Applying Machine Learning to High-Quality Wine Identification

  • Giorgio Leonardi
  • Luigi PortinaleEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10640)


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.


Classification Fraud detection Artificial data generation 



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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Computer Science Institute, DiSITUniversity of Piemonte OrientaleAlessandriaItaly

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