Geographical classification of crude oils by Kohonen self-organizing maps

In the analysis of an environmental disaster caused by spillage of crude oil. limitation of the possible sources to a few geographical origins can help in the identification of the polluting vessel from a group of potential candidates. In this paper we show that Kohonen self-organizing maps (or Kohonen neural networks) can classify samples of crude oils on the basis of gas chromtography-mass spectrometry (GC-MS) descriptors, in terms of geographical origin, with a high degree of accuracy. Two data sets were investigated - one from Instituto Hidrografico (Lisbon, Portugal) with 188 samples from 20 geographical origins, and another from EUROCRUDE (TM) with 374 samples. After training the Kohonen self-organizing maps with a training set, predictions were obtained for an independent test set, Correct predictions were obtained for 70% and 60% of the test sets for the two studies, respectively. Ensembles of networks were highly interesting for the calculation of a prediction score, which can be used as a measure of the reliability of the prediction. For the samples with high prediction scores, the percentage of correct predictions jumped to 94-96%. The ability of the maps to identify a given origin is very much dependent on the availability of samples from that class in the training set. Equally good predictions were obtained for a small test set of weathered samples. This investigation adds value to the GC-MS descriptors already in use for practical analytical work. suggesting new ways to ferret out useful knowledge from them. (c) 2005 Elsevier B.V. All rights reserved.