ANJOS, O. [et al.] (2015) - Neural networks applied to discriminate botanical origin of honeys. ISSN 0308-8146. 175. P. 128-136.
0308-8146
10.1016/j.foodchem.2014.11.121
Title
Neural networks applied to discriminate botanical origin of honeys
Subject
Honey Physical–chemical parameters Botanical origin Neural networks Overfitting Classification problem
Date
2017-07-22T16:14:31Z 2017-07-22T16:14:31Z 2015
Description
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. info:eu-repo/semantics/publishedVersion
