Comprehensive two-dimensional gas chromatography: metrics, potentials, limits

Blumberg, Leonid M.

doi:10.1016/s0021-9673(02)01416-4

Cited by 78 publications

(64 citation statements)

References 16 publications

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“…The peak capacity in the above formula can be different from the peak capacity [9,19] that, under optimal conditions, 1 U could be as high as 0.8. There are other factors that need to be considered when a 2-D separation is compared with its own first dimension standing alone.…”

Section: Linear Peak Capacity Gain From Addition Of the Second Dimensionmentioning

confidence: 97%

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Linear peak capacity of a comprehensive multi‐dimensional separation

Blumberg¹

2008

J of Separation Science

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In order to resolve (quantifiably and identifiably separate) the same number of peaks in the analysis of the same mixture yielding statistically uniform peak distribution, a comprehensive 2-D separation needs a two times larger peak capacity than a 1-D separation does. Each additional dimension further reduces the utilization of the peak capacity of comprehensive multi-dimensional (MD) separation by a factor of two per dimension. As a result, the same peak capacity means different things for separations with different dimensionalities. This complicates the use of the peak capacity for comparison of the potential separation performance of the separations with different dimensionalities. To facilitate the comparison, a concept of a linear peak capacity has been proposed. The linear peak capacity of an MD separation is the peak capacity of a 1-D separation that, in the analysis of the same mixture, is statistically expected to resolve the same number of peaks as the MD separation is. There are other factors that differently affect the performance of the separations that have different dimensionalities. Peak capacity of a 2-D separation with a rectangular separation space is 27% larger than the product of the peak capacities of its first and second dimension. This advantage of a 2-D separation is essentially nullified by the fact that the peak capacity of the first dimension of an optimized 2-D separation cannot be higher than 80% of the peak capacity of its first dimension standing alone. All in all, the incremental peak capacity gained from addition of a second dimension will not exceed 50% of the peak capacity of the added second dimension. All results are valid for arbitrarily shaped (not necessarily Gaussian) peaks.

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Section: Linear Peak Capacity Gain From Addition Of the Second Dimensionmentioning

confidence: 97%

“…Then, under optimal conditions when [9,19] 1 U Opt L 0.8, the above formula yields G G n % 0:5 Á 2 n c ðat 1 S min;o ¼ 1 S min; 1 U Opt % 0:8Þ ð 30Þ…”

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confidence: 99%

Linear peak capacity of a comprehensive multi‐dimensional separation

Blumberg¹

2008

J of Separation Science

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“…Using one-dimensional GC-tof-MS, the number of peaks observable in human serum was approximately trebled (compared with another GC-tof-MS method that was the starting point and that we thereby improved) to >1200 [45], which has already enabled the discovery of several novel biomarkers for pre-eclampsia [46] (and see later). Two-dimensional GCxGC-tof-MS has been exploited further to treble this number to >4000 raw peaks, which equates to 1800 metabolite peaks [47,48]. Comparable liquid-phase chromatographies based on reverse-phase UPLC-MS [49,50] can measure thousands of peaks, and a new version of normal-phase UPLC-MS using HILIC [51,52] columns provides complementary measurements.…”

Section: Metabolomics and Technologies For Its Measurementmentioning

confidence: 99%

Systems biology, metabolic modelling and metabolomics in drug discovery and development

