleDigital image processing combined with machine learning: A new strategy for brown sugar classificationpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage8pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleMicrochemical Journalpt_PT
oaire.citation.volume196pt_PT
person.familyNamePinto
person.givenNameEdgar
person.identifier.ciencia-id271F-B7DF-8FAB
person.identifier.orcid0000-0002-8021-4783
rcaap.rightsembargoedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationeaf9fc86-1a1c-437f-adee-d28040aa7f2f
relation.isAuthorOfPublication.latestForDiscoveryeaf9fc86-1a1c-437f-adee-d28040aa7f2f

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