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Orthogonality is a key parameter in the evaluation of the performance of a 2D chromatography-based separation system. Two different perspectives on orthogonality are determined: the extent of the separation space utilized (global orthogonality) and the uniformity of the coverage of the separation space (local orthogonality). This work aims to elucidate the impact of sample dimensionality (the number of separation processes involved) on orthogonality evaluation through the use of descriptors from seven different algorithms utilizing mutually different properties of a chromatogram: Pearson correlation, conditional entropy, asterisk equations, convex hull, arithmetic mean (AN) and harmonic mean of the nearest neighbor, and geometric surface coverage (SC). Artificial chromatograms generated in silico and real GC × GC separations of diesel, plasma, and urine were used for the evaluation of orthogonality. The sample dimensionality has a deep effect on the orthogonality results of all approaches. The SC algorithm emerged as the best descriptor of local orthogonality samples of both low and high dimensionality, the AN algorithm on the global orthogonality of low-dimensionality samples. However, in the case of samples of high dimensionality, AN consistently indicated just the exploitation of the whole separation space; therefore, only local orthogonality is optimized by means of SC. Since no approach was able to monitor both global and local orthogonality as a single value, a new descriptor, ASCA, was developed. It combines the best global (AN) and local (SC) orthogonality algorithms by averaging, giving the same importance to data spread and crowding. ASCA thus provides the best estimation of orthogonality.

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Orthogonality is a key parameter that is used to evaluate the separation power of chromatography-based two-dimensional systems. It is necessary to scale the separation data before the assessment of the orthogonality. Current scaling approaches are sample-dependent, and the extent of the retention space that is converted into a normalized retention space is set according to the retention times of the first and last analytes contained in a unique sample to elute. The presence or absence of a highly retained analyte in a sample can thus significantly influence the amount of information (in terms of the total amount of separation space) contained in the normalized retention space considered for the calculation of the orthogonality. We propose a Whole Separation Space Scaling (WOSEL) approach that accounts for the whole separation space delineated by the analytical method, and not the sample. This approach enables an orthogonality-based evaluation of the efficiency of the analytical system that is independent of the sample selected. The WOSEL method was compared to two currently used orthogonality approaches through the evaluation of in silico-generated chromatograms and real separations of human biofluids and petroleum samples. WOSEL exhibits sample-to-sample stability values of 3.8% on real samples, compared to 7.0% and 10.1% for the two other methods, respectively. Using real analyses, we also demonstrate that some previously developed approaches can provide misleading conclusions on the overall orthogonality of a two-dimensional chromatographic system.

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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.

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Modeling the complicated chemical reactions in the interstellar medium and surface materials of Titan is nontrivial. Since both the atmosphere and the surface are rich in organic molecules, the chemistry may have important implications for the origin of biomolecules. Prebiotic synthesis of DNA nucleobases from simple molecules such as formamide has been known for more than half a century. In this study, new free radical pathways leading to the synthesis of guanine, hypoxanthine, purine, and adenine have been studied using density functional theory (B3LYP with the 6-311G(d,p) basis set). The pathways of the selected nucleobases demonstrate the importance of free radicals in the production of useful biomolecules under conditions appropriate for the interstellar medium or on Titan. The pathways may be universal in nature and proceed without solvent requirements. Calculations indicate that radical pathways yield lower reaction barriers as compared to previously reported pathways. Overall, these results suggest that the chemistry on Titan's surface and/or the growth of organic particulates in the haze layers in Titan's atmosphere likely involve free radicals. The mechanisms demonstrate that important prebiotic precursors can be predicted. The reaction sequences reported here may lead to the production and build-up of molecules with prebiotic relevance.

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Bacteria produce unique volatile mixtures that could be used to identify infectious agents to the species, and possibly the strain level. However, due to the immense variety of human pathogens, and the close relatedness of some of these bacteria, the robust identification of the bacterium based on its volatile metabolome is likely to require a large number of volatile compounds for each species. We applied comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOFMS) to the identification of the headspace volatiles of Pseudomonas aeruginosa PA14 grown for 24 h in lysogeny broth. This is the first reported use of GC×GC-TOFMS for the characterization of bacterial headspace volatiles. The analytical purity that is afforded by this chromatographic method facilitated the identification of 28 new P. aeruginosa-derived volatiles, nearly doubling the list of volatiles for this species.

