Publications

American Standards for Testing and Materials method (ASTM5739-00) and Nordtest methodology, as the two major approaches for identifying the source of spilled oils using gas chromatography-mass spectrometry (GC-MS) data, are critically compared and a new method based on multi-way parallel factor analysis (PARAFAC2) is proposed. The new approach exploits both ASTM and Nordtest methodologies by using the entire extracted ion chromatogram (EIC) and taking into account the concentration diversities of different compound classes, respectively. A multi-way data preprocessing is proposed to preserve the diagnostic properties of the original GC-MS data, which are destroyed in the ASTM method by normalizing the EICs individually. Petroleum oils, in particular diesel oils, that are difficult to classify using current methods are shown to be excellent candidates for PARAFAC2 in which EIC matrices of different sizes can be analyzed simultaneously. A diesel oil sample from an oil spill and seven very similar suspect diesel source oils, which had undergone controlled weathering for 2-15 days, were compared by this method. 79% of pairwise group comparisons were separated, in contrast to the method in which EICs were each normalized to 100, which gave 32% separation of the comparisons.

The application of multi-way parallel factor analysis (PARAFAC2) is described for the classification of different kinds of petroleum oils using GC-MS. Oils were subjected to controlled weathering for 2, 7 and 15 days and PARAFAC2 was applied to the three-way GC-MS data set (MSxGCxsample). The classification patterns visualized in scores plots and it was shown that fitting multi-way PARAFAC2 model to the natural three-way structure of GC-MS data can lead to the successful classification of weathered oils. The shift of chromatographic peaks was tackled using the specific structure of the PARAFAC2 model. A new preprocessing of spectra followed by a novel use of analysis of variance (ANOVA)-least significant difference (LSD) variable selection method were proposed as a supervised pattern recognition tool to improve classification among the highly similar diesel oils. This lead to the identification of diagnostic compounds in the studied diesel oil samples.

The simultaneous determination of Cu(2+), Cd(2+) and Pb(2+) is demonstrated at four modified gold electrodes using N-PLS calibration. Three of the electrodes were modified with the peptides Gly-Gly-His, gamma-Glu-Cys Gly and human angiotensin I which were covalently attached to thioctic acid self-assembled monolayers and the fourth electrode was modified with thioctic acid only. Voltammetry at the modified electrodes in the presence of the three metal ions revealed one peak due to the reduction of copper and another due to the overlapping peaks of cadmium and lead which made quantification using conventional methods difficult. N-PLS was used to calibrate and predict trace concentrations (100 nM to 10 microM) of mixtures of Cu(2+), Cd(2+) and Pb(2+).