Publication
Computational intelligence applied to discriminate bee pollen quality and botanical origin
| dc.contributor.author | Gonçalves, Paulo J.S. | |
| dc.contributor.author | Estevinho, Leticia M. | |
| dc.contributor.author | Pereira, Ana Paula | |
| dc.contributor.author | Sousa, João M.C. | |
| dc.contributor.author | Anjos, Ofélia | |
| dc.date.accessioned | 2018-02-19T10:00:00Z | |
| dc.date.accessioned | 2018-07-31T15:22:34Z | |
| dc.date.available | 2018-01-19T10:00:00Z | |
| dc.date.available | 2018-07-31T15:22:34Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | 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%. | |
| dc.description.sponsorship | 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). | |
| dc.description.version | info:eu-repo/semantics/publishedVersion | en_EN |
| dc.identifier.citation | 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 | en_EN |
| dc.identifier.doi | 10.1016/j.foodchem.2017.06.014 | en_EN |
| dc.identifier.issn | 0308-8146 | |
| dc.identifier.uri | http://hdl.handle.net/10198/11989 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | en_EN |
| dc.subject | Bee pollen | en_EN |
| dc.subject | Botanical origin | en_EN |
| dc.subject | Fuzzy modelling | en_EN |
| dc.subject | Neural networks | en_EN |
| dc.subject | Physical–chemical parameters | en_EN |
| dc.subject | Support vector machines | en_EN |
| dc.title | Computational intelligence applied to discriminate bee pollen quality and botanical origin | en_EN |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/3599-PPCDT/Incentivo%2FAGR%2FUI0239%2F2013/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/5876/UID%2FEMS%2F50022%2F2013/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/5876/UID%2FBIA%2F04050%2F2013/PT | |
| oaire.fundingStream | 3599-PPCDT | |
| oaire.fundingStream | 5876 | |
| oaire.fundingStream | 5876 | |
| person.familyName | Estevinho | |
| person.familyName | Pereira | |
| person.givenName | Letícia M. | |
| person.givenName | Ana Paula | |
| person.identifier.ciencia-id | BA14-09D6-A406 | |
| person.identifier.ciencia-id | 1413-70AA-CDF6 | |
| person.identifier.orcid | 0000-0002-9249-1948 | |
| person.identifier.orcid | 0000-0003-1489-5207 | |
| person.identifier.scopus-author-id | 6506577664 | |
| person.identifier.scopus-author-id | 54401473200 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | openAccess | en_EN |
| rcaap.type | article | en_EN |
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