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Computational intelligence applied to discriminate bee pollen quality and botanical origin

2017Journal article Restricted access
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dc.contributor.authorGonçalves, Paulo
dc.contributor.authorEstevinho, Letícia M.
dc.contributor.authorPereira, Ana Paula
dc.contributor.authorSousa, João M.C.
dc.contributor.authorAnjos, O.
dc.date.accessioned2017-09-28T10:05:54Z
dc.date.available2018-12-31T01:30:19Z
dc.date.issued2017
dc.description.abstractThe aim of this work was to develop computational intelligence models based on neural networks (NN), fuzzy models (FM), and support vector machines (SVM) to predict physicochemical composition of bee pollen mixture given their botanical origin. To obtain the predominant plant genus of pollen (was the output variable), based on physicochemical composition (were the input variables of the predictive model), prediction models were learned from data. For the inverse case study, input/output variables were swapped. The probabilistic NN prediction model obtained 98.4% of correct classification of the predominant plant genus of pollen. To obtain the secondary and tertiary plant genus of pollen, the results present a lower accuracy. To predict the physicochemical characteristic of a mixture of bee pollen, given their botanical origin, fuzzy models proven the best results with small prediction errors, and variability lower than 10%.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationGONÇALVES, P. J. S. [et al.] (2017) - Computational intelligence applied to discriminate bee pollen quality and botanical origin. Food Chemistry. http://dx.doi.org/10.1016/j.foodchem.2017.06.014pt_PT
dc.identifier.doihttp://dx.doi.org/10.1016/j.foodchem.2017.06.014pt_PT
dc.identifier.issn0308-8146
dc.identifier.urihttp://hdl.handle.net/10400.11/5715
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationby FCT trough IDMEC, under LAETA, project UID/EMS/50022/2013pt_PT
dc.relationby FCT, project UID/BIA/04050/2013 (POCI-01-0145- 398 FEDER-007569)pt_PT
dc.relationCentro de Estudos Florestais, a research unit funded by FCT (UID/AGR/UI0239/2013); strategic programme UID/BIA/04050/2013 397 (POCI-01-0145-FEDER-007569)pt_PT
dc.relationthe ERDF through the COMPETE2020 - 399 Programa Operacional Competitividade e Internacionalização (POCI).pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectBee pollenpt_PT
dc.subjectPhysical-chemical parameterspt_PT
dc.subjectBotanical originpt_PT
dc.subjectNeural networkspt_PT
dc.subjectFuzzy modellingpt_PT
dc.subjectSupport vector machinespt_PT
dc.titleComputational intelligence applied to discriminate bee pollen quality and botanical originpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.titleFood Chemistrypt_PT
person.familyNameGonçalves
person.familyNameMiranda Fernandes Estevinho
person.familyNameAnjos
person.givenNamePaulo
person.givenNameMaria Leticia
person.givenNameOfélia
person.identifier.ciencia-id2816-A2FA-C5A3
person.identifier.ciencia-idBA14-09D6-A406
person.identifier.ciencia-idC21D-D8C7-3037
person.identifier.orcid0000-0002-8692-7338
person.identifier.orcid0000-0002-9249-1948
person.identifier.orcid0000-0003-0267-3252
person.identifier.ridE-5640-2012
person.identifier.ridG-2808-2012
person.identifier.scopus-author-id35853838000
person.identifier.scopus-author-id6506577664
person.identifier.scopus-author-id23395659700
rcaap.rightsembargoedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication86a6a234-d690-4c2b-8bee-d58005eebba2
relation.isAuthorOfPublication833b19a5-a9f2-485d-a48d-4ead7bdd3f0d
relation.isAuthorOfPublicationdf9191ae-0bbb-4bb8-bbdc-0f79c7365876
relation.isAuthorOfPublication.latestForDiscovery86a6a234-d690-4c2b-8bee-d58005eebba2

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