Prediction of Wine Sensorial Quality by Routinely Measured Chemical Properties

Adriána Bednárová 1 , Roman Kranvogl 2 , Darinka Brodnjak-Vončina 2 , and Tjaša Jug 3
  • 1 Department of Chemistry, Faculty of Natural Sciences, University of SS Cyril and Methodius in Trnava, Nám. J. Herdu 2, Trnava, SK-917 01, Slovak Republic
  • 2 Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia
  • 3 Chamber of Agriculture and Forestry of Slovenia, Institute for Agriculture and Forestry, Pri hrastu 18, 5000 Nova Gorica, Slovenia

Abstract

The determination of the sensorial quality of wines is of great interest for wine consumers and producers since it declares the quality in most of the cases. The sensorial assays carried out by a group of experts are time-consuming and expensive especially when dealing with large batches of wines. Therefore, an attempt was made to assess the possibility of estimating the wine sensorial quality with using routinely measured chemical descriptors as predictors. For this purpose, 131 Slovenian red wine samples of different varieties and years of production were analysed and correlation and principal component analysis were applied to find inter-relations between the studied oenological descriptors. The method of artificial neural networks (ANNs) was utilised as the prediction tool for estimating overall sensorial quality of red wines. Each model was rigorously validated and sensitivity analysis was applied as a method for selecting the most important predictors. Consequently, acceptable results were obtained, when data representing only one year of production were included in the analysis. In this case, the coefficient of determination (R2) associated with training data was 0.95 and that for validation data was 0.90. When estimating sensorial quality in categorical form, 94 % and 85 % of correctly classified samples were achieved for training and validation subset, respectively.

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  • ARVANITOYANNIS, I.S., KATSOTA, M.N., PSARRA, E.P., SOUFLEROS, E.H., KALLITHRAKAY, S.: Application of quality control methods for assessing wine authenticity: Use of multivariate analysis (chemometrics). Trends Food Sci. Tech., 10, 1999, 321-336.

  • BEDNÁROVÁ, A., KRANVOGL, R., BRODNJAK-VONČINA, D., JUG, T., BEINROHR, E.: Characterization of Slovenian Wines Using Multidimensional Data Analysis from Simple Enological Descriptors. Acta Chim. Slov., 60, 2013, 274-286.

  • BELTRÁN, N.H., DUARTE-MERMOUD, M.A., BUSTOS, M.A., SALAH, S.A., LOYOLA, E.A., PEÑA-NEIRA, A.I., JALOCHA, J.W.: Feature extraction and classification of Chilean wines. J. Food Eng., 75, 2006, 1-10.

  • BURATTI, S., BALLABIO, D., BENEDETTI, S., COSIO, M.S.: Prediction of Italian red wine sensorial descriptors from electronic nose, electronic tongue and spectrophotometric measurements by means of Genetic Algorithm regression models. Food Chem., 100, 2007, 211-218.

  • BISHOP, C.M.: Neural Networks for Pattern Recognition, Clarendon Press, Oxford, 1995, 482 pp.

  • CÂMARA, J.S., ALVES, M.A., MARQUES, J.C.: Multivariate analysis for the classification and differentiation of Madeira wines according to main grape varieties. Talanta, 68, 2006, 1512-1521.

  • COZZOLINO, D., CYNKAR, W.U., SHAH N., DAMBERGS R.G., SMITH P.A.: A brief introduction to multivariate methods in grape and wine analysis. Int. J. Wine Res., 1, 2009, 123-130.

  • GEMPERLINE, P.: Practical Guide to Chemometrics, CRC Press, Boca Raton, 2006, 520 pp.

  • HAYKIN, S.: Neural Networks: A comprehensive Foundation, Pearson Education, Dehli, 1999, 823 pp.

  • HUNTER, A., KENNEDY, L., HENRY, J., FERGUSON, I.: Application of neural networks and sensitivity analysis to improved prediction of trauma survival. Comput. Meth. Prog. Bio., 62, 2000, 11-19.

  • KRUZLICOVA, D., MOCAK, J., BALLA, B., PETKA, J., FARKOVA, M., HAVEL, J.: Classification of Slovak white wines using artificial neural networks and discriminant analysis. Food Chem., 112, 2009, 1046-1052.

  • KRUZLICOVA, D., FIKET, Ž., KNIEWALD, G.: Classification of Croatian wine varieties using multivariate analysis of data obtained by high resolution ICP-MS analysis. Food Res. Int., 54, 2013, 621-626.

  • LEGIN, A., RUDNITSKAYA, A., LVOVA, L., VLASOV, Y., DI NATALE, C., D’AMICO, A.: Evaluation of Italian wine by the electronic tongue recognition, quantitative analysis and correlation with human sensory perception. Anal. Chim. Acta, 484, 2003, 33-44.

  • LÓPEZ-FERIA, S., CÁRDENAS, S., VALCÁRCEL, M.: Simplifying chromatographic analysis of the volatile fraction of foods. Trends Anal. Chem., 27, 2008, 794-803.

  • LORRAIN, B., TEMPERE, S., ITURMENDI, N., MOINE, V., DE REVEL, G., TEISSEDRE, P.-L.: Influence of phenolic compounds on the sensorial perception and volatility of red wine esters in model solution: An insight at the molecular level. Food Chem., 140, 2013, 76-82.

  • MILLER, J.N., MILLER, J.C.: Statistics and Chemometrics for Analytical Chemistry, Pearson Education Limited, Harlow, 2010, 278 pp.

  • SÁENZ-NAVAJAS, M.-P., AVIZCURI, J.-M., FERREIRA, V., FERNÁNDEZZURBANO, P.: Insights on the chemical basis of the astringency of Spanish red wines. Food Chem., 134, 2012, 1484-1493. POHL, P.: What do metals tell us about wine? Trends Anal. Chem., 26, 2007, 941-949.

  • SAURINA, J.: Characterization of wines using compositional profiles and chemometrics. Trends Anal. Chem., 29, 2010, 234-245.

  • ŠNUDERL, K., MOCAK, J., BRODNJAK-VONČINA, D., SEDLÁČKOVÁ, B.: Classification of white varietal wines using chemical analysis and sensorial evaluations. Acta Chim. Slov., 56, 2009, 765-772.

  • VARMUZA, K., FILZMOSER, P.: Introduction to Multivariate Statistical Analysis in Chemometrics, CRC Press, Boca Raton, 2009, 321 pp.

  • ZUPAN, J., GASTEIGER, J.: Neural Networks for Chemists: An introduction, VCH, New York, 1993, 305 pp.

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