Neural networks applied to discriminate botanical origin of honeys

Anjos, Ofélia; Iglesias, Carla; Peres, Maria de Fátima; Martínez, Javier; García, Ángela; Taboada, Javier

Publication

Neural networks applied to discriminate botanical origin of honeys

2015Journal article

dc.contributor.author	Anjos, Ofélia
dc.contributor.author	Iglesias, Carla
dc.contributor.author	Peres, Maria de Fátima
dc.contributor.author	Martínez, Javier
dc.contributor.author	García, Ángela
dc.contributor.author	Taboada, Javier
dc.date.accessioned	2017-07-22T16:14:31Z
dc.date.available	2017-07-22T16:14:31Z
dc.date.issued	2015
dc.description.abstract	The aim of this work is develop a tool based on neural networks to predict the botanical origin of honeys using physical and chemical parameters. The managed database consists of 49 honey samples of 2 different classes: monofloral (almond, holm oak, sweet chestnut, eucalyptus, orange, rosemary, lavender, strawberry trees, thyme, heather, sunflower) and multifloral. The moisture content, electrical conductivity, water activity, ashes content, pH, free acidity, colorimetric coordinates in CIELAB space (L(∗), a(∗), b(∗)) and total phenols content of the honey samples were evaluated. Those properties were considered as input variables of the predictive model. The neural network is optimised through several tests with different numbers of neurons in the hidden layer and also with different input variables. The reduced error rates (5%) allow us to conclude that the botanical origin of honey can be reliably and quickly known from the colorimetric information and the electrical conductivity of honey.	pt_PT
dc.description.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.citation	ANJOS, O. [et al.] (2015) - Neural networks applied to discriminate botanical origin of honeys. ISSN 0308-8146. 175. P. 128-136.	pt_PT
dc.identifier.doi	10.1016/j.foodchem.2014.11.121	pt_PT
dc.identifier.issn	0308-8146
dc.identifier.uri	http://hdl.handle.net/10400.11/5611
dc.language.iso	eng	pt_PT
dc.peerreviewed	yes	pt_PT
dc.publisher	Elsevier	pt_PT
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	pt_PT
dc.subject	Honey	pt_PT
dc.subject	Physical–chemical parameters	pt_PT
dc.subject	Botanical origin	pt_PT
dc.subject	Neural networks	pt_PT
dc.subject	Overfitting	pt_PT
dc.subject	Classification problem	pt_PT
dc.title	Neural networks applied to discriminate botanical origin of honeys	pt_PT
dc.type	journal article
dspace.entity.type	Publication
oaire.citation.endPage	136	pt_PT
oaire.citation.startPage	128	pt_PT
oaire.citation.title	Food Chemistry	pt_PT
oaire.citation.volume	175	pt_PT
person.familyName	Anjos
person.familyName	Pratas Peres
person.givenName	Ofélia
person.givenName	Maria de Fátima
person.identifier.ciencia-id	C21D-D8C7-3037
person.identifier.ciencia-id	B219-0A97-8517
person.identifier.orcid	0000-0003-0267-3252
person.identifier.orcid	0000-0002-0010-2811
person.identifier.rid	G-2808-2012
person.identifier.scopus-author-id	23395659700
rcaap.rights	restrictedAccess	pt_PT
rcaap.type	article	pt_PT
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