Optimization strategy for reversed-phase liquid chromatography of peptides

Lundell, Niklas; Markides, Karin E.

doi:10.1016/0021-9673(93)80246-5

Cited by 16 publications

(7 citation statements)

References 73 publications

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“…Several approaches may be used. [2][3][4] First of all, the entire variable space can be searched in a stepwise manner. This approach is known as a grid search.…”

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

Analyst

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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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“…Several approaches may be used. [2][3][4] First of all, the entire variable space can be searched in a stepwise manner. This approach is known as a grid search.…”

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

Analyst

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“…Once a criteria function is chosen and the experimental data is regressed to a response model, the model is optimized in the experimental variables to maximize the criteria function. Techniques used previously to maximize the values of these chromatographic response models include computerized grid searches (Cotton and Down, 1983; Lundell and Markides, 1993), response surface modeling (Felinger and Guiochon, 1992; Felinger and Guiochon, 1994; Wang et al, 1991), and the sequential simplex method (Berridge, 1985; Palasota et al, 1992; Wang et al, 1993).…”

Section: Introductionmentioning

confidence: 99%

Optimization of Ion-Exchange Protein Separations Using a Vector Quantizing Neural Network

2000

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In this work, a previously proposed methodology for the optimization of analytical scale protein separations using ion-exchange chromatography is subjected to two challenging case studies. The optimization methodology uses a Doehlert shell design for design of experiments and a novel criteria function to rank chromatograms in order of desirability. This chromatographic optimization function (COF) accounts for the separation between neighboring peaks, the total number of peaks eluted, and total analysis time. The COF is penalized when undesirable peak geometries (i.e., skewed and/or shouldered peaks) are present as determined by a vector quantizing neural network. Results of the COF analysis are fit to a quadratic response model, which is optimized with respect to the optimization variables using an advanced Nelder and Mead simplex algorithm. The optimization methodology is tested on two case study sample mixtures, the first of which is composed of equal parts of lysozyme, conalbumin, bovine serum albumin, and transferrin, and the second of which contains equal parts of conalbumin, bovine serum albumin, tranferrin, beta-lactoglobulin, insulin, and alpha -chymotrypsinogen A. Mobile-phase pH and gradient length are optimized to achieve baseline resolution of all solutes for both case studies in acceptably short analysis times, thus demonstrating the usefulness of the empirical optimization methodology.

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“…12) (23), and these functions are often used in optimization procedures. *1 ^^ (10) (AW?,, -n(n + 1)…”

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confidence: 99%

“…Because of its simplicity and popularity, this function is widely used in research as a criteria function for different methods of optimization (9)(10)(11)(12). Resolution-based criteria have the disadvantage of inexact determination of the width of a spot.…”

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confidence: 99%

Optimization

Cimpoiu¹

2003

Handbook of Thin-Layer Chromatography

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Optimization strategy for reversed-phase liquid chromatography of peptides

Cited by 16 publications

References 73 publications

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

Optimization of Ion-Exchange Protein Separations Using a Vector Quantizing Neural Network

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