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.     

Optimization of artificial neural networks used for retention modelling in ion chromatography

The aim of this work is the development of an artificial neural network model, which can be generalized and used in a variety of applications for retention modelling in ion chromatography. Influences of eluent flow-rate and concentration of eluent anion (OH-) on separation of seven inorganic anions (fluoride, chloride, nitrite, sulfate, bromide, nitrate, and phosphate) were investigated. Parallel prediction of retention times of seven inorganic anions by using one artificial neural network was applied. MATLAB Neural Networks ToolBox was not adequate for application to retention modelling in this particular case. Therefore the authors adopted it for retention modelling by programming in MATLAB metalanguage. The following routines were written; the division of experimental data set on training and test set; selection of data for training and test set; Dixon's outlier test; retraining procedure routine; calculations of relative error. A three-layer feed forward neural network trained with a Levenberg-Marquardt batch error back propagation algorithm has been used to model ion chromatographic retention mechanisms. The advantage of applied batch training methodology is the significant increase in speed of calculation of algorithms in comparison with delta rule training methodology. The technique of experimental data selection for training set was used allowing improvement of artificial neural network prediction power. Experimental design space was divided into 8-32 subspaces depending on number of experimental data points used for training set. The number of hidden layer nodes, the number of iteration steps and the number of experimental data points used for training set were optimized. This study presents the very fast (300 iteration steps) and very accurate (relative error of 0.88%) retention model, obtained by using a small amount of experimental data (16 experimental data points in training set). This indicates that the method of choice for retention modelling in ion chromatography is the artificial neural network.     

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.     

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.     

Retention of some glucosinolates in anion exchange chromatography. Part I: Qualitative study on a silica-trialkylammonium exchanger

Summary The retention and separation of glucosinolates, as organic anions, were studied on a silica-based strong anion exchanger under isocratic elution conditions. All glucosinolates carry the same functional ionic group (-OSO ₃ ^– ), however they do not have the same retention in anion exchange chromatography. The plots of capacity factors of organic anions versus the reciprocal of eluent ion concentration show good linearity. From the slope and y-intercept data the major retention mechanisms are interpreted as ion exchange and reversed-phase interactions. The effects of nature and concentration of the eluent ion and the influence of organic modifier addition to the aqueous buffered mobile phase are also investigated. Direct and indirect UV detection were used.Our results open the way for the development of new systems for intact glucosinolate analysis which are easier to use than the present ion-pairing chromatographic method.     

Prediction of the chromatographic behaviour for a series of diuretic compounds

Summary The proportion of organic modifier and the pH of the acetonitrile-water mixtures used as mobile phases were optimized in order to separate a group of diuretic compounds covering a wide range of physyco-chemical properties. The Linear Solvation Energy Relationship (LSER) formalism based either on the multiparameter π^*, β and α scales or the single solvent polarity parameterE _T ^N , have been used to predict their chromatographic behaviour as a function of the percentage of acetonitrile in the eluent. Moreover, correlation established between retention and pH of the aqueous-organic mobile phases have been used to predict the chromatographic behaviour of the diuretic compounds studied as a function of the eluent pH. Linear correlation between a function of the eluent pH. Linear correlation between the chromatographic retention and theE _T ^N polarity parameter of mobile phases containing different percentages of organic modifier has been obtained Based on the knowledge of the acid-base dissociation constant the relation between retention and mobile phase pH has also been linearized. These relationship allowed an important reduction of the experimental retention data needed for developing a given separation and a great improvement in chromatographic optimization schemes.     

