Comprehensive multidimensional separations for the analysis of petroleum

Retention Time Trajectory Matching for Peak Identification in Chromatographic Analysis

Retention time drift caused by fluctuations in physical factors such as temperature ramping rate and carrier gas flow rate is ubiquitous in chromatographic measurements. Proper peak matching and identification across different chromatograms is critical prior to any subsequent analysis but is challenging without using mass spectrometry. The purpose of this work was to describe and validate a peak matching and identification method called retention time trajectory (RTT) matching that can be used in targeted analyses free of mass spectrometry. This method uses chromatographic retention times as the only input and identifies peaks associated with any subset of a predefined set of target compounds. An RTT is a two-dimensional (2D) curve formed uniquely by the retention times of the chromatographic peaks. The RTTs obtained from the chromatogram of a sample under test and those pre-installed in a library are matched and statistically compared. The best matched pair implies identification. Unlike most existing peak-alignment methods, no mathematical warping or transformation is involved. Based on the experimentally characterized RTT, an RTT hybridization method was also developed to rapidly generate more RTTs and expand the library without performing actual time-consuming chromatographic measurements, which enables successful peak matching even for chromatograms with severe retention time drifts. Additionally, 3.15 × 10⁵ tests using experimentally obtained gas chromatograms and 2 × 10¹² tests using two publicly available fruit metabolomics datasets validated the proposed method, demonstrating real-time peak/interferent identification.

Extended characterization of petroleum aromatics using off-line LC-GC-MS

Characterization of crude oil remains a challenge for analytical chemists. With the development of multi-dimensional chromatography and high-resolution mass spectrometry, an impressive number of compounds can be identified in a single sample. However, the large diversity in structure and abundance makes it difficult to obtain full compound coverage. Sample preparation methods such as solid-phase extraction and SARA-type separations are used to fractionate oil into compound classes. However, the molecular diversity within each fraction is still highly complex. Thus, in the routine analysis, only a small part of the chemical space is typically characterized. Obtaining a more detailed composition of crude oil is important for production, processing and environmental aspects. We have developed a high-resolution fractionation method for isolation and preconcentration of trace aromatics, including oxygenated and nitrogen-containing species. The method is based on semi-preparative liquid chromatography. This yields high selectivity and efficiency with separation based on aromaticity, ring size and connectivity. By the separation of the more abundant aromatics, i.e., monoaromatics and naphthalenes, trace species were isolated and enriched. This enabled the identification of features not detectable by routine methods. We demonstrate the applicability by fractionation and subsequent GC-MS analysis of 14 crude oils sourced from the North Sea. The number of tentatively identified compounds increased by approximately 60 to 150% compared to solid-phase extraction and GC × GC-MS. Furthermore, the method was used to successfully identify an extended set of heteroatom-containing aromatics (e.g., amines, ketones). The method is not intended to replace traditional sample preparation techniques or multi-dimensional chromatography but acts as a complementary tool. An in-depth comparison to routine characterization techniques is presented concerning advantages and disadvantages.

Determination of vacuum ultraviolet detector response factors by hyphenation with two-dimensional comprehensive gas chromatography with flame ionization detection

Two-dimensional comprehensive gas chromatography is an established technique, employed for the characterization of complex samples. Broadband vacuum ultraviolet absorption spectroscopy detection has recently attracted a lot of attention as it is a universal detection technique characterized by good selectivity but also ease of use and amenability to coupling with two-dimensional comprehensive gas chromatography. Vacuum ultraviolet spectroscopy is particularly interesting due to the possibility of performing spectral decomposition for species that coelute in gas chromatography analysis. This detector has quantitative capabilities, however not all species absorb vacuum ultraviolet radiation the same. Unfortunately, vacuum ultraviolet relative response factors for compounds are not always available. Methods to rapidly measure vacuum ultraviolet relative response factors and generate a large database that would allow calibration free quantitative analysis of complex mixtures are therefore of great interest. In this work, a universal methodology that permits rapid measurement of vacuum ultraviolet relative response factors is reported. It involves flow modulated two-dimensional comprehensive gas chromatography with dual vacuum ultraviolet and flame ionization detection. In this set-up, flame ionization detection is employed as a quantitative reference allowing to scale vacuum ultraviolet responses of investigated compounds. This approach was validated by flow measurements and by comparing relative response factors obtained for model compounds with literature data.

