Development of an inorganic cations retention model in ion chromatography by means of artificial neural networks with different two-phase training algorithms

Bolanča, Tomislav; Cerjan‐Stefanović, Štefica; Regelja, Melita; Regelja, Hrvoje; Lončarić, Sven

doi:10.1016/j.chroma.2005.02.018

Cited by 29 publications

(18 citation statements)

References 31 publications

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“…The weights of the hidden layer were optimized using the Levenberg-Marquardt algorithm, a second derivative optimization method (Bolanča et al 2005).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Chemometrics analysis for investigation of retention behavior of hazardous compounds in effluents

Karimi

Farmany

Noorizadeh

2012

Environ Monit Assess

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The toxic substances, pesticides, and organic contaminants in effluents can potentially be causing damage that includes increased cancer risk; liver, kidney, stomach, nervous system, and immune system problems; reproductive difficulties; cataracts; and anemia. A quantitative structure-retention relationship (QSRR) was developed using the partial least square (PLS), kernel PLS (KPLS), and Levenberg-Marquardt artificial neural network (L-M ANN) approach for chemometrics study. The data which contained retention time (RT) of the 47 hazardous compounds in effluents were obtained by reverse-phase high-performance liquid chromatography. Genetic algorithm was employed as a factor selection procedure for PLS and KPLS modeling methods. By comparing the results, GA-PLS descriptors are selected for L-M ANN. Finally, a model with a low prediction error and a good correlation coefficient was obtained by L-M ANN. The described model does not require experimental parameters and potentially provides useful prediction for RT of new compounds. This is the first research on the QSRR of hazardous compounds in effluents using the chemometrics models.

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“…The weights of the hidden layer were optimized using the Levenberg-Marquardt algorithm, a second derivative optimization method (Bolanča et al 2005).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Chemometrics analysis for investigation of retention behavior of hazardous compounds in effluents

Karimi

Farmany

Noorizadeh

2012

Environ Monit Assess

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“…In our previous papers, ANNs, coupled with experimental design (ED), were employed in optimization of chromatographic behavior of different substances [1][2][3]. In addition, ANNs proved to be a very useful approach in process optimization [4][5][6][7][8][9], retention modeling in liquid chromatography [10][11][12][13][14][15], as well as in the optimization of formulation parameters [16][17][18]. In addition, ANNs present a powerful tool in definition of the quantitative structure-retention relationships (QSRR) [19][20][21], quantitative structure-activity relationships (QSAR) [22,23], and quantitative structure-binding relationships (QSBR) [24] and so on.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Artificial Neural Networks for Modeling of Atorvastatin and Its Impurities Retention in Micellar Liquid Chromatography

et al. 2011

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Artificial Neural Networks (ANNs) present a powerful tool for the modeling of chromatographic retention. In this paper, the main objective was to use ANNs as a tool in modeling of atorvastatin and its impurities' retention in a micellar liquid chromatography (MLC) protocol. Factors referred to MLC were evaluated through 30 experiments defined by the Central Composite Design. In this manner, 5-x-3 topology as a starting point for ANNs' optimization was defined too. In the next step, in order to set the network with the best performance, network optimization was done. In the first part, the number of nodes in the hidden layer and the number of experimental data points in training set were simultaneously varied, and their importance was estimated with suitable statistical parameters. Furthermore, a series of training algorithms was applied to the current network. The Back Propagation, Conjugate Gradient-descent, Quick Propagation, QuasiNewton, and Delta-bar-Delta algorithms were used to obtain the optimal network. Finally, the predictive ability of the optimized neural network was confirmed through several statistical tests. The obtained network showed high ability to predict chromatographic retention of atorvastatin and its impurities in MLC.

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“…A large number of retention models were explained for isocratic IC elution. [5][6][7][8][9][10][11][12][13] Gradient elution retention model is harder to develop due to a fact that far more variables need to be modeled in order to present sufficient gradient combinations. Artificial neural networks (ANN) have modeled the retention behavior of inorganic anions, in relation with slope of linear gradient elution curve and starting time of gradient program.…”

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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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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Development of an inorganic cations retention model in ion chromatography by means of artificial neural networks with different two-phase training algorithms

Cited by 29 publications

References 31 publications

Chemometrics analysis for investigation of retention behavior of hazardous compounds in effluents

Chemometrics analysis for investigation of retention behavior of hazardous compounds in effluents

Optimization of Artificial Neural Networks for Modeling of Atorvastatin and Its Impurities Retention in Micellar Liquid Chromatography

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

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