Spectral Pixel-Based Analysis and Clustering Workflow for the Exploration of the Composition of Gas Oil Samples by Comprehensive Two-dimensional Gas Chromatography Vacuum Ultraviolet Spectroscopy.

Obtaining an insight into the composition of fuel and process intermediates samples is important for monitoring of the product quality, production and exploitation process improvement and complying with industry and environmental regulations. A broadband vacuum ultraviolet (VUV) spectroscopy detector has been successfully hyphenated with both GC and comprehensive two-dimensional gas chromatography (GC×GC) for the analysis of various fuel samples. This detector possesses both qualitative and quantitative capabilities. Most compounds absorb in the vacuum ultraviolet spectral range, and they exhibit rich and distinctive spectral features. Main constituents of fuel samples, hydrocarbons, such as paraffins, olefins, naphthenes, aromatics are identified with the VUV with very good selectivity. As data obtained from the GC×GC-VUV analysis of fuels can be challenging to exploit due to the sample complexity, it can be useful to employ chemometric methods for fuel composition exploration in a fast and efficient manner. In this work, 14 gas oil samples were analysed by GC×GC-VUV and after suitable data preprocessing, a pixel-based approach combined with k-means clustering was applied in order to classify compounds' spectra into major hydrocarbon families in each sample and obtain a semi-quantification result which was then compared with the results of mass spectrometry analysis and quantitative GC×GC-VUV that involved application of detailed identification templates and response factors. It was demonstrated that due to hydrocarbon families exhibiting similar VUV absorbance spectra, the proposed workflow for GC×GC-VUV data preprocessing and analysis can be a fast and efficient way for gas oils global composition exploration.