Wine comprises a beloved food and human companion since the early times of humans on earth. In this study, wine samples of different type (red, white, and rosé) and variety (Agiorgitiko, Augoustiatis, Cabernet Sauvignon, Syrah, Vlahiko, Assyrtiko, Chardonnay, Debina, Moschofilero, Vidiano, Syrah plus Mandilari, and Xinomavro) were subjected to physico-chemical and aroma compounds analyses, in an effort to characterize their identity and discriminate these samples according to variety using statistics. Results showed significant differences (p < 0.05) for wine samples of different variety in regard to the measured physico-chemical parameters (pH, electrical conductivity, total dissolved solids, salinity, L*, a*, b*, and Chroma*) and aroma compounds (alcohols, esters, phenolic compounds, pyran compounds, and terpenoids/norisoprenoids). Application of multivariate analysis of variance, linear discriminant analysis, and weighted least-squares regression analysis fired up the perfect varietal discrimination (~ 100%) of wine samples and modeling of results, contributing to new information in the literature about the identity of these wine varieties.

自从地球上的人类早期以来，葡萄酒就已成为人们喜爱的食物和人类伴侣。在这项研究中，对不同类型（红，白和桃红）和品种（Agiorgitiko，Augoustiatis，Cabernet Sauvignon，Syrah，Vlahiko，Assyrtiko，Chardonnay，Debina，Moschofilero，Vidiano，Syrah plus Mandilari和Xinomavro）的葡萄酒样品进行了研究。进行理化和香气成分分析，以鉴定其身份并使用统计数据根据品种区分这些样品。结果表明 ，就测得的理化参数（pH，电导率，总溶解固体，盐度，L *，a *，b *和）而言，不同品种的葡萄酒样品存在显着差异（p <0.05）色度*）和香气化合物（酒精，酯，酚类化合物，吡喃化合物和萜类/去甲类类异戊二烯）。多元方差分析，线性判别分析和加权最小二乘回归分析的应用激发了葡萄酒样品的完美品种区分（〜100％）和结果建模，为文献中有关这些葡萄酒身份的信息提供了新信息品种。