Abstract
This paper discusses a machine learning approach, aimed at the definition of methods for authenticity assessment of some of the highest valued Nebbiolo-based wines from Piedmont (Italy). This issue is one of the most relevant in the wine market, where commercial frauds related to such a kind of products are estimated to be worth millions of Euros. The main objective of the work is to demonstrate the effectiveness of classification algorithms in exploiting simple features about the chemical profile of a wine, obtained from inexpensive standard bio-chemical analyses. We report on experiments performed with datasets of real samples and with synthetic datasets which have been artificially generated from real data through the learning of a Bayesian network generative model.
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Notes
- 1.
Classification experiments have been performed also with the datasets of 40 and 15 attributes; results are reported in [1].
- 2.
The regularization parameter (a.k.a complexity) has been set to 10, since we are dealing with a multi-class problem with 9 different classes, and it is a good practice to set the parameter close to the number of classes [13].
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Acknowledgments
The present work has been funded by Regione Piemonte (POR-FESR grants), as a part of the TRAQUASwine project. We would like to thank M. Arlorio, J.D. Coïsson, M. Locatelli and F. Travaglia for their collaboration.
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Leonardi, G., Portinale, L. (2017). Applying Machine Learning to High-Quality Wine Identification. In: Esposito, F., Basili, R., Ferilli, S., Lisi, F. (eds) AI*IA 2017 Advances in Artificial Intelligence. AI*IA 2017. Lecture Notes in Computer Science(), vol 10640. Springer, Cham. https://doi.org/10.1007/978-3-319-70169-1_3
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