A unique data analysis framework and open source benchmark data set for the analysis of comprehensive two-dimensional gas chromatography software.

Comprehensive two-dimensional gas chromatography (GC × GC) is amongst the most powerful separation technologies currently existing. Since its advent in early 1990, it has become an established method which is readily available. However, one of its most challenging aspects, especially in hyphenation with mass spectrometry is the high amount of chemical information it provides for each measurement. The GC × GC community agrees that there, the highest demand for action is found. In response, the number of software packages allowing for in-depth data processing of GC × GC data has risen over the last couple of years. These packages provide sophisticated tools and algorithms allowing for more streamlined data evaluation. However, these tools/algorithms and their respective specific functionalities differ drastically within the available software packages and might result in various levels of findings if not appropriately implemented by the end users. This study focuses on two main objectives. First, to propose a data analysis framework and second to propose an open-source dataset for benchmarking software options and their specificities. Thus, allowing for an unanimous and comprehensive evaluation of GC × GC software. Thereby, the benchmark data includes a set of standard compound measurements and a set of chocolate aroma profiles. On this foundation, eight readily available GC × GC software packages were anonymously investigated for fundamental and advanced functionalities such as retention and detection device derived parameters, revealing differences in the determination of e.g. retention times and mass spectra.

Rapid Screening of Complex Matrices: Utilizing Kendrick Mass Defect To Enhance Knowledge-Based Group Type Evaluation of Multidimensional Gas Chromatography-High-Resolution Time-of-Flight Mass Spectrometry Data.

Organic compound characterization of highly complex matrices involves scientific challenges, such as the diversity of "true" unknowns, the concentration ranges of various compound classes, and limited available amounts of sample. Therefore, discovery-based multidimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry (GC×GC-HRToFMS) is increasingly applied. Nevertheless, most studies focus on target analysis and tend to disregard important details of the sample composition. The increased peak or separation capacity of GC×GC-ToFMS allows for in-depth chemical analysis of the molecular composition. However, high amounts of data, containing several thousands of compounds per experiment, are generally acquired during such analyses. Coupling GC×GC to high-resolution mass spectrometry further increases the amount of data and therefore requires advanced data reduction and mining techniques. Commonly, the main approach for the evaluation of GC×GC-HRToFMS data sets either focuses on the chromatographic separation (e.g., group type analysis), or utilizes exact mass data applying Kendrick mass defect analysis or van Krevelen plots. The presented approach integrates the accurate mass data and the chromatographic information by combining Kendrick mass defect information and knowledge-based rules. This combination allows for fast, visual data screening as well as quantitative estimation of the sample's composition. Moreover, the resulting sample classification significantly reduces the number of variables, allowing distinct chemometric analysis in nontargeted studies, such as detailed hydrocarbon analyses and environmental and forensic investigations.

Elucidating Environmental Fingerprinting Mechanisms of Unconventional Gas Development through Hydrocarbon Analysis.

Hydraulic fracturing is an increasingly common technique for the extraction of natural gas entrapped in shale formations. This technique has been highly criticized due to the possibility of environmental contamination, underscoring the need for method development to identify chemical factors that could be utilized in point-source identification of environmental contamination events. Here, we utilize comprehensive two-dimensional gas chromatography (GC × GC) coupled to high-resolution time-of-flight (HRT) mass spectrometry, which offers a unique instrumental combination allowing for petroleomics hydrocarbon fingerprinting. Four flowback fluids from Marcellus shale gas wells in geographic proximity were analyzed for differentiating factors that could be exploited in environmental forensics investigations of shale gas impacts. Kendrick mass defect (KMD) plots of these flowback fluids illustrated well-to-well differences in heteroatomic substituted hydrocarbons, while GC × GC separations showed variance in cyclic hydrocarbons and polyaromatic hydrocarbons among the four wells. Additionally, generating plots that combine GC × GC separation with KMD established a novel data-rich visualization technique that further differentiated the samples.

Untargeted Identification of Wood Type-Specific Markers in Particulate Matter from Wood Combustion.

Residential wood combustion emissions are one of the major global sources of particulate and gaseous organic pollutants. However, the detailed chemical compositions of these emissions are poorly characterized due to their highly complex molecular compositions, nonideal combustion conditions, and sample preparation steps. In this study, the particulate organic emissions from a masonry heater using three types of wood logs, namely, beech, birch, and spruce, were chemically characterized using thermal desorption in situ derivatization coupled to a GCxGC-ToF/MS system. Untargeted data analyses were performed using the comprehensive measurements. Univariate and multivariate chemometric tools, such as analysis of variance (ANOVA), principal component analysis (PCA), and ANOVA simultaneous component analysis (ASCA), were used to reduce the data to highly significant and wood type-specific features. This study reveals substances not previously considered in the literature as meaningful markers for differentiation among wood types.

Advanced scripting for the automated profiling of two-dimensional gas chromatography-time-of-flight mass spectrometry data from combustion aerosol.

Multidimensional gas chromatography is an appropriate tool for the non-targeted and comprehensive characterisation of complex samples generated from combustion processes. Particulate matter (PM) emission is composed of a large number of compounds, including condensed semi-volatile organic compounds (SVOCs). However, the complex amount of information gained from such comprehensive techniques is associated with difficult and time-consuming data analysis. Because of this obstacle, two-dimensional gas chromatography still receives relatively little use in aerosol science [1-4]. To remedy this problem, advanced scripting algorithms based on knowledge-based rules (KBRs) were developed in-house and applied to GCxGC-TOFMS data. Previously reported KBRs and newer findings were considered for the development of these algorithms. The novelty of the presented advanced scripting tools is a notably selective search criterion for data screening, which is primarily based on fragmentation patterns and the presence of specific fragments. Combined with "classical" approaches based on retention times, a fast, accurate and automated data evaluation method was developed, which was evaluated qualitatively and quantitatively for type 1 and type 2 errors. The method's applicability was further tested for PM filter samples obtained from ship fuel combustion. Major substance classes, including polycyclic aromatic hydrocarbons (PAH), alkanes, benzenes, esters and ethers, can be targeted. This approach allows the classification of approximately 75% of the peaks of interest within real PM samples. Various conditions of combustion, such as fuel composition and engine load, could be clearly characterised and differentiated.