Comparison of prediction power between theoretical and neural-network models in ion-interaction chromatography

Sacchero, Giovanni; Bruzzoniti, Maria Concetta; Sarzanini, Corrado; Mentasti, Edoar̈do; Metting, H. J.; Coenegracht, P.M.J.

doi:10.1016/s0021-9673(97)01044-3

Cited by 36 publications

(11 citation statements)

References 35 publications

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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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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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“…After calculation of the molecular descriptors, linear method such as multiple linear regression (MLR), or nonlinear method such as artificial neural network (ANN) can be employed to derive correlation between the molecular structures and retention parameters. ANNs have been applied to a wide variety of chemical problems, such as simulation of mass spectra [26], modeling of ion interaction chromatography [27], prediction of 13 C-NMR chemical shift [28], response surface modeling in HPLC optimization [29], and quantitative structure-activity relationship studies [30 -32]. The flame ionization detector and thermal conductivity detector response factors for a diverse set of organic molecules were also predicted by using the ANN method [33,34].…”

Section: Introductionmentioning

confidence: 99%

Prediction of supercritical fluid chromatographic retention factors at different percents of organic modifiers in mobile phase

Fatemi

Malekzadeh

Shamseddin

2009

J of Separation Science

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In this work quantitative structure-retention relationship models were developed to predict the solute retention factors in supercritical fluid chromatography (SFC) in various organic modifiers. Data set contains the retention data of 35 various organic compounds in 0, 2, 4 and 6% of methanol in mobile phase. The obtained 140 data points were divided into training, internal and external test sets which have 93, 23 and 24 retention data. The diversity validation test was performed on data the set to ensure that the structure of the training and/or test sets can represent those of the whole ones. Descriptors which were selected by stepwise multiple linear regression (MLR) methods are: the percent of organic modifier in mobile phase, salvation connectivity index chi-2, salvation connectivity index chi-5, H attached to heteroatom, the 2(nd) structural information content and polarity parameter. These descriptors were used as features in generation of linear and non-linear models using MLR and artificial neural network (ANN) methods, respectively. The root mean square error of MLR model are 0.116, 0.138 and 0.260 for training, internal and external test sets, respectively, while these values are 0.036, 0.097 and 0.244 for ANN model, respectively. Comparison between these values and other statistical parameters obtained from these two models reveals the credibility of ANN in prediction of solute retention factors in SFC.

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“…Due to their natures of nonlinearity, high parallelism, robustness, fault and failure tolerance, and generality, ANNs are now widely accepted in many disciplines to simulate complex factual problems. A successful example is to simulate real separation process and optimize separation conditions in modern separation science [15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Predicting and evaluating separation quality of micellar electrokinetic capillary chromatography by artificial neural networks

Liu

Zhang

2002

Electrophoresis

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Computer-aided optimization of micellar electrokinetic capillary chromatography (MEKC) separations was demonstrated by artificial neural networks (ANNs) using a Levenberg-Marquardt algorithm and an orthogonal experimental design. A novel criterion, named Q, for evaluating the separation quality of MEKC was firstly presented, which considered both separation selectivity and analysis time. MEKC separation conditions of seven plant hormones were then simulated and optimized using ANNs based on this novel criterion. The result was further compared to that obtained using ANNs based on a traditionally used criterion of overall normalization resolution (named r). Finally, the separation under optimum conditions predicted by ANNs using the criterion Q was compared to, and proved to be better than that obtained by empirical step-by-step optimization procedures. This method may also be adapted to other separation methods due to its generality.

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Comparison of prediction power between theoretical and neural-network models in ion-interaction chromatography

Cited by 36 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

Prediction of supercritical fluid chromatographic retention factors at different percents of organic modifiers in mobile phase

Predicting and evaluating separation quality of micellar electrokinetic capillary chromatography by artificial neural networks

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