Application of Statistical Methods for Classification of Varietal and Regional Origin of White Wines
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White wines of four wine-growing regions of Krasnodar region were studied by the ICP-AES method. Nineteen elements were identified in 153 samples of white wines of the Chardonnay, Riesling, and Muscat brands. Methods of statistical analysis demonstrated that, despite the complicated technological cycle of wine production (soil–grapes–juice–wine), information about the grape variety and the region of its origin is preserved in the totality of trace elements. Probabilistic statistical models were constructed to identify the grape variety and the region where it was grown by the concentrations of a certain set of microelements contained in wine. The proposed models are based on the assumption that the soil origin is an important factor determining the microelement composition in wine.
Keywords:identification of regional and varietal origin of white wines content of trace elements in wines methods of classification trees discriminant analysis
The study was supported by the Russian Foundation for Basic Research (grant no. 18-03-00059); experiments were carried out with the use of scientific equipment of the Ecological and Analytical Center of the Kuban State University, unique identifier RFMEFI59317Х0008.
- 2.Korovin, I.A., Panasyuk, A.L., et al., Vino i alkogol’nye napitki. Direktivy i reglamenty Evropeiskogo soyuza (Wine and Alcoholic Drinks. European Union Directives and Regulations), Moscow: Izd. Standartov, 2000.Google Scholar
- 3.Titarenko, V.O., Kaunova, A.A., Temerdashev, Z.A., and Popandopulo, V.G., Investigation of the correlation between the elemental content of grapes and the soil of the region of its growth, Anal. Kontrol’, 2016, vol. 20, no. 2, pp. 138–146.Google Scholar
- 5.Yakuba, Yu.F., Kaunova, A.A., Temerdashev, Z.A., et al., Grape wines, problems of their quality and regional origin evaluation, Anal. Kontrol’, 2014, vol. 18, no. 4, pp. 344–373.Google Scholar
- 9.Giaccio, M. and Vicentini, A., Determination of the geographical origin of wines by means of the mineral content and the stable isotope ratios: a review, J. Commod. Sci., Technol. Qual., 2008, vol. 47, pp. 267–284.Google Scholar
- 11.Khalafyan, A.A., Yakuba, Yu.F., and Temerdashev, Z.A., Application of table of congruences and correspondence analysis to comparative assessment of wine quality in rating scale, Zavod. Lab., Diagn. Mater., 2016, vol. 82, no. 2, pp. 66–71.Google Scholar
- 12.Khalafyan, A.A., Temerdashev, Z.A., Yakuba, Yu.F., and Gutuchkina, T.I., The use of multivariate analysis for the final evaluation of the results of expert assessments, Zavod. Lab., Diagn. Mater., 2016, vol. 82, no. 10, pp. 71–78.Google Scholar
- 13.Egorov, E.A., Guguchkina, T.I., Adzhiev, A.M., and Oseledtseva, I.V., Geograficheskie zony vin i natsional’nykh kon’yakov (brendi) vysokogo kachestva na yuge Rossii (Geographical Production Areas of High-Quality Wine and National Cognac (Brandy) in Southern of Russia), Krasnodar: Prosveshchenie-Yug, 2013.Google Scholar
- 15.Khalafyan, A.A., Statistica 6. Matematicheskaya statistika s elementami teorii veroyatnostei (Statistica 6: Mathematical Statistics with Elements of Theory of Probability), Moscow: Binom, 2010.Google Scholar
- 16.StatSoft, Electronic textbook on statistics, 2012. http://www.statsoft.ru/home/textbook/default.htm. Accessed March 28, 2017.Google Scholar