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

This paper describes development of artificial neural network (ANN) retention model, which can be used for method development in variety of ion chromatographic applications. By using developed retention model it is possible both to improve performance characteristic of developed method and to speed up new method development by reducing unnecessary experimentation. Multilayered feed forward neural network has been used to model retention behaviour of void peak, lithium, sodium, ammonium, potassium, magnesium, calcium, strontium and barium in relation with the eluent flow rate and concentration of methasulphonic acid (MSA) in eluent. The probability of finding the global minimum and fast convergence at the same time were enhanced by applying a two-phase training procedure. The developed two-phase training procedure consists of both first and second order training. Several training algorithms were applied and compared, namely: back propagation (BP), delta-bar-delta, quick propagation, conjugate gradient, quasi Newton and Levenberg-Marquardt. It is shown that the optimized two-phase training procedure enables fast convergence and avoids problems arisen from the fact that every new weight initialization can be regarded as a new starting position and yield irreproducible neural network if only second order training is applied. Activation function, number of hidden layer neurons and number of experimental data points used for training set were optimized in order to insure good predictive ability with respect to speeding up retention modelling procedure by reducing unnecessary experimental work. The predictive ability of optimized neural networks retention model was tested by using several statistical tests. This study shows that developed artificial neural network are very accurate and fast retention modelling tool applied to model varied inherent non-linear relationship of retention behaviour with respect to mobile phase parameters.     

Application of artificial neural networks for gradient elution retention modelling in ion chromatography

Gradient elution in ion chromatography (IC) offers several advantages: total analysis time can be significantly reduced, overall resolution of a mixture can be increased, peak shape can be improved (less tailing) and effective sensitivity can be increased (because there is little variation in peak shape). More importantly, it provides the maximum resolution per time unit. The aim of this work was the development of a suitable artificial neural network (ANN) gradient elution retention model that can be used in a variety of applications for method development and retention modelling of inorganic anions in IC. Multilayer perceptron ANNs were used to model the retention behaviour of fluoride, chloride, nitrite, sulphate, bromide, nitrate and phosphate in relation to the starting time of gradient elution and the slope of the linear gradient elution curve. The advantage of the developed model is the application of an optimized two-phase training algorithm that enables the researcher to make use of the advantages of first- and second-order training algorithms in one training procedure. This results in better predictive ability, with less time required for the calculations. The number of hidden layer neurons and experimental data points used for the training set were optimized in terms of obtaining a precise and accurate retention model with respect to minimization of unnecessary experimentation and time needed for the calculation procedures. This study shows that developed, ANNs are the method of first choice for retention modelling of inorganic anions in IC.     

Application of Artificial Neural Network and Multiple Linear Regression Retention Models for Optimization of Separation in Ion Chromatography by Using Several Criteria Functions

This work focuses on problems regarding empirical retention modelling and optimization of separation in ion chromatography. Influences of eluent flow rate and concentration of eluent competing ion (OH^–) on separation of seven inorganic anions (fluoride, chloride, nitrite, sulphate, bromide, nitrate, and phosphate) were investigated. Artificial neural networks and multiple linear regression retention models in combination with several criteria functions were used and compared in global optimization process. It can be seen that general recommendations for optimization of separation in ion chromatography is application of chromatography exponential function criterion in combination with artificial neural networks retention model.     

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.     

Optimization of artificial neural network for retention modeling in high-performance liquid chromatography

An artificial neural network (ANN) model for the prediction of retention times in high-performance liquid chromatography (HPLC) was developed and optimized. A three-layer feed-forward ANN has been used to model retention behavior of nine phenols as a function of mobile phase composition (methanol-acetic acid mobile phase). The number of hidden layer nodes, number of iteration steps and the number of experimental data points used for training set were optimized. By using a relatively small amount of experimental data (25 experimental data points in the training set), a very accurate prediction of the retention (percentage normalized differences between the predicted and the experimental data less than 0.6%) was obtained. It was shown that the prediction ability of ANN model linearly decreased with the reduction of number of experiments for the training data set. The results obtained demonstrate that ANN offers a straightforward way for retention modeling in isocratic HPLC separation of a complex mixture of compounds widely different in pK_a and log K_ow values.     

