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Chromatographic unsupervised classification of olive and non-olive oil samples with the aid of Graph Theory

Abstract

Graph theory is a tool originated from the 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. The two nodes are similar if they have smaller the edge weight. In the present work, the analytical potential of the graph theory and its ability to capture the heterogeneity present in the data sets are explored by analysing the high performance liquid chromatography (HPLC) data sets of 118 samples belonging to the class of olive and non-olive oils. The graph theory based model clearly discriminated the oil samples belonging to different class. The obtained results show that graph theory can be used to achieve the unsupervised classification of the samples. The present work suggest the graph theory should be considered as an useful analytical approach for analysing the data acquired for samples belonging to environmental, clinical, pharmaceutical fields.

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Publication details

The article was received on 27 Jul 2017, accepted on 12 Oct 2017 and first published on 13 Oct 2017


Article type: Paper
DOI: 10.1039/C7AY01828B
Citation: Anal. Methods, 2017, Accepted Manuscript
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    Chromatographic unsupervised classification of olive and non-olive oil samples with the aid of Graph Theory

    K. Kumar, Anal. Methods, 2017, Accepted Manuscript , DOI: 10.1039/C7AY01828B

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