IC Determination of Halide Impurities in Ionic Liquids

An ion chromatographic (IC) method has been developed for determination of trace levels of halide impurities in various types of ionic liquids (ILs). The advantage of this method is that all relevant halide species can be measured in a single chromatographic analysis. Separation of halides was performed on a Dionex AS9-HC column using an eluent consisting of 20 mM NaOH and 10% (v/v) acetonitrile, delivered at 1.5 mL min⁻¹. Using this eluent, fluoride, chloride and bromide were well resolved from each other, but iodide was co-eluted with tetrafluoroborate (BF₄⁻) present as a counter-anion in tetrafluoroborate-based ILs. The same eluent was also used successfully for the determination of halides in highly hydrophobic ILs, such as those based on bis-(trifluoromethanesulfonyl)imide (TFSI⁻) and bis-perfluoroethylsulfonylimide (BETI⁻). In this case, 50% (v/v) acetonitrile aqueous was needed to dissolve the sample before injection, and this did not adversely affect the separation. Detection limits in the measured solution were 0.1, 0.2 and 1.0 ppm for chloride, bromide and iodide, respectively, by conductivity detection, and 0.02 ppm for iodide by UV detection.     

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).     

Portable,lightweight, low power,ion chromatographic system with open tubular capillary columns

Basic operation principles of a lightweight, low power, low cost, portable ion chromatograph utilizing open tubular ion chromatography in capillary columns coated with multi-layer polymeric stationary phases are demonstrated. A minimalistic configuration of a portable IC instrument was developed that does not require any chromatographic eluent delivery system, nor sample injection device as it uses gravity-based eluent flow and hydrodynamic sample injection adopted from capillary electrophoresis. As a detection device, an inexpensive commercially available capacitance sensor is used that has been shown to be a suitable substitute for contactless conductivity detection in capillary separation systems. The built-in temperature sensor allows for baseline drift correction typically encountered in conductivity/capacitance measurements without thermostating device. The whole instrument does not require any power supply for its operation, except the detection and data acquisition part that is provided by a USB port of a Netbook computer. It is extremely lightweight, its total weight including the Netbook computer is less than 2.5 kg and it can be continuously operated for more than 8 h. Several parameters of the instrument, such as detection cell design, eluent delivery systems and data treatment were optimized as well as the composition of eluent for non-suppressed ion chromatographic analysis of common inorganic cations (Na⁺, NH₄⁺, K⁺, Cs⁺, Ca²⁺, Mg²⁺, transition metals). Low conductivity eluents based on weakly complexing organic acids such as tartaric, oxalic or pyridine-2,6-dicarboxylic acids were used with contactless capacitance detection for simultaneous separation of mono- and divalent cations. Separation of Na⁺ and NH₄⁺ cations was optimized by addition of 18-crown-6 to the eluent. The best separation of 6 metal cations commonly present in various environmental samples was accomplished in less than 30 min using a 1.75 mM pyridine-2,6-dicarboxylic acid and 3 mM 18-crown-6 eluent with excellent repeatability (below 2%) and detection limits in the low micromolar range. The analysis of field samples is demonstrated; the concentrations of common inorganic cations in river water, mineral water and snow samples were determined.     

Use of the Synthesized Titania Monolith to Determine Benzoic Acid and Vanillin in Foodstuffs by HPLC

A simple and reliable HPLC method for the determination of benzoic acid and vanillin in food samples has been developed, in which a pure titania monolithic column synthesized through a template-free sol-gel synthesis route was used as chromatography column. To fully understand the retention mechanism of benzoic acid and vanillin on titania, acetonitrile (ACN) percentage, buffer concentration, and buffer pH of the mobile phase were investigated. The retention mechanism of benzoic acid and vanillin on the titania monolith column belongs to hydrophilic interaction and ligand exchange. When the high %ACN and appropriate acetate existed in eluent, the hydrophilic interaction was the dominant retention mode. Benzoic acid and vanillin in preserved fruit and jelly samples were successfully determined and quantitative analysis was carried out by external standard method with correlation coefficient (R ² ) of 0.9994 for benzoic acid and 0.9989 for vanillin. The relative standard deviations (RSDs) of benzoic acid and vanillin were 0.94% and 1.50%, respectively. The developed titania-based HPLC method is simple, rapid, accurate, and competent for the separation of polar and hydrophilic compounds, and this work has also promoted the application of titania monolith in chromatographic separation.     

