Classification of weathered petroleum oils by multi-way analysis of gas chromatography–mass spectrometry data using PARAFAC2 parallel factor analysis

Non-targeted discovery-based analysis for gas chromatography with mass spectrometry: A comparison of peak table, tile, and pixel-based Fisher ratio analysis

The ability to discover minute differences between samples or sample classes for gas chromatography coupled to mass spectrometry (GC-MS) can be a challenging endeavor, especially when those differences are not a priori. Fisher ratio (F-ratio) analysis is an apt technique to probe the differences between GC-MS chromatograms. F-ratio analysis is a supervised, non-targeted, discovery-based method that compares two different samples (or sample classes) to reduce the GC-MS dataset into a hit list composed of class distinguishing compounds. Three different F-ratio techniques, peak table, tile, and pixel-based were used to "discover" nine non-native analytes that were spiked into gasoline at four different nominal concentrations of 250, 85, 25, 5 parts-per-million (ppm). For the tile and pixel-based F-ratio calculations, a novel methodology is introduced to improve the sensitivity of the F-ratio calculations while reducing false positives. Furthermore, we use a combinatorial technique using null class comparisons, termed null distribution analysis, to determine a statistical F-ratio cutoff for analysis of the hit lists. The pixel-based algorithm was the most sensitive method and was able to "discover" all nine spiked analytes at a nominal concentration of 250 ppm albeit with one false positive interspersed towards the bottom of the hit list. The pixel-based software was also able to "discover" more of the spiked analytes at the lower concentrations with seven of the spiked analytes "discovered" at 85 ppm, four of the spiked analytes "discovered" at 25 ppm, and one analyte "discovered" at 5 ppm.

Effects of weathering process on the stable carbon isotope compositions of polycyclic aromatic hydrocarbons of fuel oils and crude oils

Two fuel oils and two crude oils were subjected to a 60-day weathering simulation experiment, and the effects of weathering on some common parameters for aromatics and aromatic δ¹³C values were studied. The results show that weathering of all oil samples affected little the DBT/P (dibenzothiophene/phenanthrene) ratio and methylphenanthrene distribution fraction. Four oil samples could be distinguished only by the DBT/P ratio. The effect of weathering on isotopes in polycyclic aromatic hydrocarbons was small. The results show that the types of four oil samples can be distinguished, while Kuwait and Russia crude oils cannot be discriminated from each other totally by double-coordinate two-dimensional maps for aromatic δ¹³C; all of the oil samples can be distinguished by principal component analysis of δ¹³C for aromatics, the relationship of DBT/P and PAHs δ¹³C values. Therefore, the δ¹³C value of aromatics can be used as an alternative index for the analysis of oil spills.

An Electronic Nose Based Method for the Discrimination of Weathered Petroleum-Derived Products

In recent years pollution due to hydrocarbon spills has increased markedly as a result of the numerous advances in technologies and industrial processes. Anthropogenic activities (accidental or illegal) are responsible for most of these incidents. In some cases, the spills are not detected at the moment they occur and the contaminants are subjected to different degradation phenomena that may change the chemical composition of the hydrocarbon over time. An incorrect or ineffective identification of the spill could lead to significant consequences, bearing in mind that most spills are hazardous to the environment. In the present work the capacity of the analytical technique based on the Electronic Nose (eNose) combined with chemometrics in the identification and discrimination of different weathered petroleum-derived products (PDPs) was studied. Different volumes (40 μL and 80 μL) of PDPs (gasoline, diesel, and paraffin) were poured onto different supports (wood, cork, paper, and cotton sheet) and subjected to a natural weathering process by evaporation for one month. The porosity of the support was also studied. The application of linear discriminant analysis allowed the full discrimination of the samples according to the presence/absence of PDP and a 97.7% of correct discrimination of the different PDPs regardless of the weathering time, support or volume used. The results show that the system is capable of detecting and discriminating the presence of petroleum-derived products in any of the situations studied.

Interpreting comprehensive two-dimensional gas chromatography using peak topography maps with application to petroleum forensics

Background

Comprehensive two-dimensional gas chromatography \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(GC \times GC)$$\end{document}(GC×GC) provides high-resolution separations across hundreds of compounds in a complex mixture, thus unlocking unprecedented information for intricate quantitative interpretation. We exploit this compound diversity across the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(GC \times GC)$$\end{document}(GC×GC) topography to provide quantitative compound-cognizant interpretation beyond target compound analysis with petroleum forensics as a practical application. We focus on the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(GC \times GC)$$\end{document}(GC×GC) topography of biomarker hydrocarbons, hopanes and steranes, as they are generally recalcitrant to weathering. We introduce peak topography maps (PTM) and topography partitioning techniques that consider a notably broader and more diverse range of target and non-target biomarker compounds compared to traditional approaches that consider approximately 20 biomarker ratios. Specifically, we consider a range of 33–154 target and non-target biomarkers with highest-to-lowest peak ratio within an injection ranging from 4.86 to 19.6 (precise numbers depend on biomarker diversity of individual injections). We also provide a robust quantitative measure for directly determining “match” between samples, without necessitating training data sets.

