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Classification of gasoline samples using variable reduction and expectation-maximization methods

Pasadakis Nikos, Andreas Kardamakis

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URI: http://purl.tuc.gr/dl/dias/65257598-A57F-4B13-BA19-A008C433388D
Year 2006
Type of Item Conference Full Paper
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Bibliographic Citation Nikos Pasadakis and Andreas Kardamakis, “Classification of gasoline samples using variable reduction and expectation-maximization methods”, in International Conference of Computational Methods in Sciences and Engineering, Brill Academic Publishers, 2006, pp.435-437.
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Summary

Gasoline classification is an important issue in environmental and forensic applications. Several categorization algorithms exist that attempt to correctly classify gasoline samples in data sets. We demonstrate a method that can improve classification performance by maximizing hit-rate without using a priori knowledge of compounds in gasoline samples. This is accomplished by using a variable reduction technique that de-clutters the data set from redundant information by minimizing multivariate structural distortion and by applying a greedy Expectation-Maximization (EM) algorithm that optimally tunes parameters of a Gaussian mixture model (GMM). These methods initially classify premium and regular gasoline samples into clusters relying on their gas chromatography-mass spectroscopy (GC-MS) spectral data and then they discriminate them into their winter and summer subgroups. Approximately 89% of the samples were correctly classified as premium or regular gasoline and 98.8% of the samples were correctly classified according to their seasonal characteristics.

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