title: Neural networks applied to discriminate botanical origin of honeys.
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.
authors.affiliation: Instituto Politécnico de Castelo Branco, Escola Superior Agrária, Apartado 119, 6001-909 Castelo Branco, Portugal; Centro de Estudos Florestais, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisboa, Portugal. Electronic address: ofelia@ipcb.pt.
authors.forename: Ofélia
authors.initials: O
authors.lastname: Anjos
authors.affiliation: Department of Natural Resources and Environmental Eng., University of Vigo, 36310 Vigo, Spain.
authors.forename: Carla
authors.initials: C
authors.lastname: Iglesias
authors.affiliation: Instituto Politécnico de Castelo Branco, Escola Superior Agrária, Apartado 119, 6001-909 Castelo Branco, Portugal.
authors.forename: Fátima
authors.initials: F
authors.lastname: Peres
authors.affiliation: Centro Universitario de la Defensa, Academia General Militar, 50090 Zaragoza, Spain.
authors.forename: Javier
authors.initials: J
authors.lastname: Martínez
authors.affiliation: Department of Natural Resources and Environmental Eng., University of Vigo, 36310 Vigo, Spain.
authors.forename: Ángela
authors.initials: Á
authors.lastname: García
authors.affiliation: Department of Natural Resources and Environmental Eng., University of Vigo, 36310 Vigo, Spain.
authors.forename: Javier
authors.initials: J
authors.lastname: Taboada
mesh_terms: D006722:Honey / Q000032:analysis*; D016571:Neural Networks, Computer; D010636:Phenols / Q000032:analysis
chemical_list: D010636:Phenols
keywords: Botanical origin; Classification problem; Honey; Neural networks; Overfitting; Physical–chemical parameters