Fully automatic high-performance liquid chromatographic optimization

Djordjevic, Nebojša M.; Erni, F.; Schreiber, B.; Lankmayr, Ernst; Wegscheider, Wolfhard; Jaufmann, L.

doi:10.1016/s0021-9673(01)88528-9

Cited by 12 publications

(5 citation statements)

References 5 publications

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“…The result from this new experiment is then used to improve the chemometric model and suggest the next experiment in a repetitive process until an acceptable separation is reached. This way of empirical modelling combines the advantages of simultaneous and sequential optimisation, and similar approaches have been made by Drouen et al [2] and Djordjevic et al [3].…”

Section: Introductionmentioning

confidence: 85%

Optimisation of chromatographic separations by use of a chromatographic response function, empirical modelling and multivariate analysis

1997

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SummaryThe chiral separation of the drug substance R,Soxybutynin chloride on a reversed phase HPLC system has been optimised by use of empirical modelling and multivariate analysis. The separation was characterised by a new chromatographic response function developed to modulate both quality of separation and retention time. The study includes a comparison between three different multivariate techniques (multi-layer feed-forward neural networks, multiple linear regression and partial least squares regression) of their capabilities to model the new chromatographic response function and predict its value for new experiments. It was indicated that the most accurate models were achieved with neural networks, although partial least squares regression could also be used to solve the problem since it gives the major directions for the optimal settings of the variables.

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Section: Introductionmentioning

confidence: 85%

Optimisation of chromatographic separations by use of a chromatographic response function, empirical modelling and multivariate analysis

1997

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“…11 An advanced way of performing peak tracking, based on peak area, is the use of fuzzy theory. [12][13][14] This method can also use the elution order of the components as a source of information for the peak tracking procedure, since the elution order usually does not change abruptly when changing from one experimental condition to another. The method takes into account unknown components and peak overlap.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of high-performance liquid chromatography with diode array detection using an automatic peak tracking procedure based on augmented iterative target transformation factor analysis

Zomeren

Hoogvorst

Coenegracht

et al. 2004

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An automated method for the optimisation of high-performance liquid chromatography is developed. First of all, the sample of interest is analysed with various eluent compositions. All obtained data are combined into one augmented data matrix. Subsequently, augmented iterative target transformation factor analysis performs the integrated tasks of curve resolution and peak tracking. Since this type of curve resolution processes all data at once, it can deal with strong peak overlap and reveal the correspondence of compounds between runs, i.e. peak tracking. The retention time and peak width at half height for each component of the sample are determined for every eluent composition. Next, models are built for the retention time and the peak width at half height. These models are used to predict the resolution and the analysis time for each point in factor space. Finally, a multi-criterion decision-making method, Pareto optimality, is used to find the optimum. The method completes all calculations within a few minutes and without user intervention. By means of this procedure, a mixture of three benzodiazepines is successfully separated using a ternary mobile phase. There are two requirements for the automated optimisation method to work correctly. Firstly, all components of the sample must have sufficiently different spectra. Secondly, each compound should have the same spectrum under all experimental conditions.

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“…The large interpatient variability in CyA pharmacokinetics, coupled with serious adverse side-effects of CyA on the kidneys and liver means that constant monitoring of CyA in patients blood levels is required. High hydrophobicity of the cyclosporins makes HPLC method development rather laborious [7,8]. In HPLC, elevated temperatures are necessary for the analysis of CyA to ensure a fast equilibration of individual conformers into the single conformational structure [5,6] required for a gaussian-shaped peak [7].…”

Section: Introductionmentioning

confidence: 99%

Separation of peptides utilizing ultrahigh-temperature micellar electrokinetic chromatography

2001

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The use of ultrahigh column temperatures, up to 110 degrees C, in micellar electrokinetic capillary chromatography was investigated. The number of plates generated per unit time increased from 0.22 to 12.8 plates/s for separations at 15 degrees C and 110 degrees C, respectively. Ultrahigh-temperature micellar electrokinetic capillary chromatography was used for the separation of cyclic undecapeptides (cyclosporins). A minimum resolution of 1.39 was calculated for a critical peak pair at 110 degrees C, which is more than a 50% increase over resolution generated at 40 degrees C. During a run time of more than 90 min at 110 degrees C and at pH 9.3, no sample degradation or solvent boiling was observed.

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Fully automatic high-performance liquid chromatographic optimization

Cited by 12 publications

References 5 publications

Optimisation of chromatographic separations by use of a chromatographic response function, empirical modelling and multivariate analysis

Optimisation of chromatographic separations by use of a chromatographic response function, empirical modelling and multivariate analysis

Optimisation of high-performance liquid chromatography with diode array detection using an automatic peak tracking procedure based on augmented iterative target transformation factor analysis

Separation of peptides utilizing ultrahigh-temperature micellar electrokinetic chromatography

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