
Random Forest, Artificial Neural Network, and Support Vector Machine Models for Honey Classification
Author(s) -
MartinezCastillo Cecilia,
Astray Gonzalo,
Mejuto Juan Carlos,
SimalGandara Jesus
Publication year - 2020
Publication title -
efood
Language(s) - English
Resource type - Journals
ISSN - 2666-3066
DOI - 10.2991/efood.k.191004.001
Subject(s) - random forest , support vector machine , artificial neural network , artificial intelligence , computer science , machine learning , pattern recognition (psychology)
Different separated protein fractions by the electrophoretic method in polyacrylamide gel were used to classify two different types of honeys, Galician honeys and commercial honeys produced and packaged outside of Galicia. Random forest, artificial neural network, and support vector machine models were tested to differentiate Galician honeys and other commercial honeys produced and packaged outside of Galicia. The results obtained for the best random forest model allowed us to determine the origin of honeys with an accuracy of 95.2%. The random forest model, and the other developed models, could be improved with the inclusion of new data from different commercial honeys.