From Isocratic Data to a Gradient Elution Retention Model in IC: An Artificial Neural Network Approach

Gradient elution is used in ion chromatography to achieve rapid analysis with reasonable separation. Optimization and prediction of the gradient is clearly a multidimensional problem, however. One approach to prediction of gradient retention behavior is based on isocratic experimentation. In this work, a gradient model for simultaneous prediction of the retention behavior of fluoride, chlorite, chloride, chlorate, nitrate, and sulfate ions, on the basis of isocratic experimental data, is proposed. An artificial neural network was used to predict isocratic results; the network was optimized with regard to the number of data in the training set (25) and number of neurons in the hidden layer (6). A slight systematic error was observed in the isocratic prediction, but this did not effect gradient prediction. Good predictions were achieved for all the anions investigated (average error 1.79%). Deviations were somewhat higher for prediction of sulfate retention than for the other anions, probably because of the higher charge and larger size of sulfate in comparison with the other ions examined.     

Prediction of retention times for anions in linear gradient elution ion chromatography with hydroxide eluents using artificial neural networks

The feasibility of using an artificial neural network (ANN) to predict the retention times of anions when eluted from a Dionex AS11 column with linear hydroxide gradients of varying slope was investigated. The purpose of this study was to determine whether an ANN could be used as the basis of a computer-assisted optimisation method for the selection of optimal gradient conditions for anion separations. Using an ANN with a (1, 10, 19) architecture and a training set comprising retention data obtained with three gradient slopes (1.67, 2.50 and 4.00 mM/min) between starting and finishing conditions of 0.5 and 40.0 mM hydroxide, respectively, retention times for 19 analyte anions were predicted for four different gradient slopes. Predicted and experimental retention times for 133 data points agreed to within 0.08 min and percentage normalised differences between the predicted and experimental data averaged 0.29% with a standard deviation of 0.29%. ANNs appear to be a rapid and accurate method for predicting retention times in ion chromatography using linear hydroxide gradients.     

Ion chromatographic method development for monitoring of seawater quality used in over-the-counter pharmaceutical industry

A methodology is developed for the analysis of inorganic anions (fluoride, chloride, bromide, sulphate) in seawater used for over-the-counter (OTC) nasal spray production. The eluent flow rate and concentration of eluent competing ions are optimised by using an artificial neural network resolution model in combination with normalised resolution product criterion function. The developed artificial neural network resolution model shows good predictive ability R2 > or = 0.9973. The determined ion chromatographic parameters enable baseline separation of all components of interest. By performing a validation procedure and a number of statistical tests, it is shown that the developed ion chromatographic method has superior performance characteristics: linearity R2 > or = 0.9993, recovery = 99.77-100.65%, repeatability RSD < or = 1.85%. This result proves that the proposed method can be used for routine quality assurance analysis in OTC pharmaceutical industry.     

Determination of Inorganic Ions in Fertilizer Industry Wastewater by Ion Chromatography

The aim of this work is development of methodology for analysis of inorganic cations (sodium, ammonium, potassium, magnesium and calcium) in fertilizer industry wastewater. Method development includes optimization of eluent flow rate and concentration of eluent competing ion in order to obtain optimal separation within reasonable analysis time. For that purpose artificial neural network retention model was developed and used in combination with normalized resolution product criteria function. Developed artificial neural network retention model shows good predictive ability R² ≥ 0.9983. The determined ion chromatographic parameters enable baseline separation of all components of interest. By performing validation procedure and number of statistical tests it is shown that developed ion chromatographic method has superior performance characteristic: linearity R² ≥ 0.9984, recovery = 99.81% − 99.44%, repeatability RSD ≤ 0.52%. That result proves that proposed method can be used for routine monitoring analysis in fertilizer industry.     

Vitamin U-bonded stationary phase in capillary ion chromatography

A vitamin U-bonded stationary phase was prepared and the retention behavior of inorganic anions was examined using ion chromatography. Inorganic anions were retained on the vitamin U-bonded stationary phase under acidic as well as neutral eluent conditions in the ion-exchange mode. The elution order of the examined anions under neutral eluent conditions was nearly the same as that observed in common ion exchange mode, while the elution order observed under acidic eluent conditions was completely different from that observed in common ion exchange mode. The retention of the analyte anions under the neutral eluent conditions was due to the sulfonium groups of the vitamin U, while protonated primary amino groups caused retention of the analyte anions with different selectivity under acidic conditions. The retention factor of the analyte anions increased with decreasing eluent concentration under both eluent conditions. The present system was applied to the determination of bromide and nitrate contained in seawater.     

