Evaluation of separation in gradient elution ion chromatography by combining several retention models and objective functions

Bolanča, Tomislav; Cerjan‐Stefanović, Štefica; Luša, Melita; Ukić, Šime; Rogošić, Marko

doi:10.1002/jssc.200700470

Cited by 7 publications

(5 citation statements)

References 35 publications

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“…This approach, taken from literature, 16,19 is based on summation of contributions of small timeintervals inside which the retentions were calculated by use of experimentally obtained isocratic models. The isocratic models were 2 nd order polynomial correlations of analyte retention coefficient logarithm, logk, and eluent concentration logarithm, logc.…”

Section: Optimization Methodologymentioning

confidence: 99%

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Computer-assisted Development of ad hoc Methodology for Monitoring of Inorganic Cations in Surface Water Using Gradient Elution Ion Chromatography

Bolanča¹,

Ukić²,

Ruždjak³

2011

Croat. Chem. Acta

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Abstract. This work focuses on development of ad hoc methodology for monitoring of sodium, ammonium, potassium, magnesium and calcium ions in rivers Ilova and Kutinica (southeastern part of Croatia) since often their composition significantly differs from the average (concentration of components and/or matrix). Therefore fast and reliable method development has to be performed in order to meet criteria of the monitoring methodology performance characteristic. Computer assisted routine for prediction of chromatographic signal, based on a transfer of isocratic elution information into a gradient elution environment, was used in combination with several in-house developed optimization criteria. It is shown that algorithm used for prediction of ion-chromatographic signal offers sufficient information (in comparison to actual measured chromatographic signal) to be applied in global optimization process. Calculated optimal gradient incorporates linearly decreasing, increasing and isocratic part of the eluent concentration time-profile, utilizing full potential of the gradient separation. Developed method was validated proving that it meets criteria for river Ilova and Kutinica monitoring purposes. Moreover, this results show that used computer assisted optimization routine can be successfully applied for fast and reliable ad hoc method development in ion chromatography. (doi: 10.5562/cca1736)

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Section: Optimization Methodologymentioning

confidence: 99%

“…18,19 The aim of this work is application of computerassisted methodology in rapid method development process for IC determination of inorganic cations in surface water. Gradient elution retention model was used to predict resolution and peak asymmetry.…”

Section: Introductionmentioning

confidence: 99%

Computer-assisted Development of ad hoc Methodology for Monitoring of Inorganic Cations in Surface Water Using Gradient Elution Ion Chromatography

Bolanča¹,

Ukić²,

Ruždjak³

2011

Croat. Chem. Acta

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“…ANNs can also be used as the basis of computer-assisted optimization method for selection of optimal gradient conditions for anion separations in chromatography [39][40][41]. The ANNs with 1-10-9 architecture has been found to be rapid and accurate in predicting retention times for anions in linear gradient elution ion chromatography with hydroxide eluents.Tham et al applied ANNs in quantitative structuregradient elution retention relationship of the compounds such as phenyl thiocarbamyl amino acids derivatives with estimation of the chromatographic retention times [42].…”

Section: Anns Applications Analytical Data Analysis and Structure Retmentioning

confidence: 99%

Artificial Neural Network in Drug Delivery and Pharmaceutical Research

Sutariya¹,

Groshev²,

Sadana³

et al. 2013

TOBIOIJ

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Artificial neural networks (ANNs) technology models the pattern recognition capabilities of the neural networks of the brain. Similarly to a single neuron in the brain, artificial neuron unit receives inputs from many external sources, processes them, and makes decisions. Interestingly, ANN simulates the biological nervous system and draws on analogues of adaptive biological neurons. ANNs do not require rigidly structured experimental designs and can map functions using historical or incomplete data, which makes them a powerful tool for simulation of various non-linear systems.ANNs have many applications in various fields, including engineering, psychology, medicinal chemistry and pharmaceutical research. Because of their capacity for making predictions, pattern recognition, and modeling, ANNs have been very useful in many aspects of pharmaceutical research including modeling of the brain neural network, analytical data analysis, drug modeling, protein structure and function, dosage optimization and manufacturing, pharmacokinetics and pharmacodynamics modeling, and in vitro in vivo correlations. This review discusses the applications of ANNs in drug delivery and pharmacological research.

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“…We have shown that, at least for the studied solution/stationary phase system in the studied concentration range, they are sufficient to describe the whole peak, including prediction of individual peak asymmetry as well as chromatographic resolution between adjacent peaks 37. Generalized logistic function was used for this purpose; it is rather simple and its three parameters A , B , and C can be easily calculated from the abovementioned three characteristic points of the experimental distribution curve 38.…”

Section: Integrated Approach Descriptionmentioning

confidence: 99%

“…A number of different theoretical and empirical models for the prediction of characteristic chromatographic parameters (retention, resolution, efficiency, etc.) and the determination of optimal separation conditions have been brought out 1–16 in the last few decades. Some of them became bases of different optimization software (Drylab 17, Preopt‐W 18, 19, Osiris 20, Michrom 21, 22, Chromsword 23, and Virtual column 24).…”

Section: Introductionmentioning

confidence: 99%

Novel criteria for fast searching for optimal method in gradient ion chromatography: An integrated approach

Ukić

Bolanča

Rogošić

2011

J of Separation Science

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In this article, an integrated approach for prediction and optimization in ion chromatography (IC) was presented. The approach provides a fast and reliable insight in the elution behavior of an IC system. The predictions are based on a mathematical model that predicts ion retentions (for both isocratic and gradient modes) by using an empirical isocratic model. Other chromatographic values significant for the optimal elution conditions (resolution, peak asymmetry) are calculated quickly and easily from the predicted retention values of characteristic points of a chromatographic peak. Every day, IC users might find this approach a suitable tool for finding optimal IC elution conditions in a given system.

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Evaluation of separation in gradient elution ion chromatography by combining several retention models and objective functions

Cited by 7 publications

References 35 publications

Computer-assisted Development of ad hoc Methodology for Monitoring of Inorganic Cations in Surface Water Using Gradient Elution Ion Chromatography

Computer-assisted Development of ad hoc Methodology for Monitoring of Inorganic Cations in Surface Water Using Gradient Elution Ion Chromatography

Artificial Neural Network in Drug Delivery and Pharmaceutical Research

Novel criteria for fast searching for optimal method in gradient ion chromatography: An integrated approach

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