Decoding complex multicomponent chromatograms.
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Chromatography↗
Biomedical subjects
A Felinger
Publications and source records attributed to A Felinger.
Analysis of the band profiles of the enantiomers of phenylglycine in liquid chromatography on bonded teicoplanin columns using the stochastic theory of chromatography.
The retention behaviour of the enantiomers of underivatized phenylglycine was studied on a Chirobiotic T column packed with amphoteric glycopeptide teicoplanin covalently bonded to the surface of silica gel. The retention and the selectivity of separation of the enantiomers increase with rising concentration of ethanol or of methanol in aqueous-organic mobile phases. The band profiles of the less retained L-phenylglycine are symmetrical, but the band profiles of the more strongly retained D-phenylglycine are tailing in all mobile phases tested. The band broadening does not diminish even at very low concentrations of phenylglycine, so that it cannot be attributed to possible column overload. The analysis of the band profile using the stochastic theory of chromatography suggests that the broadening can be attributed to at least two additional chiral centres of adsorption in the stationary phase contributing to the retention of the more strongly retained enantiomer in addition to the adsorption of the less retained one. This behaviour can be explained by the complex structure of the teicoplanin chiral stationary phase.
Adsorption↗
Validation of a chromatography data analysis software.
The performance of chromatography data analysis software packages is of cardinal importance when the precision and the accuracy of a chromatographic system are evaluated. Users cannot rely on a procedure generating chromatographic data of known accuracy. Holistic approaches cannot always be entirely trusted. We propose a new method consisting in validating a data analysis package against computer generated chromatograms of exactly known characteristics by feeding these chromatograms into the vendor supplied software and comparing the results supplied by the software and the exact answers. We simulated symmetrical and tailing chromatograms and processed these signals with the Agilent Technologies (formerly Hewlett-Packard) ChemStation software. The noise profile (i.e. the power spectrum of the baseline) was determined for a HPLC UV detector prior to the calculations, and chromatograms of different signal-to-noise ratios were used for the analysis. For every chromatogram, we simulated 25 replicates with identical signal-to-noise ratios but different noise sequences. In this manner, both the random and the systematic errors of the retention data and peak shape characteristics can be evaluated. When analyzing tailing peaks, we simulated the effects of extra-column band broadening and those of column overload. Our calculations show that the general performance of the data analysis system studied is excellent. The contribution of the random error originating from the data analysis procedure is in most cases negligible compared to the repeatability of the chromatographic measurement itself.
Chromatography, High Pressure Liquid↗
Identification of the factors that influence the reproducibility of chromatographic retention data.
Principal component analysis was used to identify the parameters that influence the column-to-column and batch-to-batch reproducibility of retention times and retention factors measured on Symmetry C18, Kromasil C18, Luna C18 (2) and Vydac RP C18, all reversed-phase silica columns. We devised a procedure that allows the determination of the differences in column volume and packing density between two columns, provided that these columns are packed with identical stationary phases (i.e., phases that originate from the same batch). Principal component analysis of the retention times confirmed that the column-to-column variations of the column volume and the total porosity of the bed are the factors that influence the reproducibility of the retention times, the column volume being the major factor. For the fluctuations of the retention factors, the column phase ratios (or the bed porosities) and some specific, secondary retention mechanisms are responsible. All the C18 columns investigated proved to behave in a very similar fashion. Two principal components were always sufficient to characterize the variations of either the retention times or the retention factors.
Chromatography, Liquid↗
Comparing the optimum performance of the different modes of preparative liquid chromatography.
A comparative study of the optimization of the different modes of the preparative separation of binary mixtures by liquid chromatography is presented. Band profiles were calculated by means of the equilibrium-dispersive model of chromatography in the cases of isocratic elution, gradient elution, and displacement chromatography. The objective function to be maximized was the product of the production rate and the recovery yield. The production rate was calculated using the same definition of the cycle time in all cases. This common definition accounts for column regeneration after each run in each mode of the separation. The calculations reveal that the number of experimental parameters to be adjusted to achieve optimum separations is relatively small. The major parameters are the loading factor and the number of theoretical plates, besides the displacer concentration in displacement chromatography, or the gradient steepness in gradient elution. The relative advantages of the different modes of preparative chromatography are discussed.
Chromatography, Liquid↗
The change of pressure drop during large-scale chromatography of viscous samples.
The variation of the inlet pressure during the injection and the elution of a band of a viscous compound, or pressure-drop profile, was calculated in overloaded elution chromatography, assuming a significant difference between the viscosities of the solvent and the pure solute. The viscosity of the mixture was calculated using the Grunberg-Nissan equation. By means of the numerical integration of the mass balance equation, the concentration profile along the length of the column was calculated. Then, using the instantaneous concentration profile to derive the local viscosity, hence the local pressure gradient, the pressure drop was determined by integrating the local pressure gradient along the column. The change of pressure drop calculated is significant only when the concentration of the injected sample is very high. If a dilute sample is injected, even large sample volumes will not really change the pressure drop. The maximum pressure drop observed in each case is practically independent of the absorption isotherm. The only determining factors are the solute concentration in the sample, the amount of sample injected, and the solute diffusivity.
Biotechnology↗