Melita Luša

Application of Different Artificial Neural Networks Retention Models for Multi-Criteria Decision-Making Optimization in Gradient Ion Chromatography

In this work, the principles of multi-criteria decision-making were used to develop an efficient optimization strategy in gradient elution ion chromatographic analysis. Two different artificial neural network retention models (multi-layer perceptron and radial basis function), three different separation criterion functions (chromatography response function, separation factor product and normalized retention difference product), and four different robustness criterion functions (CR1-CR4) were examined. The shape of the calculated separation vs the robustness response surface was used as principal criterion. Analysis time and minimum separation of adjacent peaks were additional criteria. The results showed that the radial basis artificial neural network retention model in combination with normalized retention difference product separation criterion function and CR3 robustness criterion function provided the optimal gradient ion chromatographic analysis.

Application of a Gradient Retention Model Developed by Using Isocratic Data for the Prediction of Retention, Resolution, and Peak Asymmetry in Ion Chromatography

Abstract In this work a model was developed for the prediction of retention time, resolution, and peak asymmetry in gradient elution mode by using isocratic experimental data. The predictive performance and generalization ability of the developed model was extensively tested by using an external experimental data set. The analysis of errors was performed in order to discuss and explain characteristics of the model. It was shown that the model performed satisfactorily and that it could be used for a modeling procedure in the optimization part of the ion chromatography method development.

Evaluation 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.

Prediction 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).

Multi-criteria decision making development of ion chromatographic method for determination of inorganic anions in oilfield waters based on artificial neural networks retention model.

This paper describes the development of ad hoc methodology for determination of inorganic anions in oilfield water, since their composition often significantly differs from the average (concentration of components and/or matrix). Therefore, fast and reliable method development has to be performed in order to ensure the monitoring of desired properties under new conditions. The method development was based on computer assisted multi-criteria decision making strategy. The used criteria were: maximal value of objective functions used, maximal robustness of the separation method, minimal analysis time, and maximal retention distance between two nearest components. Artificial neural networks were used for modeling of anion retention. The reliability of developed method was extensively tested by the validation of performance characteristics. Based on validation results, the developed method shows satisfactory performance characteristics, proving the successful application of computer assisted methodology in the described case study.