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Volume 11
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10.3390/app11156723
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Article

Opportunities and Constraints in Applying Artificial Neural Networks (ANNs) in Food Authentication. Honey—A Case Study

by
Ariana Raluca Hategan
1,
Romulus Puscas
2,
Gabriela Cristea
2,
Adriana Dehelean
2,
Francois Guyon
3,
Arthur Jozsef Molnar
1,
Valentin Mirel
2 and
Dana Alina Magdas
2,*
1
Faculty of Mathematics and Computer Science, Babes-Bolyai University, 400084 Cluj-Napoca, Romania
2
National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Str., 400293 Cluj-Napoca, Romania
3
Service Commun des Laboratoires, 146 Traverse Charles Susini, 13388 Marseille, France
*
Author to whom correspondence should be addressed.
Appl. Sci. 2021, 11(15), 6723; https://doi.org/10.3390/app11156723
Submission received: 16 June 2021 / Revised: 15 July 2021 / Accepted: 20 July 2021 / Published: 22 July 2021
(This article belongs to the Special Issue Emerging Technologies in Food and Beverages Authentication)
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Abstract

The present work aims to test the potential of the application of Artificial Neural Networks (ANNs) for food authentication. For this purpose, honey was chosen as the working matrix. The samples were originated from two countries: Romania (50) and France (53), having as floral origins: acacia, linden, honeydew, colza, galium verum, coriander, sunflower, thyme, raspberry, lavender and chestnut. The ANNs were built on the isotope and elemental content of the investigated honey samples. This approach conducted to the development of a prediction model for geographical recognition with an accuracy of 96%. Alongside this work, distinct models were developed and tested, with the aim of identifying the most suitable configurations for this application. In this regard, improvements have been continuously performed; the most important of them consisted in overcoming the unwanted phenomenon of over-fitting, observed for the training data set. This was achieved by identifying appropriate values for the number of iterations over the training data and for the size and number of the hidden layers and by introducing of a dropout layer in the configuration of the neural structure. As a conclusion, ANNs can be successfully applied in food authenticity control, but with a degree of caution with respect to the “over optimization” of the correct classification percentage for the training sample set, which can lead to an over-fitted model.
Keywords: ANNs; honey; geographical differentiation; recognition models; food authentication ANNs; honey; geographical differentiation; recognition models; food authentication

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MDPI and ACS Style

Hategan, A.R.; Puscas, R.; Cristea, G.; Dehelean, A.; Guyon, F.; Molnar, A.J.; Mirel, V.; Magdas, D.A. Opportunities and Constraints in Applying Artificial Neural Networks (ANNs) in Food Authentication. Honey—A Case Study. Appl. Sci. 2021, 11, 6723. https://doi.org/10.3390/app11156723

AMA Style

Hategan AR, Puscas R, Cristea G, Dehelean A, Guyon F, Molnar AJ, Mirel V, Magdas DA. Opportunities and Constraints in Applying Artificial Neural Networks (ANNs) in Food Authentication. Honey—A Case Study. Applied Sciences. 2021; 11(15):6723. https://doi.org/10.3390/app11156723

Chicago/Turabian Style

Hategan, Ariana Raluca, Romulus Puscas, Gabriela Cristea, Adriana Dehelean, Francois Guyon, Arthur Jozsef Molnar, Valentin Mirel, and Dana Alina Magdas. 2021. "Opportunities and Constraints in Applying Artificial Neural Networks (ANNs) in Food Authentication. Honey—A Case Study" Applied Sciences 11, no. 15: 6723. https://doi.org/10.3390/app11156723

APA Style

Hategan, A. R., Puscas, R., Cristea, G., Dehelean, A., Guyon, F., Molnar, A. J., Mirel, V., & Magdas, D. A. (2021). Opportunities and Constraints in Applying Artificial Neural Networks (ANNs) in Food Authentication. Honey—A Case Study. Applied Sciences, 11(15), 6723. https://doi.org/10.3390/app11156723

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