Improved method for computing temperature programmed retention indices from isothermal data

Eveleigh, Luc; Ducauze, C.; Arpino, Patrick

doi:10.1016/0021-9673(95)00975-2

Cited by 9 publications

(5 citation statements)

References 13 publications

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“…However, for the two different models, the difference in the time spacing of two peaks corresponding to the same pair of solutes seldom exceeds 0.1 min or about 1% of the entire analysis time shown in Figure 8.3. This observation appears to be in agreement with experimental data confirming that the ideal thermodynamic model [24][25][26][27] and several linearized models [2,3,28,29] provide a good prediction of a general elution order of all peaks. This observation appears to be in agreement with experimental data confirming that the ideal thermodynamic model [24][25][26][27] and several linearized models [2,3,28,29] provide a good prediction of a general elution order of all peaks.…”

Section: Isobaric Linear Heating Ramp and Highly Interactive Solutessupporting

confidence: 86%

Formation of Peak Spacing

Blumberg¹

2010

Temperature‐Programmed Gas Chromatography

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When two solutes, say 1 and 2, simultaneously injected in a column migrate along the column with different velocities, v 1 and v 2 , their longitudinal distance,from each other, and temporal distancethe difference,is thermal spacing of the peaks. Generally, a closed form solution for Dt R cannot be obtained. A numerical solution can be found from subtraction of numerical solutions of Eqs. (8.27) and (8.28) for t R1 and t R2 . In several practically important special cases, substantial simplifications in evaluations of Dt R or DT R can be made.where v oR is the outlet velocity of the t R -solute at the time t R of its elution. The last formula is based on two assumptions. First, the change in Dz R during the time interval Dt R is negligible compared to Dz R , and, second, the difference between v oR and the outlet velocity, v oR,1 , of the neighboring solute is negligible compared to v oR . Both the assumptions are direct consequence of the properties of the solutes that make them to migrate close to each other. The negative sign in the formula reflects the fact that t R,1 > t R when the t R -solute arrives to the column outlet before its neighbor, and therefore, Dz R ¼ z 1,R À L < 0.An infinitesimally small change, d(Dz), in Dz that takes place during infinitesimally small time interval, dt, when the t R -solute and its neighbor advance from arbitrary locations, z and z 1 to locations z þ dz and z 1 þ dz 1 , respectively, can be 194j 9 Formation of Peak Spacing 9.2 Closely Migrating Solutes in Dynamic Analysis j195

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Section: Isobaric Linear Heating Ramp and Highly Interactive Solutessupporting

confidence: 86%

Formation of Peak Spacing

Blumberg¹

2010

Temperature‐Programmed Gas Chromatography

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“…Methods for the calculation of retention indices in TPGC have been proposed, and they have been tested in different laboratories (7,8,17). Although they seem to work well, they still have the disadvantage of the need for mixtures of reference compounds (hydrocarbons), and the mathematical calculations are more complex than those involved in isothermal analysis.…”

Section: Discussionmentioning

confidence: 99%

“…However, in TPGC there is no apparent relationship between the number of carbon atoms and the log r. Therefore, it seems appropriate to search for mathematical procedures for the identification of peaks from compounds of homologous series in TPGC analysis. The need for expanding mathematical methods for the identification of peaks on TPGC has already been identified and studied for monoterpenes, halogenated pesticides, and n-alkanes, among others (6)(7)(8), but due to the requirement of relatively sophisticated and time-consuming mathematical analyses, these procedures are still of limited practical use, especially for routine analyses. Additionally, to our knowledge there are no mathematical or graphical methods for the identification of FAME in TPGC described in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…The need for expanding mathematical methods for the identification of peaks on TPGC has already been identified and studied for monoterpenes, halogenated pesticides, and n -alkanes, among others ( − ), but due to the requirement of relatively sophisticated and time-consuming mathematical analyses, these procedures are still of limited practical use, especially for routine analyses. Additionally, to our knowledge there are no mathematical or graphical methods for the identification of FAME in TPGC described in the literature.…”

Section: Introductionmentioning

confidence: 99%

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Mathematical Method for the Prediction of Retention Times of Fatty Acid Methyl Esters in Temperature-Programmed Capillary Gas Chromatography

Torres

Trugo

2002

J. Agric. Food Chem.

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An accurate method for identification of fatty acids in complex mixtures analyzed by temperature-programmed capillary gas chromatography is described. The method is based on a mathematical approach using regression curves obtained by plotting the relative retention times of fatty acid methyl esters (FAMEs) analyzed in isothermal and gradient temperature conditions. The method was applied to a complex biological sample (human milk), and it was possible to identify 64 fatty acids, including branched-chain and other fatty acids for which reference standards were not readily available. The identities of the majority of the peaks were confirmed by mass spectrometry. The relative residuals and the relative differences between estimated and measured relative retention times of individual FAMEs varied from 0.03 to 3.15% and from 0.0 to 2.9%, respectively. The method is useful for identification of fatty acids in routine analysis.

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“…Much experimental effort [4 -6] has been invested in the Gibbs free energy of solution model of the GLC capacity factor, measured at various temperatures [6]:…”

Section: Introductionmentioning

confidence: 99%

Determination of Vapor Liquid Equilibrium from the Kovats Retention Index on Dimethylsilicone using the Wilson Mixing Model

Spieksma¹

1999

J. High Resol. Chromatogr.

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Non‐ideal mixing in a dimethylsilicone stationary phase is modeled according to the Wilson activity coefficient. Pure liquid vapor pressures of alkylated compound series are calculated from capillary GLC retention with the functional group heat of solution in a polymer solvent. The new method uses the Kovats index, molar mass, and functional group to determine the bubble line of a compound. Boiling points at reduced and normal pressure are compared to literature values of 194 gasoline components. An unlike molecular pair interaction parameter is derived, using only bubble line data of the pure liquids. Binary phase diagrams are constructed and compared to vapor liquid equilibrium data.

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Improved method for computing temperature programmed retention indices from isothermal data

Cited by 9 publications

References 13 publications

Formation of Peak Spacing

Formation of Peak Spacing

Mathematical Method for the Prediction of Retention Times of Fatty Acid Methyl Esters in Temperature-Programmed Capillary Gas Chromatography

Determination of Vapor Liquid Equilibrium from the Kovats Retention Index on Dimethylsilicone using the Wilson Mixing Model

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