Comparison of Thermal and Flow-Based Modulation in Comprehensive Two-Dimensional Gas Chromatography—Time-of-Flight Mass Spectrometry (GC × GC-TOFMS) for the Analysis of Base Oils

Base oils are produced by refining crude oil or through chemical synthesis. They are a key component of engine oils. With an immense range of carbon numbers and boiling points, analyzing such complex mixtures is very difficult. The need to monitor industrial petroleum processing steps, as well as to identify petrochemical environmental pollutants, drives the search for improved characterization methods. Comprehensive two-dimensional gas chromatography (GC × GC) is one of the best tools for that. The modulator used in GC × GC is responsible for trapping/sampling the first dimension (1D) column analytes, then reinjecting them in the form of narrow bands onto the second dimension (2D) column for further separation. Modulators used today generally fall into two categories, thermal and flow ones. Heater-based thermal modulators trap the 1D column effluent at or above ambient temperatures. Flow-based modulators utilize storage loop(s) to collect the 1D effluent, which is subsequently flushed into the second-dimension column for further separation. A single-stage, consumable-free thermal modulator and a reverse fill/flush flow modulator were compared for the characterization of base oils. Both were evaluated on their ability to achieve separation of several conventional and synthetic engine oils components. A reverse column set, polar 1D and nonpolar 2D, allowed group-type analysis of all classes, including linear, branched, and aromatic species. The results show the ability to achieve a comprehensive separation of specific compound classes and the differentiation of engine oil types and manufacturers. Soft ionization assisted in tentative identification of two alkylated diphenylamines in each sample. The advantages and limitations of both thermal and flow modulation are presented.

Development of retention time indices for comprehensive multidimensional gas chromatography and application to ignitable liquid residue mapping in wildfire investigations

In this study, we introduce a simple three-step workflow for a universally applicable RI system, to be used in GC×GC analysis of ignitable liquid residue (ILR) for arson investigations. The proposed RI system applies a combination of two well-established GC RI systems: non-isothermal Kovats (K) index in the first dimension and Lee (L) index in the second dimension. The proposed KLI RI system showed very good correlations when compared with predicted values and existing RI systems (r² = 0.97 in first dimension, r² = 0.99 in second dimension) and was valid for a wide range of analyte concentrations and operational settings (coefficient of variance (CV) < 1% in first dimension, < 10% in second dimension). Using the KLI RI, an ILR classification contour map was created to assist with the identification of ILR types within ASTM E1618. The contour map was successfully applied to neat fuels and a fire scene sample, highlighting the application to wildfire investigation. Standardizing the RI process and establishing acceptable error margins allows the exploration and comparison of comprehensive data generated from GC×GC analysis of ILRs regardless of location, time, or system, further enhancing comprehensive and tenable chemometric analyses of samples. Overall, the KLI workflow was inexpensive, quick to apply, and user-friendly with its simple 3-step design.

Gas chromatography vacuum ultraviolet spectroscopy: A review

Accelerated technological progress and increased complexity of interrogated matrices imposes a demand for fast, powerful, and resolutive analysis techniques. Gas chromatography has been for a long time a 'go-to' technique for the analysis of mixtures of volatile and semi-volatile compounds. Coupling of the several dimensions of gas chromatography separation has allowed to access a realm of improved separations in the terms of increased separation power and detection sensitivity. Especially comprehensive separations offer an insight into detailed sample composition for complex samples. Combining these advanced separation techniques with an informative detection system such as vacuum ultraviolet spectroscopy is therefore of great interest. Almost all molecules absorb the vacuum ultraviolet radiation and have distinct spectral features with compound classes exhibiting spectral signature similarities. Spectral information can be 'filtered' to extract the response in the most informative spectral ranges. Developed algorithms allow spectral mixture estimation of coeluting species. Vacuum ultraviolet detector follows Beer-Lambert law, with the possibility of calibrationless quantitation. The purpose of this article is to provide an overview of the features and specificities of gas chromatography-vacuum ultraviolet spectroscopy coupling which has gained interest since the recent introduction of a commercial vacuum ultraviolet detector. Potentials and limitations, relevant theoretical considerations, recent advances and applications are explored.

