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/10198/11989 |
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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Computational intelligence applied to discriminate bee pollen quality and botanical originBee pollenBotanical originFuzzy modellingNeural networksPhysical–chemical parametersSupport vector machinesThe 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%.This work was partly supported by FCT, through IDMEC, under LAETA, project UID/EMS/50022/2013, and partly funded by FCT I.P.: Centro de Estudos Florestais, a research unit funded by FCT (UID/AGR/UI0239/2013); strategic programme UID/ BIA/04050/2013 (POCI-01-0145-FEDER- 007569) and strategic programme UID/BIA/04050/2013 (POCI-01-0145-FEDER-007569). In addition, it was also funded by the ERDF through the COMPETE2020 – Programa Operacional Competitividade e Internacionalização (POCI).Biblioteca Digital do IPBGonçalves, Paulo J.S.Estevinho, Leticia M.Pereira, Ana PaulaSousa, João M.C.Anjos, Ofélia2018-01-19T10:00:00Z20182018-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10198/11989engGonçalves, Paulo J.S.; Estevinho, Letícia M.; Pereira, Ana Paula; Sousa, João M.C.; Anjos, Ofélia (2018). Computational intelligence applied to discriminate bee pollen quality and botanical origin. Food Chemistry. ISSN 0308-8146. 267, p. 36-420308-814610.1016/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:RCAAP2023-01-16T12:19:20ZPortal 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. Bee pollen Botanical origin Fuzzy modelling Neural networks Physical–chemical parameters Support vector machines |
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, Leticia M. Pereira, Ana Paula Sousa, João M.C. Anjos, Ofélia |
author_role |
author |
author2 |
Estevinho, Leticia M. Pereira, Ana Paula Sousa, João M.C. Anjos, Ofélia |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Biblioteca Digital do IPB |
dc.contributor.author.fl_str_mv |
Gonçalves, Paulo J.S. Estevinho, Leticia M. Pereira, Ana Paula Sousa, João M.C. Anjos, Ofélia |
dc.subject.por.fl_str_mv |
Bee pollen Botanical origin Fuzzy modelling Neural networks Physical–chemical parameters Support vector machines |
topic |
Bee pollen Botanical origin Fuzzy modelling Neural networks Physical–chemical parameters Support vector machines |
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 |
2018-01-19T10:00:00Z 2018 2018-01-01T00:00:00Z |
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/10198/11989 |
url |
http://hdl.handle.net/10198/11989 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Gonçalves, Paulo J.S.; Estevinho, Letícia M.; Pereira, Ana Paula; Sousa, João M.C.; Anjos, Ofélia (2018). Computational intelligence applied to discriminate bee pollen quality and botanical origin. Food Chemistry. ISSN 0308-8146. 267, p. 36-42 0308-8146 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 |
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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) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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1777301894316687360 |