Advertisement

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)

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.

Keywords

Classification Fraud detection Artificial data generation 

Notes

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.

References

  1. 1.
    Arlorio, M., Coisson, J., Leonardi, G., Locatelli, M., Portinale, L.: Exploiting data mining for authenticity assessment and protection of high-quality Italian wines from Piedmont. In: Proceedings of 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2015), pp. 1671–1680. AUS, Sydney (2015)Google Scholar
  2. 2.
    Arvanitoyannis, I., Katsota, M., Psarra, E., Soufleros, E., Kallithraka, S.: Application of quality control methods for assessing wine authenticity: use of multivariate analysis (chemometrics). Trends Food Sci. Technol. 10, 321–336 (1999)CrossRefGoogle Scholar
  3. 3.
    Cooper, G., Herskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9(4), 309–347 (1992)zbMATHGoogle Scholar
  4. 4.
    Džeroski, S., Ženko, B.: Is combining classifiers with stacking better than selecting the best one? Mach. Learn. 54(3), 255–273 (2004)CrossRefzbMATHGoogle Scholar
  5. 5.
    Gòmez-Meire, S., Campos, C., Falqué, E., Dìaz, F., Fdez-Riverola, F.: Assuring the authenticity of northwest Spain white wine varieties using machine learning techniques. Food Res. Int. 60, 230–240 (2014)CrossRefGoogle Scholar
  6. 6.
    Grzegorczyk, M.: An introduction to Gaussian Bayesian networks. In: Yan, Q. (ed.) Systems Biology in Drug Discovery and Development, vol. 662, pp. 121–147. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-1-60761-800-3_6
  7. 7.
    Halkidi, M., Batistakis, Y., Varzirgannis, M.: Cluster validity methods: Part 1. ACM SIGMOD Record 31(2), 40–45 (2002)CrossRefGoogle Scholar
  8. 8.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  9. 9.
    Hall, M.A.: Correlation-based feature subset selection for machine learning. Ph.D. thesis, University of Waikato, Hamilton, New Zealand (1998)Google Scholar
  10. 10.
    Holmberg, L.: Wine fraud. Int. J. Wine Res. 2010(2), 105–113 (2010)CrossRefGoogle Scholar
  11. 11.
    Locatelli, M., Travaglia, F., Coïsson, J., Bordiga, M., Arlorio, M.: Phenolic composition of Nebbiolo grape (Vitis vinifera L.) from Piedmont: characterization during ripening of grapes selected in different geographic areas and comparison with Uva Rara and Vespolina. Eur. Food Res. Technol. 242, 1057–1068 (2016)CrossRefGoogle Scholar
  12. 12.
    Marini, F., Bucci, R., Magr, A., Magr, A.: Authentication of Italian CDO wines by class-modeling techniques. Chemom. Intell. Lab. Syst. 84(1), 164–171 (2006)CrossRefGoogle Scholar
  13. 13.
    Mattera, D., Haykin, S.: Support vector machines for dynamic reconstruction of a chaotic system. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods, pp. 211–241. MIT Press, Cambridge (1999)Google Scholar
  14. 14.
    Platt, J.: Fast training of support vector machines using sequential minimal optimization. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods, pp. 185–208. MIT Press, Cambridge (1999)Google Scholar
  15. 15.
    Platt, J.: Probability for SV machines. In: Smola, A., Batlett, P., Schölkopf, B., Schuurmans, D. (eds.) Advances in Large Margin Classifiers, pp. 61–74. MIT Press, Cambridge (2000)Google Scholar
  16. 16.
    Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction and Search. Springer, Berlin (1993).  https://doi.org/10.1007/978-1-4612-2748-9
  17. 17.
    Üstün, B., Melssen, W., Buydens, L.: Facilitating the application of support vector regression by using a universal Pearson VII function based kernel. Chemom. Intell. Lab. Syst. 81, 29–40 (2006)CrossRefGoogle Scholar
  18. 18.
    Versari, A., Laurie, V., Ricci, A., Laghi, L., Parpinello, G.: Progress in authentication, typification and traceability of grapes and wines by chemometric approaches. Food Res. Int. 60, 2–18 (2014)CrossRefGoogle Scholar
  19. 19.
    Wagstaff, K.: Machine learning that matters. In: Proceedings of the 29th International Conference on Machine Learning (ICML 2012), Edinburgh, UK (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

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

Personalised recommendations