Comparative technological characteristics of vine varieties for red wines (Vitis vinifera L.)
Keywords:
comparative cluster analysis; dispersion analysis; principal component analysis (PCA); technological indicators of wine; vine varieties for red winesAbstract
The subject of the study are 41 local, introduced and newly developed vine varieties for the production of red wines, examined according to the most important technological indicators reflecting the quality of the wine obtained from them: alcohol, sugars, sugar-free extract, titratable acids, volatile acids, pH, anthocyanins, color intensity and tasting score. The researched vine varieties are grouped on the basis of their phenotypic proximity and remoteness in view of the mentioned indicators. A combination of statistical approaches is applied for this purpose. The groups of the respective varieties with similar phenotypic characteristics are obtained by means of a hierarchical cluster analysis. For the qualitative description of the formed clusters, one factor dispersion analysis, Duncan’s test, and principal component analysis (PCA) are conducted. Three generalized clusters are formed. The first one is the largest, including the varieties whose wines are characterized by a relatively low content of sugar-free extract, moderate pH level, a smaller amount of anthocyanins and low color intensity. The second cluster consists of varieties with comparatively high alcohol content in wines, as well as high pH, anthocyanins, color intensity, and a very good tasting score – Syrah, Petit Verdot and Cabernet Sauvignon. The third cluster comprises of Ancellotta, Grand Noir, Dornfelder, Alicante Bouschet and Saperavi, whose wines contain significant amounts of anthocyanins and possess high color intensity. The amount of anthocyanins, the color intensity and the sugar-free extract in wine exert the most significant influence on the differentiation of red wine varieties into groups.
References
Anastasiadi, M., Zira, A., Magiatis, P., Haroutounian, S. A., Skaltsounis, A. L., & Mikros, E. (2009). 1H NMR-based metabonomics for the classification of Greek wines according to variety, region, and vintage. Comparison with HPLC data. Journal of Agricultural and Food Chemistry, 57(23), 11067-11074.
Bulgarian Аmpelography. (1990). General ampelography. Publishing House of the Bulgarian Academy of Sciences. Agricultural Academy. Institute of Viticulture and Enology – Pleven. Sofia, Volume. І, 296 p.
Bulgarian Аmpelography. (2010). Private Аmpelografy, Sofia, Volume II, 280 p.
Bulgarian Аmpelography. (2015). Private Аmpelografy, Sofia, Volume III, 274 p.
Barata, A., Pais, A., Malfeito-Ferreira, M., & Loureiro, V. (2011). Influence of sour rotten grapes on the chemical composition and quality of grape must and wine. European Food Research and Technology, 233(2), 183-194.
Câmara, J. S., Alves, M. A., & Marques, J. C. (2006). Multivariate analysis for the classification and differentiation of Madeira wines according to main grape varieties. Talanta, 68(5), 1512-1521.
Coetzee, P. P., Steffens, F. E., Eiselen, R. J., Augustyn, O. P., Balcaen, L., & Vanhaecke, F. (2005). Multi-element analysis of South African wines by ICP− MS and their classification according to geographical origin. Journal of Agricultural and Food Chemistry, 53(13), 5060-5066.
Fan, S., Zhong, Q., Fauhl-Hassek, C., Pfister, M. K. H., Horn, B., & Huang, Z. (2018). Classification of Chinese wine varieties using 1H NMR spectroscopy combined with multivariate statistical analysis. Food Control, 88, 113-122.
Figueiredo-González, M., Martínez-Carballo, E., Cancho-Grande, B., Santiago, J. L., Martínez, M. C., & Simal-Gándara, J. (2012). Pattern recognition of three Vitis vinifera L. red grapes varieties based on anthocyanin and flavonol profiles, with correlations between their biosynthesis pathways. Food Chemistry, 130(1), 9-19.
Fraige, K., Pereira-Filho, E. R., & Carrilho, E. (2014). Fingerprinting of anthocyanins from grapes produced in Brazil using HPLC–DAD–MS and exploratory analysis by principal component analysis. Food chemistry, 145, 395-403.
Geana, E., Ciucure, C., Apetrei, C., & Artem, V. (2019). Application of spectroscopic UV-Vis and FT-IR screening techniques coupled with multivariate statistical analysis for red wine authentication: varietal and vintage year discrimination, Molecules, 24 (22), 4166, https://www.mdpi.com/1420-3049/24/22/4166/htm
Geana, E. I., Popescu, R., Costinel, D., Dinca, O. R., Ionete, R. E., Stefanescu, I., ... & Bala, C. (2016). Classification of red wines using suitable markers coupled with multivariate statistic analysis. Food chemistry, 192, 1015-1024.
Son, H. S., Hwang, G. S., Ahn, H. J., Park, W. M., Lee, C. H., & Hong, Y. S. (2009). Characterization of wines from grape varieties through multivariate statistical analysis of 1H NMR spectroscopic data. Food Research International, 42(10), 1483-1491.
Xiao, H., Li, A., Li, M., Sun, Y., Tu, K., Wang, S., & Pan, L. (2018). Quality assessment and discrimination of intact white and red grapes from Vitis vinifera L. at five ripening stages by visible and near-infrared spectroscopy. Scientia Horticulturae, 233, 99-107.
Ivanov, T. (1981). Wine technology. Plovdiv, Publishing house “Hristo G. Danov”, 574 p. (Bg).
Kallithraka, S., Mohdaly, A. A. A., Makris, D. P., & Kefalas, P. (2005). Determination of major anthocyanin pigments in Hellenic native grape varieties (Vitis vinifera sp.): association with antiradical activity. Journal of Food Composition and Analysis, 18(5), 375-386.
Karimali, D., Kosma, I., & Badeka, A. (2020). Varietal classification of red wine samples from four native Greek grape varieties based on volatile compound analysis, color parameters and phenolic composition. European Food Research and Technology, 246(1), 41-53.
Landau, S., & Everitt, B. S. (2004). A handbook of statistical analyses using SPSS. Chapman and Hall/CRC.
Liu, L., Cozzolino, D., Cynkar, W. U., Dambergs, R. G., Janik, L., O’neill, B. K., ... & Gishen, M. (2008). Preliminary study on the application of visible–near infrared spectroscopy and chemometrics to classify Riesling wines from different countries. Food chemistry, 106(2), 781-786.
Penkov, М. (2008). The most valuable varieties of vines and vine rootstocks for creating modern and efficient vineyards in Bulgaria. Sofia, Zemizdat, 159 p. (Bg).
Roychev, V. (2012). Ampelography. Academic Publishing House of the Agricultural University- Plovdiv, 574 p. (Bg).
Rajha, H. N., Boussetta, N., Louka, N., Maroun, R. G., & Vorobiev, E. (2014). A comparative study of physical pretreatments for the extraction of polyphenols and proteins from vine shoots. Food Research International, 65, 462-468.
Yankov, А. (1991). Теchnology of wine production. Sofia, Zemizdat, 355 p. (Bg).
Ziółkowska, A., Wąsowicz, E., & Jeleń, H. H. (2016). Differentiation of wines according to grape variety and geographical origin based on volatiles profiling using SPME-MS and SPME-GC/MS methods. Food Chemistry, 213, 714-720.
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