Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/241830
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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampMesquita, Marcos Eduardo Ribeiro do Vallept_BR
dc.typeArtigopt_BR
dc.titleApplication of self-organising maps towards segmentation of soybean samples by determination of inorganic compounds contentpt_BR
dc.contributor.authorCremasco, Hágatapt_BR
dc.contributor.authorBorsato, Dionísiopt_BR
dc.contributor.authorAngilelli, Karina Gomespt_BR
dc.contributor.authorGalão, Olívio Fernandespt_BR
dc.contributor.authorBona, Evandropt_BR
dc.contributor.authorValle, Marcos Eduardopt_BR
dc.subjectRedes neurais (Computação)pt_BR
dc.subjectInteligência artificialpt_BR
dc.subjectMapas auto-organizáveispt_BR
dc.subjectSojapt_BR
dc.subjectCompostos inorgânicos - Análisept_BR
dc.subject.otherlanguageNeural networks (Computer science)pt_BR
dc.subject.otherlanguageArtificial intelligencept_BR
dc.subject.otherlanguageSoybeanpt_BR
dc.subject.otherlanguageInorganic compounds - Analysispt_BR
dc.description.abstractIn this study, 20 samples of soybean, both transgenic and conventional cultivars, which were planted in two different regions, Londrina and Ponta Grossa, both located at Parana, Brazil, were analysed. In order to verify whether the inorganic compound levels in soybeans varied with the region of planting, K, P, Ca, Mg, S, Zn, Mn, Fe, Cu and B contents were analysed by an artificial neural network self-organising map. It was observed that with a topology 10 x 10, 8000 epochs, initial learning rate of 0.1 and initial neighbourhood ratio of 4.5, the network was able to differentiate samples according to region of origin. Among all of the variables analysed by the artificial neural network, the elements Zn, Ca and Mn were those which most contributed to the classification of the samples. The results indicated that samples planted in these two regions differ in their mineral content; however, conventional and transgenic samples grown in the same region show no difference in mineral contents in the grain.pt_BR
dc.relation.ispartofJournal of the science of food and agriculturept_BR
dc.relation.ispartofabbreviationJ. sci. food agric.pt_BR
dc.publisher.cityOxfordpt_BR
dc.publisher.countryReino Unidopt_BR
dc.publisherJohn Wiley & Sonspt_BR
dc.date.issued2015pt_BR
dc.date.monthofcirculationJan.pt_BR
dc.identifier.citationApplication Of Self-organising Maps Towards Segmentation Of Soybean Samples By Determination Of Inorganic Compounds Content. Wiley-blackwell, v. 96, p. 306-310 Jan-2016.pt_BR
dc.language.isoengpt_BR
dc.description.volume96pt_BR
dc.description.issuenumber1pt_BR
dc.description.firstpage306pt_BR
dc.description.lastpage310pt_BR
dc.rightsfechadopt_BR
dc.sourceWOSpt_BR
dc.identifier.issn0022-5142pt_BR
dc.identifier.eissn1097-0010pt_BR
dc.identifier.doi10.1002/jsfa.7094pt_BR
dc.identifier.urlhttps://onlinelibrary.wiley.com/doi/full/10.1002/jsfa.7094pt_BR
dc.description.sponsorshipCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESpt_BR
dc.description.sponsorship1CAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORpt_BR
dc.description.sponsordocumentnumbersem informaçãopt_BR
dc.date.available2016-06-07T13:15:16Z-
dc.date.accessioned2016-06-07T13:15:16Z-
dc.description.provenanceMade available in DSpace on 2016-06-07T13:15:16Z (GMT). No. of bitstreams: 1 wos_000367222400035.pdf: 662841 bytes, checksum: 2656b849b18d772d00d084034dc5ff4c (MD5) Previous issue date: 2016 Bitstreams deleted on 2020-07-15T19:57:48Z: wos_000367222400035.pdf,. Added 1 bitstream(s) on 2020-07-15T20:19:07Z : No. of bitstreams: 1 000367222400035.pdf: 738866 bytes, checksum: dc43c9abec8ec2695f4ab60e285595e8 (MD5)en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/241830-
dc.contributor.departmentDepartamento de Matemática Aplicadapt_BR
dc.contributor.unidadeInstituto de Matemática, Estatística e Computação Científicapt_BR
dc.subject.keywordArtificial neural networkspt_BR
dc.subject.keywordUnsupervised learningpt_BR
dc.subject.keywordKohonen self-organising mappt_BR
dc.subject.keywordSynaptic adaptationpt_BR
dc.subject.keywordTopological neighbour-hoodpt_BR
dc.subject.keywordSoybean mineralspt_BR
dc.identifier.source000367222400035pt_BR
dc.creator.orcid0000-0003-4026-5110pt_BR
dc.type.formArtigopt_BR
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