Chemical Fingerprinting of Petroleum Biomarkers Using Time Warping and PCA

Abstract

A new method for chemical fingerprinting of petroleum biomakers is described. The method consists of GC-MS analysis, preprocessing of GC-MS chromatograms, and principal component analysis (PCA) of selected regions. The preprocessing consists of baseline removal by derivatization, normalization, and alignment using correlation optimized warping. The method was applied to chromatograms of m/z 217 (tricyclic and tetracyclic steranes) of oil spill samples and source oils. Oil spill samples collected from the coastal environment in the weeks after the Baltic Carrier oil spill were clustered in principal components 1 to 4 with oil samples from the tank of the Baltic Carrier (source oil). The discriminative power of PCA was enhanced by deselecting the most uncertain variables or scaling them according to their uncertainty, using a weighted least squares criterion. The four principal components were interpreted as follows: boiling point range (PC1), clay content (PC2), carbon number distribution of sterols in the source rock (PC3), and thermal maturity of the oil (PC4). In summary, the method allows for analyses of chromatograms using a fast and objective procedure and with more comprehensive data usage compared to other fingerprinting methods.

Exploratory Analysis of Biodiesel by Combining Comprehensive Two-Dimensional Gas Chromatography and Multiway Principal Component Analysis

“…GCImage (Zoex, Houston, USA) software was used for identification and to generate the figures. The data set were folded and aligned by using correlation optimized warping (COW), 19,20 and MPCA was performed using PLS Toolbox 4.0 (Eigenvector Technologies, Manson, EUA) running on MatLab 7.8 environment (MathWorks, Natick-MA, USA).…”

“…19,20 MPCA was used with comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometer (GC×GC-TOF-MS) in order to identify the differences in chemical composition between three different species of plants and two types of extracts, providing a direct relation between the different metabolite concentrations. 21 Ventura et al 17 used MPCA on large numbers of specific chemical components resolved with GC×GC-FID to determine the molecular relation of eight different maltene fractions of crude oils and performed a classification between the samples.…”

La cromatografía de gases acoplada a espectrometría de masas como herramienta de alta selectividad para caracterizar fósiles químicos en el petróleo

“…Christensen y colaboradores [15] utilizaron el método de análisis de componentes principales (PCA) para detectar grupos de crudos similares a partir de datos cromatográficos pre-procesados. Los fragmentogramas de masas para el ion en m/z 217 (que selecciona las señales de esteranos tri-y tetracíclicos), fueron los datos originales y su procesamiento previo consistió en alinear los cromatogramas para corregir los desplazamientos de los tiempos de retención, causados por diferencias instrumentales.…”

Automated alignment of chromatographic data

“…reference chromatogram) is often made from a priori knowledge on the data set. This could, for example, be the chromatogram in the middle of the run sequence [2,10] or the chromatogram containing the highest number of common chemical constituents (i.e. peaks) [3][4].…”