A influência do envelhecimento ativo na qualidade de vida da pessoa idosa - revisão integrativa da literatura
Azevedo, Luís
2022
Computational intelligence applied to discriminate bee pollen quality and botanical origin
Type
article
Publisher
Identifier
GONÇALVES, 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.2017.06.014
0308-8146
Title
Computational intelligence applied to discriminate bee pollen quality and botanical origin
Subject
Bee pollen
Physical-chemical parameters
Botanical origin
Neural networks
Fuzzy modelling
Support vector machines
Physical-chemical parameters
Botanical origin
Neural networks
Fuzzy modelling
Support vector machines
Relation
by FCT trough IDMEC, under LAETA, project UID/EMS/50022/2013
by FCT, project UID/BIA/04050/2013 (POCI-01-0145- 398 FEDER-007569)
Centro 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)
the ERDF through the COMPETE2020 - 399 Programa Operacional Competitividade e Internacionalização (POCI).
by FCT, project UID/BIA/04050/2013 (POCI-01-0145- 398 FEDER-007569)
Centro 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)
the ERDF through the COMPETE2020 - 399 Programa Operacional Competitividade e Internacionalização (POCI).
Date
2017-09-28T10:05:54Z
2018-12-31T01:30:19Z
2017
2018-12-31T01:30:19Z
2017
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%.
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/publishedVersion
Access restrictions
embargoedAccess
http://creativecommons.org/licenses/by/4.0/
http://creativecommons.org/licenses/by/4.0/
Language
eng
Comments
