Statistical theory of component overlap in multicomponent chromatograms

Davis, Joe M.; Giddings, J. Calvin

doi:10.1021/ac00254a003

Cited by 552 publications

(325 citation statements)

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“…Even under gradient elution conditions, peak capacity limits for separations of small molecules are in the range of 100 -200 for conventional conditions (d p A 2.0 lm, DP a 400 bar). Furthermore, the statistical models of peak overlap of Davis and Giddings [44] and Guiochon and coworkers [49] show that the numbers of peaks that are actually observed never exceed a small fraction of the peak capacity. Thus for separations of more than 20 -40 components, the resolving power of traditional 1-D HPLC analysis is inadequate.…”

Section: Increasing Peak Capacity By Using Htlcmentioning

confidence: 96%

“…2-D HPLC (2-DLC) has received considerable attention as the resolving power of 1-D HPLC is insufficient for many purposes [44], especially for many bioanalytical applications [45,46]. However, the primary impediment to the widespread use of 2-DLC is its long analysis time [46,47], making the technique nearly impractical for routine use.…”

Section: Increasing Peak Capacity By Using Htlcmentioning

confidence: 99%

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Practice and theory of high temperature liquid chromatography

McNeff¹,

Yan

Stoll

et al. 2007

J of Separation Science

115

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Review Practice and theory of high temperature liquid chromatographyHigh temperature liquid chromatography (HTLC) exists in a temperature region beyond ambient (ca. 408C) and below super critical temperatures. The promises of HTLC, such as increased analysis speed, enhanced separation productivity, "green" LC with pure water mobile phases coupled to universal FID detection, and fast analysis of complex samples by combination with fast 2-D techniques, have become an option for routine practice. The focus of this paper is to review the key developments that have made the application of HTLC a practical technique and draw attention to new developments in 2-D techniques that incorporate HTLC that offer an opportunity to vastly increase the usefulness of HPLC for the analysis of complex samples.

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Section: Increasing Peak Capacity By Using Htlcmentioning

confidence: 96%

Section: Increasing Peak Capacity By Using Htlcmentioning

confidence: 99%

Practice and theory of high temperature liquid chromatography

McNeff¹,

Yan

Stoll

et al. 2007

J of Separation Science

115

View full text Add to dashboard Cite

show abstract

“…Peak broadening upstream of the column under gradient conditions is usually negligible given that the solute is well retained in the initial eluent. 34 Therefore, we only considered the contribution from the post-column tubing ( ), the finite detector flow cell volume ( ) and the detector time constant ( ): (17) The variance from dispersion in the tubing between the column and the detector was estimated by the Atwood-Golay method, 35 which corrects the Aris-Taylor equation to make it applicable for very short straight tubes. The variances from the detector flow cell volume and detector time constant were calculated using equations described by Sternberg.…”

Section: Prediction Of Gradient Peak Widthmentioning

confidence: 99%

Peak Capacity Optimization of Peptide Separations in Reversed-Phase Gradient Elution Chromatography: Fixed Column Format

et al. 2006

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The optimization of peak capacity in gradient elution RPLC is essential for the separation of multicomponent samples such as those encountered in proteomic research. In this work we study the effect of gradient time (t G ), flow rate (F), temperature (T) and final eluent strength (ϕ final ) on the peak capacity of separations of peptides that are representative of the range in peptides found in a tryptic digest. We find that there are very strong interactions between the individual variables (e.g. flow rate and gradient time) which make the optimization quite complicated. On a given column, one should first set the gradient time to the longest tolerable and then set the temperature to the highest achievable with the instrument. Next, the flow rate should be optimized using a reasonable but arbitrary value of ϕ final . Last, the final eluent strength should be adjusted so that the last solute elutes as close as possible to the gradient time. We also develop an easily implemented, highly efficient and effective Monte Carlo search strategy to simultaneously optimize all the variables. We find that gradient steepness is an important parameter that influences peak capacity and an optimum range of gradient steepness exists in which the peak capacity is maximized.

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“…It is important to note that the peak capacity is a theoretical maximum number of components that can be resolved. In practice the number of components that can be resolved in real samples is usually much less than the peak capacity because elution times of real components are disordered (Davis and Giddings, 1983). This issue is discussed briefly in Section 1.2.…”

Section: Concept Of Peak Capacitymentioning

confidence: 99%