Journal of Analytical Chemistry

, Volume 74, Issue 6, pp 617–624 | Cite as

Determination of the Wine Variety and Geographical Origin of White Wines Using Neural Network Technologies

  • A. A. Khalafyan
  • Z. A. TemerdashevEmail author
  • A. A. Kaunova
  • A. G. Abakumov
  • V. O. Titarenko
  • V. A. Akin’shina
  • E. A. Ivanovets


In order to determine the geographical origin and wine variety of white wines, we studied 153 samples of the white wines Riesling (49), Chardonnay (56), and Muscat (48) produced in the territory of the main wineries of geographical zones in the Krasnodar krai. The concentrations of trace and macro elements in wines were determined by inductively coupled plasma atomic emission spectrometry. Chemometric studies were performed using the STATISTICA Neural Networks. From a set of 15 trace and macro elements determined, 5 trace elements (Fe, Mg, Rb, Ti, and Na) were recognized by correlation analysis as the predictors of a constructed neural network model, which successfully identified the brands of wines. To determine the region of grape growing, a neural network model was constructed based on six predictors: five trace elements and a specified wine brand. A software was developed to automate the computations required.


geographical origin and wine variety elemental analysis neural network technologies 



The experiments were carried out with the use of equipment of the Environmental Analytical Center of Collective Use at the Kuban State University, unique identifier RFMEFI59317Х0008.


This study was supported by the Russian Foundation for Basic Research (project no. 18-03-00059).


  1. 1.
    Schlesier, K., Fauhl-Hassek, C., Forina, M., Cotea, M., Kocsi, V., Schoula, E., van Jaarsveld, R., and Wittkowski, F.R., Eur. Food Res. Technol., 2009, vol. 230, no. 1, p. 1.CrossRefGoogle Scholar
  2. 2.
    Daniel, C. and Smyth, H., Compr. Anal. Chem., 2013, vol. 60, p. 385.CrossRefGoogle Scholar
  3. 3.
    Geana, I., Iordache, A., Ionete, R., Marinescu, A., Ranca, A., and Culea, M., Food Chem., 2013, vol. 138, p. 1125.CrossRefGoogle Scholar
  4. 4.
    Selih Vid, S., Sala, M., and Drgan, V., Food Chem., 2014, vol. 153, p. 414.CrossRefGoogle Scholar
  5. 5.
    Martin, A.E., Watling, R.J., and Lee, G.S., Food Chem., 2012, vol. 133, p. 1081.CrossRefGoogle Scholar
  6. 6.
    Rodrigues, S.M., Otero, M., Alves, A.A., Coimbra, J., Coimbra, M.A., Pereira, E., and Duarte, A.C., J. Food Compos. Anal., 2011, vol. 24, nos. 4–5, p. 548.CrossRefGoogle Scholar
  7. 7.
    Khalafyan, A.A., Yakuba, Yu.F., Temerdashev, Z.A., Kaunova, A.A., and Titarenko, V.O., J. Anal. Chem., 2016, vol. 71, no. 11, p. 1138.CrossRefGoogle Scholar
  8. 8.
    Geana, E.I., Marinescu, A., Iordache, A.M., Sandru, C., Ionete, R.E., and Bala, C., Food Anal. Methods, 2014, vol. 7, p. 2064.CrossRefGoogle Scholar
  9. 9.
    Dinca, O.R., Ionete, R.E., Costinel, D., Geana, I.E., Popescu, R., Stefanescu, I., and Radu, G.L., Food Anal. Methods, 2016, vol. 9, p. 2406.CrossRefGoogle Scholar
  10. 10.
    Kaunova, A.A. Titarenko, V.O., Temerdashev, Z.A., Sekunova, M.V., and Popandopulo, V.G., Zavod. Lab., Diagn. Mater., 2016, vol. 82, no. 8, p. 69.Google Scholar
  11. 11.
    Giaccio, M. and Vicentini, A., J. Commod. Sci., Technol. Qual., 2008, vol. 47, p. 267.Google Scholar
  12. 12.
    Ríos-Reina, R., Elcoroaristizabal, S., Ocaña-González, J.A., García-González, D.L., Amigo, J.M., and Callejón, R.M., Food Chem., 2017, vol. 230, no. 9, p. 108.CrossRefGoogle Scholar
  13. 13.
    Pohl, P., TrAC, Trends Anal. Chem., 2007, vol. 26, p. 941.CrossRefGoogle Scholar
  14. 14.
    Hopfer, H., Nelson, J., Collins, T.S., Heymann, H., and Ebeler, S.E., Food Chem., 2015, vol. 172, p. 486.CrossRefGoogle Scholar
  15. 15.
    Jurado, J.M., Alcázar, A., Palacios-Morillo, A., and de Pablos, F., Food Chem., 2012, vol. 135, p. 898.CrossRefGoogle Scholar
  16. 16.
    Rapeanu, G., Vicol, C., and Bichescu, C., Innovative Rom. Food Biotechnol., 2009, vol. 5, p. 1.Google Scholar
  17. 17.
    Grindlay, G., Mora, J., Gras, L., and de Loos-Vollebregt, M.T.C., Anal. Chim. Acta, 2011, vol. 691, nos. 1–2, p. 18.CrossRefGoogle Scholar
  18. 18.
    Ivanova-Petropulos, V., Balabanova, B., Mitrev, S., Nedelkovski, D., Dimovska, V., and Gulaboski, R., Food Anal. Methods, 2016, vol. 9, no. 1, p. 48.CrossRefGoogle Scholar
  19. 19.
    Gonzálvez, A., Armenta, S., Pastor, A., and de la Guardia, M., J. Agric. Food Chem., 2008, vol. 56, no. 13, p. 4943.CrossRefGoogle Scholar
  20. 20.
    Food Protected Designation of Origin: Methodologies and Applications, de la Guardia, M. and Gonzálvez, A., Eds., vol. 60 of Comprehensive Analytical Chemistry, Amsterdam: Elsevier, 2013.Google Scholar
  21. 21.
    Zioła-Frankowska, A. and Frankowski, M., Food Anal. Methods, 2017, vol. 10, p. 180.CrossRefGoogle Scholar
  22. 22.
    Kaunova, A.A., Petrov, V.I., Tsyupko, T.G., Temerdashev, Z.A., Perekotii, V.V., and Luk’yanov, A.A., J. Anal. Chem., 2013, vol. 68, no. 9, p. 831.CrossRefGoogle Scholar
  23. 23.
    Hill, T. and Lewicki, P., Statistics Methods and Applications, Tulsa, OK: StatSoft, 2007.Google Scholar
  24. 24.
    Neironnye seti. STATISTICA Neural Networks. Metodologiya i tekhnologii sovremennogo analiza dannykh (Neural Networks. STATISTICA Neural Networks. Methodology and Technology of Modern Data Analysis), Borovikov, V.P., Ed., Moscow: Telekom, 2008, 2nd ed.Google Scholar
  25. 25.
    Titarenko, V.O., Khalafyan, A.A., Temerdashev, Z.A., Kaunova, A.A., and Ivanovets, E.A., Inorg. Mater., 2018, vol. 54, no. 14, p. 1435.CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2019

Authors and Affiliations

  • A. A. Khalafyan
    • 1
  • Z. A. Temerdashev
    • 1
    Email author
  • A. A. Kaunova
    • 1
  • A. G. Abakumov
    • 1
  • V. O. Titarenko
    • 1
  • V. A. Akin’shina
    • 1
  • E. A. Ivanovets
    • 1
  1. 1.Kuban State UniversityKrasnodarRussia

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