Development of an ion chromatographic gradient retention model from isocratic elution experiments

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

doi:10.1016/j.chroma.2006.04.036

Cited by 23 publications

(21 citation statements)

References 26 publications

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“…However, the use of ANN retention models in gradient optimization is limited only to the number of variables used in the training process [13]. This fact can be overcome by using isocratic-to-gradient retention model based on isocratic experiments and crossing of information to the gradient elution mode using numerical integration procedure [14].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the chromatographic signal in gradient elution ion chromatography

Bolanča¹,

Stefanović

Ukić

et al. 2009

J of Separation Science

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Original PaperPrediction of the chromatographic signal in gradient elution ion chromatography This study describes the development of a signal prediction model in gradient elution ion chromatography. The proposed model is based on a retention model and generalized logistic peak shape function which guarantees simplicity of the model and its easy implementation in method development process. Extensive analysis of the model predictive ability has been performed for ion chromatographic determination of bromate, nitrite, bromide, iodide, and perchlorate, using KOH solutions as eluent. The developed model shows good predictive ability (average relative error of gradient predictions 1.94%). The developed model offers short calculation times as well as low experimental effort (only nine isocratic runs are used for modeling).

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Section: Introductionmentioning

confidence: 99%

Prediction of the chromatographic signal in gradient elution ion chromatography

Bolanča¹,

Stefanović

Ukić

et al. 2009

J of Separation Science

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“…It is demonstrated that errors obtained in the isocratic fitting are propagated in the prediction of gradient retention [13]. The predictive ability of such gradient retention model is therefore compromised by the model used for isocratic retention modeling.…”

Section: Introductionmentioning

confidence: 99%

“…The quality of prediction is more extensively evaluated by checking the uncertainty of the isocratic to gradient model [13]. It is demonstrated that errors obtained in the isocratic fitting are propagated in the prediction of gradient retention [13].…”

Section: Introductionmentioning

confidence: 99%

“…One possibility to solve gradient elution optimization problem is by the prediction of retention in gradient elution mode by using isocratic experimental data [10][11][12][13]. That model is based on final (integral) of retention times of solutes, t g , which is described in terms of measurable properties (capacity factor, k; void time of a column, t 0 ):…”

Section: Introductionmentioning

confidence: 99%

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Application of different training methodologies for the development of a back propagation artificial neural network retention model in ion chromatography

Bolanča

Cerjan‐Stefanović

Ukić

et al. 2008

Journal of Chemometrics

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aThe reliability of predicted separations in ion chromatography depends mainly on the accuracy of retention predictions. Any model able to improve this accuracy will yield predicted optimal separations closer to the reality. In this work artificial neural networks were used for retention modeling of void peak, fluoride, chlorite, chloride, chlorate, nitrate and sulfate. In order to increase performance characteristics of the developed model, different training methodologies were applied and discussed. Furthermore, the number of neurons in hidden layer, activation function and number of experimental data used for building the model were optimized in terms of decreasing the experimental effort without disruption of performance characteristics. This resulted in the superior predictive ability of developed retention model (average of relative error is 0.4533%).

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“…A limited range of gradient programs may be modeled, similar to those used in data acquisition [32]. This problem may be overcome by using isocratic experimental data for gradient elution retention modeling [33].…”

Section: Introductionmentioning

confidence: 99%

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

Bolanča¹,

Cerjan‐Stefanović

Luša

et al. 2008

J of Separation Science

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Original PaperEvaluation of separation in gradient elution ion chromatography by combining several retention models and objective functions In this work, three different methods for modeling of gradient retention were combined with several optimization objective functions in order to find the most appropriate combination to be applied in ion chromatography method development. The system studied was a set of seven inorganic anions (fluoride, chloride, nitrite, sulfate, bromide, nitrate, and phosphate) with a KOH eluent. The retention modeling methods tested were multilayer perceptron artificial neural network (MLP-ANN), radial-basis function artificial neural network (RBF-ANN), and retention model based on transfer of data from isocratic to gradient elution mode. It was shown that MLP retention model in combination with the objective function based on normalized retention difference product was the most adequate tool for optimization purposes.

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Development of an ion chromatographic gradient retention model from isocratic elution experiments

Cited by 23 publications

References 26 publications

Prediction of the chromatographic signal in gradient elution ion chromatography

Prediction of the chromatographic signal in gradient elution ion chromatography

Application of different training methodologies for the development of a back propagation artificial neural network retention model in ion chromatography

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

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