Results

We validate our methods across 34 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(GC \times GC)$$\end{document}(GC×GC) injections from a diverse portfolio of petroleum sources, and provide quantitative comparison of performance against established statistical methods such as principal components analysis (PCA). Our data set includes a wide range of samples collected following the 2010 DeepwaterHorizon disaster that released approximately 160 million gallons of crude oil from the Macondo well (MW). Samples that were clearly collected following this disaster exhibit statistically significant match \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(99.23 \pm 1.66 )\,\%$$\end{document}(99.23±1.66)% using PTM-based interpretation against other closely related sources. PTM-based interpretation also provides higher differentiation between closely correlated but distinct sources than obtained using PCA-based statistical comparisons. In addition to results based on this experimental field data, we also provide extentive perturbation analysis of the PTM method over numerical simulations that introduce random variability of peak locations over the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(GC \times GC)$$\end{document}(GC×GC) biomarker ROI image of the MW pre-spill sample (sample \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\#1$$\end{document}#1 in Additional file 4: Table S1). We compare the robustness of the cross-PTM score against peak location variability in both dimensions and compare the results against PCA analysis over the same set of simulated images. Detailed description of the simulation experiment and discussion of results are provided in Additional file 1: Section S8.

Conclusions

We provide a peak-cognizant informational framework for quantitative interpretation of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(GC \times GC)$$\end{document}(GC×GC) topography. Proposed topographic analysis enables \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(GC \times GC)$$\end{document}(GC×GC) forensic interpretation across target petroleum biomarkers, while including the nuances of lesser-known non-target biomarkers clustered around the target peaks. This allows potential discovery of hitherto unknown connections between target and non-target biomarkers.

Electronic supplementary material

The online version of this article (doi:10.1186/s13065-016-0211-y) contains supplementary material, which is available to authorized users.

Combining molecular fingerprints with multidimensional scaling analyses to identify the source of spilled oil from highly similar suspected oils

Oil fingerprints have been a powerful tool widely used for determining the source of spilled oil. In most cases, this tool works well. However, it is usually difficult to identify the source if the oil spill accident occurs during offshore petroleum exploration due to the highly similar physiochemical characteristics of suspected oils from the same drilling platform. In this report, a case study from the waters of the South China Sea is presented, and multidimensional scaling analysis (MDS) is introduced to demonstrate how oil fingerprints can be combined with mathematical methods to identify the source of spilled oil from highly similar suspected sources. The results suggest that the MDS calculation based on oil fingerprints and subsequently integrated with specific biomarkers in spilled oils is the most effective method with a great potential for determining the source in terms of highly similar suspected oils.

Gas chromatography with parallel hard and soft ionization mass spectrometry

RATIONALE

Mass spectrometric identification of compounds in chromatography can be obtained from molecular masses from soft ionization mass spectrometry techniques such as field ionization (FI) and fragmentation patterns from hard ionization techniques such as electron ionization (EI). Simultaneous detection by EI and FI mass spectrometry allows alignment of the different information from each method.

METHODS

We report the construction and characteristics of a combined instrument consisting of a gas chromatograph and two parallel mass spectrometry ionization sources, EI and FI. When considering both ion yield and signal-to-noise it was postulated that good-quality EI and FI mass spectra could be obtained simultaneously using a post-column splitter with a split fraction of 1:10 for EI/FI. This has been realised and we report its application for the analysis of several complex mixtures.

RESULTS

The differences between the full width at half maximum (FWHM) of the EI and FI chromatograms were statistically insignificant, and the retention times of the chromatograms were highly correlated (r(2) =0.9999) with no detectable bias. The applicability and significance of this combined instrument and the attendant methodology are illustrated by the analysis of standard samples of 13 compounds with diverse structures, and the analysis of mixtures of fatty acids, fish oil, hydrocarbons and yeast metabolites.

CONCLUSIONS

This combined dual-source instrument saves time and resources, and more importantly generates equivalent chromatograms aligned in time, in EI and FI (i.e. peaks with similar shapes and identical positions). The identical FWHMs and retention times of the EI and FI chromatograms in this combined instrument enable the accurate assignment of fragment ions from EI to their corresponding molecular ions in FI.

An iterative block-shifting approach to retention time alignment that preserves the shape and area of gas chromatography-mass spectrometry peaks

Background

Metabolomics, petroleum and biodiesel chemistry, biomarker discovery, and other fields which rely on high-resolution profiling of complex chemical mixtures generate datasets which contain millions of detector intensity readings, each uniquely addressed along dimensions of time (e.g., retention time of chemicals on a chromatographic column), a spectral value (e.g., mass-to-charge ratio of ions derived from chemicals), and the analytical run number. They also must rely on data preprocessing techniques. In particular, inter-run variance in the retention time of chemical species poses a significant hurdle that must be cleared before feature extraction, data reduction, and knowledge discovery can ensue. Alignment methods, for calibrating retention reportedly (and in our experience) can misalign matching chemicals, falsely align distinct ones, be unduly sensitive to chosen values of input parameters, and result in distortions of peak shape and area.

Results

We present an iterative block-shifting approach for retention-time calibration that detects chromatographic features and qualifies them by retention time, spectrum, and the effect of their inclusion on the quality of alignment itself. Mass chromatograms are aligned pairwise to one selected as a reference. In tests using a 45-run GC-MS experiment, block-shifting reduced the absolute deviation of retention by greater than 30-fold. It compared favourably to COW and XCMS with respect to alignment, and was markedly superior in preservation of peak area.

Conclusion

Iterative block-shifting is an attractive method to align GC-MS mass chromatograms that is also generalizable to other two-dimensional techniques such as HPLC-MS.