Kell

2006

Drug Discovery Today

265

163

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Unlike signalling pathways, metabolic networks are subject to strict stoichiometric constraints. Metabolomics amplifies changes in the proteome, and represents more closely the phenotype of an organism. Recent advances enable the production (and computer-readable encoding as SBML) of metabolic network models reconstructed from genome sequences, as well as experimental measurements of much of the metabolome. There is increasing convergence between the number of human metabolites estimated via genomics ( 3000) and the number measured experimentally. It is thus both timely, and now possible, to bring these two approaches together as an integrated (if distributed) whole to help understand the genesis of metabolic biomarkers, the progress of disease, and the modes of action, efficacy, off-target effects and toxicity of pharmaceutical drugs. Systems biology and metabolic modelling in the 21st CenturyAlthough there are many individual definitions, most commentators (including this one [1,2]) take it that systems biology involves an iterative interplay between more or less high-throughput and high-content 'wet' experiments, technology development, theory and computational modelling, and that it is the involvement of computational modelling, in particular, in the process that sets systems biology apart from the more traditional and more reductionist molecular biology. Metabolomics illustrates this amply. There is also the view that the perceived decrease in the effectiveness of the target-based drug discovery process [3][4][5], including the still-high levels of attrition [6], means that we must move towards understanding organisms at something more akin to a whole-system level [7][8][9][10][11].The question then arises as to what part of a system one might first beneficially model? Although there has, unsurprisingly, been considerable interest in modelling the major signalling pathways (e.g. Refs [12,13]), there are several reasons why it is timely to turn our attention to the level of small molecule metabolism, which is the focus of this review. Metabolism is more discriminatingIt has long been known, and proven through the formalism of metabolic control analysis [7,10,[14][15][16], that whereas small changes in the concentrations of enzymes (and the transcripts that encode them) have only small effects on the fluxes through metabolic pathways, they have substantial effects on the concentrations of metabolic intermediates. Because the metabolome (nominally the concentrations of 'all' the metabolites measured in a system of interest [17]) is downstream of the proteome, it is thereby 'amplified' both in theory [18] and in practice [19,20] and represents a more sensitive level of organisation than do the macromolecular 'omes for understanding a complex biological system, and the changes in it that might be occasioned by disease or pharmaceutical intervention [21,22]. Metabolic reconstruction is now mature and timelyAn attractive feature for the purposes of modelling is that metabolism, in contrast to signalling pathways, ...

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“…In practise, the second dimension columns generate only about 2500 effective plates [13], and can separate 5-10 peaks [7,14] that are so narrow that they require fast detectors and mass spectrometers [15][16][17], which with the present state of the art cannot provide accurate masses. In addition, and critically for flavour and fragrance work, GC-olfactometry has not been demonstrated to be possible after comprehensive separations.…”

Section: Introductionmentioning

confidence: 99%

Low cost, robust, in‐house hardware for heart cutting two‐dimensional gas chromatography

Apps¹

2006

J of Separation Science

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Natural materials are so complex that no single column can separate all of the components. Heart cutting 2-D GC (GC-GC) using a Deans switch provides maximum separation, but the requisite gas flow configurations have earned a reputation for being fiddly and time consuming to set up and tune, unless electronic gas controls and costly software are employed. A design for a vented Deans switch is presented that can be assembled in-house from standard commercial components. A vent to atmosphere replaces the balancing resistor, which solves the problem of balancing the pressures and flows, and requires no compensation for changes in gas viscosity and back-pressure during temperature programming. First and second dimension columns can both be run at optimum flow rates, even if they are of different diameters. Analyte detectability is preserved by cutting only the target fraction from the first column and transferring the whole of it to the second column. Cryotrapping the selected fraction allows cuts of any width, and transfer of analytes to the second column as sharp bands. I have applied the vented Deans switch-cold trap to the identification of flavour and fragrance compounds from South African plants and foods; cuts are very repeatable, and the detectability of trace components is enhanced. Its capabilities are demonstrated by examples from analyses of essential oils and flavour extracts.

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Comprehensive two-dimensional gas chromatography: metrics, potentials, limits

Cited by 78 publications

References 16 publications

Linear peak capacity of a comprehensive multi‐dimensional separation

Linear peak capacity of a comprehensive multi‐dimensional separation

Systems biology, metabolic modelling and metabolomics in drug discovery and development

Low cost, robust, in‐house hardware for heart cutting two‐dimensional gas chromatography

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