Graph theory is a tool originating from discrete mathematics. A graph essentially consists of two fundamental units, nodes and edges. The nodes represent the samples and edges describe their connections. The edges are usually weighted with a dissimilarity value. Two nodes are similar if they have a smaller edge weight. In the present work, the analytical potential of graph theory and its ability to capture the heterogeneity present in the datasets are explored by analysing the high performance liquid chromatography (HPLC) datasets of 118 samples belonging to the classes of olive and non-olive oils. The graph theory based model clearly discriminated between the oil samples belonging to different classes. The obtained results show that graph theory can be used to achieve unsupervised classification of the samples. The present work suggests that graph theory should be considered as a useful analytical approach for analysing the data acquired for samples belonging to environmental, clinical, and pharmaceutical fields.

中文翻译：

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借助图论，对橄榄油和非橄榄油样品进行色谱无监督分类

图论是源自离散数学的工具。图基本上由两个基本单元，节点和边组成。节点代表样本，边缘描述它们的连接。通常用不相似值对边缘加权。如果两个节点的边缘权重较小，则它们是相似的。在本工作中，通过分析属于橄榄油和非橄榄油类的118个样品的高效液相色谱（HPLC）数据集，探索了图论的分析潜力及其捕获数据集中存在的异质性的能力。基于图论的模型清楚地区分了属于不同类别的油样。获得的结果表明，图论可用于实现样本的无监督分类。