Random Forest, Artificial Neural Network, and Support Vector Machine Models for Honey Classification

Random Forest, Artificial Neural Network, and Support Vector Machine Models for Honey Classification

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 w...

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Bibliographic Details
Main Authors: Cecilia Martinez-Castillo, Gonzalo Astray, Juan Carlos Mejuto, Jesus Simal-Gandara
Format: Article
Language:English
Published: Atlantis Press 2019-10-01
Series:eFood
Subjects:
Food authenticity
honey
Galician honeys
classification models
Online Access:https://www.atlantis-press.com/article/125919278/view
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spelling doaj-db5a568ac99d467bbf684cad86d22ff42021-02-01T15:04:29ZengAtlantis PresseFood2666-30662019-10-011110.2991/efood.k.191004.001Random Forest, Artificial Neural Network, and Support Vector Machine Models for Honey ClassificationCecilia Martinez-CastilloGonzalo AstrayJuan Carlos MejutoJesus Simal-GandaraDifferent 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.https://www.atlantis-press.com/article/125919278/viewFood authenticityhoneyGalician honeysclassification models
collection DOAJ
language English
format Article
sources DOAJ
author Cecilia Martinez-Castillo
Gonzalo Astray
Juan Carlos Mejuto
Jesus Simal-Gandara
spellingShingle Cecilia Martinez-Castillo
Gonzalo Astray
Juan Carlos Mejuto
Jesus Simal-Gandara
Random Forest, Artificial Neural Network, and Support Vector Machine Models for Honey Classification
eFood
Food authenticity
honey
Galician honeys
classification models
author_facet Cecilia Martinez-Castillo
Gonzalo Astray
Juan Carlos Mejuto
Jesus Simal-Gandara
author_sort Cecilia Martinez-Castillo
title Random Forest, Artificial Neural Network, and Support Vector Machine Models for Honey Classification
title_short Random Forest, Artificial Neural Network, and Support Vector Machine Models for Honey Classification
title_full Random Forest, Artificial Neural Network, and Support Vector Machine Models for Honey Classification
title_fullStr Random Forest, Artificial Neural Network, and Support Vector Machine Models for Honey Classification
title_full_unstemmed Random Forest, Artificial Neural Network, and Support Vector Machine Models for Honey Classification
title_sort random forest, artificial neural network, and support vector machine models for honey classification
publisher Atlantis Press
series eFood
issn 2666-3066
publishDate 2019-10-01
description 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.
topic Food authenticity
honey
Galician honeys
classification models
url https://www.atlantis-press.com/article/125919278/view
work_keys_str_mv AT ceciliamartinezcastillo randomforestartificialneuralnetworkandsupportvectormachinemodelsforhoneyclassification
AT gonzaloastray randomforestartificialneuralnetworkandsupportvectormachinemodelsforhoneyclassification
AT juancarlosmejuto randomforestartificialneuralnetworkandsupportvectormachinemodelsforhoneyclassification
AT jesussimalgandara randomforestartificialneuralnetworkandsupportvectormachinemodelsforhoneyclassification
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