"-Omics" workflow for paleolimnological and geological archives: A review

"-Omics" is a powerful screening method with applications in molecular biology, toxicology, wildlife biology, natural product discovery, and many other fields. Genomics, proteomics, metabolomics, and lipidomics are common examples included under the "-omics" umbrella. This screening method uses combinations of untargeted, semi-targeted, and targeted analyses paired with data mining to facilitate researchers' understanding of the genome, proteins, and small organic molecules in biological systems. Recently, however, the use of "-omics" has expanded into the fields of geology, specifically petrology, and paleolimnology. Specifically, untargeted analyses stand to transform these fields as petroleomics, and sediment-"omics" become more prevalent. "-Omics" facilitates the visualization of small molecule profiles from environmental matrices (i.e. oil and sediment). Small molecule profiles can provide improved understanding of small molecules distributions throughout the environment, and how those compositions can change depending on conditions (i.e. climate change, weathering, etc.). "-Omics" also facilities discovery of next-generation biomarkers that can be used for oil source identification and as proxies for reconstructing past environmental changes. Untargeted analyses paired with data mining and multivariate statistical analyses represents a powerful suite of tools for hypothesis generation, and new method development for environmental reconstructions. Here we present an introduction to "-omics" methodology, technical terms, and examples of applications to paleolimnology and petrology. The purpose of this review is to highlight the important considerations at each step in the "-omics" workflow to produce high quality and statistically powerful data for petrological and paleolimnological applications.

Unsupervised classification of petroleum Certified Reference Materials and other fuels by chemometric analysis of gas chromatography-mass spectrometry data

As feedstocks transition from conventional oil to unconventional petroleum sources and biomass, it will be necessary to determine whether a particular fuel or fuel blend is suitable for use in engines. Certifying a fuel as safe for use is time-consuming and expensive and must be performed for each new fuel. In principle, suitability of a fuel should be completely determined by its chemical composition. This composition can be probed through use of detailed analytical techniques such as gas chromatography-mass spectroscopy (GC-MS). In traditional analysis, chromatograms would be used to determine the details of the composition. In the approach taken in this paper, the chromatogram is assumed to be entirely representative of the composition of a fuel, and is used directly as the input to an algorithm in order to develop a model that is predictive of a fuel's suitability. When a new fuel is proposed for service, its suitability for any application could then be ascertained by using this model to compare its chromatogram with those of the fuels already known to be suitable for that application. In this paper, we lay the mathematical and informatics groundwork for a predictive model of hydrocarbon properties. The objective of this work was to develop a reliable model for unsupervised classification of the hydrocarbons as a prelude to developing a predictive model of their engine-relevant physical and chemical properties. A set of hydrocarbons including biodiesel fuels, gasoline, highway and marine diesel fuels, and crude oils was collected and GC-MS profiles obtained. These profiles were then analyzed using multi-way principal components analysis (MPCA), principal factors analysis (PARAFAC), and a self-organizing map (SOM), which is a kind of artificial neural network. It was found that, while MPCA and PARAFAC were able to recover descriptive models of the fuels, their linear nature obscured some of the finer physical details due to the widely varying composition of the fuels. The SOM was able to find a descriptive classification model which has the potential for practical recognition and perhaps prediction of fuel properties.

Selective mass spectrometric analysis of thiols using charge-tagged disulfides

A simple chemical derivatization technique was developed for electrospray ionization mass spectrometry (ESI-MS) in which thiols and disulfides may be selectively analyzed in a complex matrix and easily characterized. These reagents enhance detection of thiols and disulfides solely due to the nature of the charge-tag derivatization agent and therefore does not require an isotopically labelled substrate. The charged disulfides readily and exclusively react with thiols in a complex matrix in a short amount of time. Furthermore, the synthesis of these reagents is simple and results in a highly pure and stable reagent. The efficacy of this reaction was demonstrated using on-line monitoring, while the scope and usefulness of the reaction was demonstrated in petroleum fractions.

An overview of multidimensional liquid phase separations in food analysis

Food safety is a priority public health concern that demands analytical methods capable to detect low concentration level of contaminants (e.g. pesticides and antibiotics) in different food matrices. Due to the high complexity of these matrices, a sample preparation step is in most cases mandatory to achieve satisfactory results being usually tedious, lengthy, and prone to the introduction of errors. For this reason, many research groups have focused efforts on the development of online systems capable to do the cleanup, concentration, and separation steps at once through multidimensional separation techniques (MDS). Among several possible setups, the most popular are the multidimensional chromatographic techniques (MDC) that consist in combining more than one mobile and/or stationary phase to provide a satisfactory separation. In the present review, we selected a variety of multidimensional separation systems used for food contaminant analysis in order to discuss the instrumentation aspects, the concept of orthogonality, column approaches used in these systems, and new materials that can be used in these columns. Selected classes of contaminants present in food matrices are introduced and discussed as example of the potential applications of multidimensional liquid phase separation techniques in food safety.

