Orientador(es)
Resumo(s)
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%.
Descrição
Palavras-chave
Bee pollen Botanical origin Fuzzy modelling Neural networks Physical–chemical parameters Support vector machines
Contexto Educativo
Citação
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