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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
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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. | ||
| Appears in Collections |
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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