In vitro volatile organic compound profiling using GC×GC-TOFMS to differentiate bacteria associated with lung infections: a proof-of-concept study

Chronic pulmonary infections are the principal cause of morbidity and mortality in individuals with cystic fibrosis (CF). Due to the polymicrobial nature of these infections, the identification of the particular bacterial species responsible is an essential step in diagnosis and treatment. Current diagnostic procedures are time-consuming, and can also be expensive, invasive and unpleasant in the absence of spontaneously expectorated sputum. The development of a rapid, non-invasive methodology capable of diagnosing and monitoring early bacterial infection is desired. Future visions of real-time, in situ diagnosis via exhaled breath testing rely on the differentiation of bacteria based on their volatile metabolites. The objective of this proof-of-concept study was to investigate whether a range of CF-associated bacterial species (i.e. Pseudomonas aeruginosa, Burkholderia cenocepacia, Haemophilus influenzae, Stenotrophomonas maltophilia, Streptococcus pneumoniae and Streptococcus milleri) could be differentiated based on their in vitro volatile metabolomic profiles. Headspace samples were collected using solid phase microextraction (SPME), analyzed using comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOFMS) and evaluated using principal component analysis (PCA) in order to assess the multivariate structure of the data. Although it was not possible to effectively differentiate all six bacteria using this method, the results revealed that the presence of a particular pattern of VOCs (rather than a single VOC biomarker) is necessary for bacterial species identification. The particular pattern of VOCs was found to be dependent upon the bacterial growth phase (e.g. logarithmic versus stationary) and sample storage conditions (e.g. short-term versus long-term storage at  -18 °C). Future studies of CF-associated bacteria and exhaled breath condensate will benefit from the approaches presented in this study and further facilitate the production of diagnostic tools for the early detection of bacterial lung infections.

Measurement of H2S in Crude Oil and Crude Oil Headspace Using Multidimensional Gas Chromatography, Deans Switching and Sulfur-selective Detection

A method for the analysis of dissolved hydrogen sulfide in crude oil samples is demonstrated using gas chromatography. In order to effectively eliminate interferences, a two dimensional column configuration is used, with a Deans switch employed to transfer hydrogen sulfide from the first to the second column (heart-cutting). Liquid crude samples are first separated on a dimethylpolysiloxane column, and light gases are heart-cut and further separated on a bonded porous layer open tubular (PLOT) column that is able to separate hydrogen sulfide from other light sulfur species. Hydrogen sulfide is then detected with a sulfur chemiluminescence detector, adding an additional layer of selectivity. Following separation and detection of hydrogen sulfide, the system is backflushed to remove the high-boiling hydrocarbons present in the crude samples and to preserve chromatographic integrity. Dissolved hydrogen sulfide has been quantified in liquid samples from 1.1 to 500 ppm, demonstrating wide applicability to a range of samples. The method has also been successfully applied for the analysis of gas samples from crude oil headspace and process gas bags, with measurement from 0.7 to 9,700 ppm hydrogen sulfide.

Improving the quality of biomarker candidates in untargeted metabolomics via peak table-based alignment of comprehensive two-dimensional gas chromatography-mass spectrometry data

The potential of high-resolution analytical technologies like GC×GC/TOF MS in untargeted metabolomics and biomarker discovery has been limited by the development of fully automated software that can efficiently align and extract information from multiple chromatographic data sets. In this work we report the first investigation on a peak-by-peak basis of the chromatographic factors that impact GC×GC data alignment. A representative set of 16 compounds of different chromatographic characteristics were followed through the alignment of 63 GC×GC chromatograms. We found that varying the mass spectral match parameter had a significant influence on the alignment for poorly-resolved peaks, especially those at the extremes of the detector linear range, and no influence on well-chromatographed peaks. Therefore, optimized chromatography is required for proper GC×GC data alignment. Based on these observations, a workflow is presented for the conservative selection of biomarker candidates from untargeted metabolomics analyses.

Preparation of multiwalled carbon nanotubes/hydroxyl-terminated silicone oil fiber and its application to analysis of crude oils

A simple and efficient method to analyze the volatile and semivolatile organic compounds in crude oils has been developed based on direct immersion solid-phase microextraction coupled to comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry (DI-SPME-GC × GC/TOFMS). A novel fiber, multiwalled carbon nanotubes/hydroxyl-terminated silicone oil (MWNTs-TSO-OH), was prepared by sol-gel technology. Using standard solutions, the extraction conditions were optimized such as extraction mode, extraction temperature, extraction time, and salts effect. With the optimized conditions, a real crude oil sample was extracted and then analyzed in detail. It shows that the proposed method is very effective in simultaneously analyzing the normal and branched alkanes, cycloalkanes, aromatic hydrocarbons, and biomarkers of crude oil such as steranes and terpanes. Furthermore, the method showed good linearity (r > 0.999), precision (RSD < 8%), and detection limits ranging from 0.2 to 1.6 ng/L.