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Grouping of Bulgarian wines according to grape variety by using statistical methods

机译:通过使用统计方法,根据葡萄品种分组保加利亚葡萄酒

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68 different types of Bulgarian wines were studied in accordance with 9 optical parameters as follows: color parameters in XYZ and SIE Lab color systems, lightness, Hue angle, chroma, fluorescence intensity and emission wavelength. The main objective of this research is using hierarchical cluster analysis to evaluate the similarity and the distance between examined different types of Bulgarian wines and their grouping based on physical parameters. We have found that wines are grouped in clusters on the base of the degree of identity between them. There are two main clusters each one with two subclusters. The first one contains white wines and Sira, the second contains red wines and rose. The results from cluster analysis are presented graphically by a dendrogram. The other statistical technique used is factor analysis performed by the Method of Principal Components (PCA). The aim is to reduce the large number of variables to a few factors by grouping the correlated variables into one factor and subdividing the noncorrelated variables into different factors. Moreover the factor analysis provided the possibility to determine the parameters with the greatest influence over the distribution of samples in different clusters. In our study after the rotation of the factors with Varimax method the parameters were combined into two factors, which explain about 80 % of the total variation. The first one explains the 61.49% and correlates with color characteristics, the second one explains 18.34% from the variation and correlates with the parameters connected with fluorescence spectroscopy.
机译:根据9个光学参数研究了68种不同类型的保加利亚葡萄酒如下:XYZ和SIE LAB彩色系统中的颜色参数,亮度,色调角,色度,荧光强度和发射波长。本研究的主要目标是使用分层集群分析来评估基于物理参数的检查不同类型保加利亚葡萄酒的相似性和距离。我们发现葡萄酒被分组为它们之间的身份程度的群集。每个主要集群有两个带有两个子轮级的。第一个包含白色葡萄酒和Sira,第二个包含红葡萄酒和玫瑰。集群分析结果由树木图以图形方式呈现。使用的其他统计技术是通过主成分(PCA)的方法进行的因子分析。目的是通过将相关变量分组为一个因素并将非相关变量分组到不同因素中,将大量变量减少到几个因素。此外,因子分析提供了确定对不同簇中样品分布的最大影响的可能性。在我们的研究中,使用varimax方法旋转的因素后,参数组合成两个因素,这解释了总变化的约80%。第一个解释了61.49%并与颜色特性相关,第二个一个解释从变异的18.34%,与与荧光光谱相关的参数相关。

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