Effect of eluent composition on retention behavior of anions in ion chromatography on anion-exchangers modified with heparin

Effects of eluent composition on retention behavior of inorganic anions have been investigated in ion chromatography using anion-exchangers modified with heparin. Both cation and anion of the eluent affected the retention of analyte anions and unusual retention behavior was observed on the modified stationary phase. The retention time of anions decreased with decreasing eluent concentration when sodium sulfate, magnesium sulfate and chlorides of alkali metals were used as the eluent, whereas it increased with decreasing eluent concentration when aluminum sulfate, copper sulfate and sulfuric acid were used as the eluent. The retention of nitrate increased in the order of Li⁺, Na⁺, K⁺, Rb⁺ and Cs⁺ when their chlorides were used as the eluent. When sodium perchlorate and chlorides of alkaline-earth metals were used as the eluent, the eluent should include heparin. Otherwise, the modifier was partially bled from the column.     

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

The 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%). Copyright © 2008 John Wiley & Sons, Ltd.     

Determination of iodide in seawater and edible salt by microcolumn liquid chromatography with poly(ethylene glycol) stationary phase

An ion chromatography method for rapid and direct determination of iodide in seawater and edible salt is reported. Separation was achieved using a laboratory-made C30 packed column (100 mm x 0.32 mm i.d.) modified with poly(ethylene glycol) (PEG). Effects of eluent composition on retention behavior of inorganic anions have been investigated. Both cation and anion of the eluent affected the retention of analyte anions. The retention time of anions increased with increasing eluent concentration when lithium chloride, sodium chloride, potassium chloride, sodium sulfate, magnesium sulfate were used as the eluent, while it decreased with increasing eluent concentration when ammonium sulfate was used as the eluent. The detection limit for iodide obtained by injecting 0.2 microl of sample was 9 microg/l (S/N = 3). The present method was successfully applied to the rapid and direct determination of iodide in seawater and edible salt samples. Partition may be involved in the present separation mode.     

整体柱离子相互作用色谱快速分析无机阴离子

研究了用硅胶整体柱和直接电导检测的离子相互作用色谱快速分析常见无机阴离子的方法。实验采用氢氧化四丁铵和邻苯二甲酸为淋洗液,讨论了包括淋洗液浓度、流速和pH对分离的影响。当以1.5 mmol/L氢氧化四丁铵和1.1 mmol/L邻苯二甲酸为淋洗液(pH 5.5),流速6 mL/min时,可以在1 min内分离Cl-、NO2-、Br-、NO3-、ClO3-、SO42-和I-7种阴离子。方法的检出限为0.3~1.9 mg/L,峰面积、峰高的相对标准偏差(RSD,n=5)分别为0.4%~2.2%和0.1%~1.5%。将该法用于测定矿泉水和地下水中的阴离子,加标回收率在97.9%~100.3%之间。     

Retention profiles and mechanism of anion separation on latex-based pellicular ion exchanger in ion chromatography

A retention model based on stoichiometric approach has been developed in order to describe analyte retention of anions on latex-based pellicular ion exchanger. The chromatographic process entails two stepwise and complex equilibria, first is ion-pair forming of analyte or eluent ion with ion-exchange sites under the effect of electrostatic forces due to the sulfonic layer behind the aminated functional groups of stationary phase. Second component is the ion-exchange between the analyte and eluent ions. As a new parameter of the fractional electrostatic coefficient of the ion exchange capacity was introduced to develop retention profiles of anions. Analysis of the dependence of the capacity factors on the eluent concentrations at different values of fractional coefficient shed light on the possible complex mechanism. Extensive experimental retention data were obtained for 14 anions (formate, acetate, propionate, pyruvate, lactate, chloride, nitrate, oxalate, malonate, succinate, tartarate, fumarate, maleate, sulphate) using hydroxide eluents of varying concentration. The ion-pair formation and ion-exchange selectivity constants for analyte and eluent species are determined using derived retention equation from experimental data by nonlinear iterative calculation. The model was utilized to predict retention data under elution conditions of practical importance. The predicted and obtained retention factors are in good agreement, which confirms the predictive power of the model.     