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The cellular fatty acid profiles of eight strains of Bacillus, Staphylococcus, and Enterobacteriacae (Escherichia coli and Salmonella) were analyzed by comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry. A novel template method was developed to standardize the raw two-dimensional gas chromatography retention data through the use of a chemical indexing mixture. Analyte retention coordinates were normalized in the primary dimension with respect to a series of n-alkanes (Kovats index) and in the secondary dimension with respect to a series of aromatic hydrocarbons (Lee index). Fatty acid profiles extracted from the templates were compared by multidimensional scaling and principal component analysis. Differences in the profiles of Gram-positive and Gram-negative bacteria were observed, and a series of heterogeneous mixtures comprising different fractions (containing one Gram-positive and one Gram-negative bacteria strain) were also distinguished from their homogeneous constituents.

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The determination of polycyclic aromatic hydrocarbon (PAH) metabolites in human urine is the method of choice for assessing exposure to carcinogenic compounds. The objective of this study was the development of a comprehensive two-dimensional gas chromatography (GCxGC) method using a flame ionisation detector (FID) to simultaneously determine 10 hydroxylated PAH. The method was based on enzymatic deconjugation, liquid-liquid extraction, and trimethylsilyl (TMS) derivatization of the analytes by microwave heating. Satisfactory separation was achieved. The coefficient of variance was 3.8-12.8%. LOD was 0.03-0.18 microg/L, and LOQ was 0.1-0.5 microg/L. The mean recovery was 76%. The method was applied to the analysis of urine from smokers and non-smokers.

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Solid-phase microextraction (SPME) coupled to gas chromatography/mass spectrometry (GC/MS) was applied to the determination of phthalate esters in human serum. The present method decreased the sample preparation time by a factor of 50 by using direct immersion SPME with an 85-microm polyacrylate fiber to extract phthalate esters from the matrix. The use of fast GC/MS further improves total analysis time when compared to other techniques. Isotope dilution was successfully applied to improve the precision, reproducibility, and repeatability of the SPME method. The linear dynamic range spans several orders of magnitude from low ppb to ppm levels, and the LOD for the method is 15 pg microL(-1) on average with RSDs less than 4% for the six phthalate esters included in this study.

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A method for the high-throughput analysis of human serum for the 38 most prevalent polychlorinated biphenyls (PCBs) based on the use of fast gas chromatography-isotopic dilution time-of-flight mass spectrometry (GC-IDTOFMS) is presented. The chromatographic separation time was 8 min. The separation of the congeners was carried out either chromatographically or analytically using the mass spectral deconvolution capability of the TOFMS. The instrument and the method limits of detection (LODs) were 0.5 pg microL(-1) and 20 pg microL(-1), respectively, which is not as good as the one achieved using high resolution mass spectrometry (HRMS) but allows the detection and quantification of the prevalent PCBs present in real human serum samples. The dynamic range covered 3 orders of magnitude. The comparison with the high resolution mass spectrometry (HRMS) reference method (28 min) was good and some separation improvements have been observed. This method allows the analysis of 100 samples per day per instrument.

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A novel column characterization test mixture is developed for use in comprehensive two-dimensional gas chromatography (GC x GC). This mixture has been named the "Phillips mix" in honor of the late professor John B. Phillips, the father of GC x GC. The mixture comprises a series of homologous compounds from structural groups that cover a volatility and polarity range that is similar to the Grob mix, and includes saturated hydrocarbons (alkanes), unsaturated hydrocarbons (alkenes and alkynes), carbonyls (ketones and aldehydes), primary alcohols, fatty acid methyl esters, alkyl ethers, carboxylic acids, aromatics, as well as other unique functional groups (such as amines, etc.). Similarly to the Grob mix in conventional one-dimensional GC, the Phillips mix can be used as a standardized test for performance characterization of GC x GC column sets. Unlike the Grob mix, however, the Phillips mix's most important use is as a practical guideline for column users. This paper addresses some qualitative aspects of the use of the Phillips mix through an investigation of the chromatographic fingerprints of two different GC x GC column combinations.

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