eFood

In Press, Corrected Proof, Available Online: 17 October 2019

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

Authors
Cecilia Martinez-Castillo1, 2, Gonzalo Astray1, *, Juan Carlos Mejuto1, Jesus Simal-Gandara2, *
1Department of Physical Chemistry, Faculty of Food Science and Technology, University of Vigo - Ourense Campus, Ourense E32004, Spain
2Nutrition and Bromatology Group, Department of Analytical and Food Chemistry, Faculty of Food Science and Technology, University of Vigo - Ourense Campus, Ourense E32004, Spain
*Corresponding authors. Email: gastray@uvigo.es; jsimal@uvigo.es
Corresponding Author
Gonzalo Astray
Received 14 May 2019, Accepted 29 September 2019, Available Online 17 October 2019.
DOI
https://doi.org/10.2991/efood.k.191004.001How to use a DOI?
Keywords
Food authenticity, honey, Galician honeys, classification models
Abstract

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.

Copyright
© 2019 International Association of Dietetic Nutrition and Safety. Publishing services by Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
eFood
Publication Date
2019/10
ISSN (Online)
2666-3066
DOI
https://doi.org/10.2991/efood.k.191004.001How to use a DOI?
Copyright
© 2019 International Association of Dietetic Nutrition and Safety. Publishing services by Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Cecilia Martinez-Castillo
AU  - Gonzalo Astray
AU  - Juan Carlos Mejuto
AU  - Jesus Simal-Gandara
PY  - 2019
DA  - 2019/10
TI  - Random Forest, Artificial Neural Network, and Support Vector Machine Models for Honey Classification
JO  - eFood
SN  - 2666-3066
UR  - https://doi.org/10.2991/efood.k.191004.001
DO  - https://doi.org/10.2991/efood.k.191004.001
ID  - Martinez-Castillo2019
ER  -