Artificial neural networks modeling in ultra performance liquid chromatography method optimization of mycophenolate mofetil and its degradation products

The study of experimental design in conjunction with artificial neural networks for optimization of isocratic ultra performance liquid chromatography method for separation of mycophenolate mofetil and its degradation products has been reported. Experimental design showed to be suitable for selection of experimental scheme, while Kennard‐Stone algorithm was used for selection of training data set. The input variables were column temperature and composition of mobile phase including percentage of acetonitrile, concentration of ammonium acetate in buffer, and its pH value. The retention factor of the most retentive component and selectivity factors were used as the dependent variables (outputs). In this way, artificial neural network has been applied as a predictable tool in solving a method optimization problem using small number of experiments. Network architecture and training parameters were optimized to the lowest root‐mean‐square error values, and the network with 5‐4‐4‐4 topology has been selected as the most predictable one. Predicted data were in good agreement with experimental data, and regression statistics confirmed good ability of trained network to predict compounds retention. The optimal chromatographic conditions included column temperature of 40°C, flow rate of 700 µl min⁻¹, 26% of acetonitrile and 9 mM ammonium acetate in mobile phase, and buffer pH of 5.87. The chromatographic analysis has been achieved within 5.2 min. The validation of the proposed method was also performed considering selectivity, linearity, accuracy, precision, limit of detection, and limit of quantification, and the results indicated that the method fulfilled all required criteria. The method was successfully applied to the analysis of commercial dosage form. Copyright © 2014 John Wiley & Sons, Ltd.     

Polyoxyethylene as the stationary phase in ion chromatography

The present article reviews the use of polyethylene glycol (PEG) or polyoxyethylene (POE) as the stationary phase for the separation of inorganic anions in ion chromatography and discusses about the retention mechanisms involved in the separation of anions on the novel stationary phases. PEG permanently coated on a hydrophobic stationary phase retained anions in the partition mode and allowed us to use high-concentration eluents because the retention of anions increased with increasing eluent concentration for most of the eluents. This situation was convenient to determine trace anions contained in seawater samples without any disturbance due to matrices. Chemically bonded POE stationary phases retained not only anions but also cations. Anions were retained in the ion-exchange mode, although POE chains possess no ion exchange sites. The retention behavior suggested that eluent cations could be trapped among multiple POE chains via ion-dipole interaction, and that the trapped cations worked as the anion-exchange sites. Anions could be separated using crown ether, i.e., cyclic POE, as the eluent additive with a hydrophobic stationary phase, where analyte anions were retained via electrostatic interaction with the eluent cation trapped on the crown ether.     

Critical comparison of retention models for the optimisation of the separation of anions in ion chromatography: II. Suppressed anion chromatography using carbonate eluents

Seven theoretical retention models, namely the linear solvent strength model (using the dominant equilibrium approach and competing ion effective charge approach), the dual eluent species model, the Kuwamoto model, the extended dual eluent species model, the multiple species eluent/analyte model and the empirical end-points model, were used to describe the retention behaviour of anions in suppressed ion chromatography (IC). An extensive set of experimental retention data was gathered for 24 anions (fluoride, formate, bromate, chloride, hexanesulfonate, bromide, chlorate, nitrate, iodide, thiocyanate, perchlorate, sulfite, succinate, sulfate, tartrate, selenate, oxalate, tungstate, phthalate, molybdate, chromate, thiosulfate and phosphate) on a Dionex AS4A-SC column using carbonate eluents of varying concentration and HCO₃⁻:CO₃²⁻ ratios. Statistical comparison of the predicted and experimentally obtained retention factors showed that the performance of the theoretical models improved with the complexity of the model. However the empirical model (in which a linear relationship is assumed between the logarithm of retention factor and the logarithm of eluent strength, but the slope is determined empirically) gave the most consistent performance across the widest range of anions. The empirical end-points model was also shown to be the most satisfactory model due to its low knowledge requirements and easy solution. Compared with non-suppressed IC (see Part I), the retention behaviour in suppressed IC was found to be easier to model by all retention models.     

Separation of inorganic anions on a triazole-functionalized ion exchanger in ion chromatography

The retention behavior of inorganic anions on a triazole-based stationary phase was first examined in ion chromatography. It was initially designed for hydrophilic interaction liquid chromatography and was simply prepared by introducing the triazole groups onto the surface of silica gel via click chemistry. Effective separation of common inorganic anions, including iodate, chloride, bromide, nitrate and iodide, was achieved with Na(2)SO(4) eluent. The logarithm of the retention factor of analytes was observed to be linear with the logarithm of the eluent concentration, and the slopes of the plots were almost the same as those of the ideal theoretical value. The eluent pH value in the range of 3.4-7.0 had little effect on the separation. The utility of the column was demonstrated for the determination of UV-absorbing anions in saliva and tap water.