Computational intelligence applied to discriminate bee pollen quality and botanical origin
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.11/6458 |
Resumo: | The 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%. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Computational intelligence applied to discriminate bee pollen quality and botanical originPollenSupport Vector MachineNeural Networks (Computer)The 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%.Repositório Científico do Instituto Politécnico de Castelo BrancoGonçalves, Paulo J.S.Estevinho, Letícia M.Pereira, Ana PaulaSousa, João M.C.Anjos, O.2019-04-12T23:28:49Z20182018-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.11/6458eng10.1016/j.foodchem.2017.06.014info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-01-16T11:46:24ZPortal AgregadorONG |
dc.title.none.fl_str_mv |
Computational intelligence applied to discriminate bee pollen quality and botanical origin |
title |
Computational intelligence applied to discriminate bee pollen quality and botanical origin |
spellingShingle |
Computational intelligence applied to discriminate bee pollen quality and botanical origin Gonçalves, Paulo J.S. Pollen Support Vector Machine Neural Networks (Computer) |
title_short |
Computational intelligence applied to discriminate bee pollen quality and botanical origin |
title_full |
Computational intelligence applied to discriminate bee pollen quality and botanical origin |
title_fullStr |
Computational intelligence applied to discriminate bee pollen quality and botanical origin |
title_full_unstemmed |
Computational intelligence applied to discriminate bee pollen quality and botanical origin |
title_sort |
Computational intelligence applied to discriminate bee pollen quality and botanical origin |
author |
Gonçalves, Paulo J.S. |
author_facet |
Gonçalves, Paulo J.S. Estevinho, Letícia M. Pereira, Ana Paula Sousa, João M.C. Anjos, O. |
author_role |
author |
author2 |
Estevinho, Letícia M. Pereira, Ana Paula Sousa, João M.C. Anjos, O. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico de Castelo Branco |
dc.contributor.author.fl_str_mv |
Gonçalves, Paulo J.S. Estevinho, Letícia M. Pereira, Ana Paula Sousa, João M.C. Anjos, O. |
dc.subject.por.fl_str_mv |
Pollen Support Vector Machine Neural Networks (Computer) |
topic |
Pollen Support Vector Machine Neural Networks (Computer) |
description |
The 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%. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018 2018-01-01T00:00:00Z 2019-04-12T23:28:49Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.11/6458 |
url |
http://hdl.handle.net/10400.11/6458 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1016/j.foodchem.2017.06.014 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
|
repository.mail.fl_str_mv |
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_version_ |
1777301849541443584 |