Interpretation of chromatographic behavior of ions based on the electric double-layer theory

A retention model based on electrostatic theories is applied to the analysis of the ion-exchange chromatographic separation of ions. The adsorption of counterions and the ion-pair formation between ion-exchange sites and counterions are included in the model; these represent separation selectivity. A nonstoichiometric contribution, the accumulation of ions in an electrical double layer, is also involved in the model. The retention of ions is calculated by assuming these ionic properties for both eluent and solute ions. The comparison of calculated retention factors with experimental values gives insight into the ion-exchange nature of ions; e.g. a strongly adsorbed ion should have higher ion-pair formation ability, and vice versa.     

Development of a novel RP‐HPLC method for the efficient separation of aripiprazole and its nine impurities

The development of an RP‐HPLC method for the separation of aripiprazole and its nine impurities was performed with the use of partial least squares regression, response surface plot methodology, and chromatographic response function. The HPLC retention times and computed molecular parameters of the aripiprazole and its nine impurities were further used for the quantitative structure–retention relationship (QSRR) study. The QSRR model, R²: 0.899, Q²: 0.832, root mean square error of estimation: 4.761, root mean square error of prediction: 6.614, was developed. Very good agreement between the predicted and observed retention times (t_R) for three additional aripiprazole impurities (TC1–TC3) indicated the high prediction potential of the QSRR model for t_R evaluation of other aripiprazole impurities and metabolites. The developed HPLC method is the first reported method for the efficient separation of aripiprazole and its nine impurities, which could be used for the analysis of an additional three aripiprazole impurities (TC1–TC3).     

Optimisation of the separation of herbicides by linear gradient high performance liquid chromatography utilising artificial neural networks

An artificial neural network (ANN) was employed to model the chromatographic response surface for the linear gradient separation of 10 herbicides that are commonly detected in storm run-off water in agricultural catchments. The herbicides (dicamba, simazine, 2,4-D, MCPA, triclopyr, atrazine, diuron, clomazone, bensulfuron-methyl and metolachlor) were separated using reverse phase high performance liquid chromatography and detected with a photodiode array detector. The ANN was trained using the pH of the mobile phase and the slope of the acetonitrile/water gradient as input variables. A total of nine experiments were required to generate sufficient data to train the ANN to accurately describe the retention times of each of the herbicides within a defined experimental space of mobile phase pH range 3.0-4.8 and linear gradient slope 1-4% acetonitrile/min. The modelled chromatographic response surface was then used to determine the optimum separation within the experimental space. This approach allowed the rapid determination of experimental conditions for baseline resolution of all 10 herbicides. Illustrative examples of determination of these components in Milli-Q water, Sydney mains water and natural water samples spiked at 0.5-1 μg/L are shown. Recoveries were over 70% for solid-phase extraction using Waters Oasis^® HLB 6 cm³ cartridges.     

Separation of organic and inorganic arsenic species by HPLC-ICP-MS

The HPLC separation of eight anionic, cationic or neutral arsenic species (arsenite, arsenate, monomethylarsonic acid, dimethylarsinic acid, arsenobetaine, arsenocholine, trimethylarsine oxide and tetramethylarsonium ion) on a high-capacity, anion-exchange column (Ion Pac AS 7, Dionex) was studied. The separation was performed during one run with a nitric acid gradient ranging from pH 4–1.3. The influence of sodium dodecyl sulfate (SDS), sodium octyl sulfate (SOS) and 1,2-benzenedisulfonic acid (BDSA) as ion pairing eluent modifiers was investigated. In addition the effect of elevated temperatures (30 to 40 °C) was studied. The best results were obtained at room temperature of 20 °C with 0.05 mM benzenedisulfonic acid as the eluent modifier. The chromatograph was connected to an ICP-MS via a cross-flow nebulizer. Detection limits obtained with the optimized chromatographic separation were 0.16–0.60 μg As L^–1 for different species. The proposed speciation method was applied to the determination of arsenic species in the DORM-2 reference material (Dogfish Muscle) and in aqueous extracts of mushrooms collected on arsenic contaminated ground. Received: 3 August 1998 / Revised: 17 September 1998 / Accepted: 21 September 1998     

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.     

A novel silica based click lysine anion exchanger for ion exchange chromatography

Ion chromatography (IC) is one of the most powerful analysis technologies for the determination of charged compounds. A novel click lysine stationary phase was prepared via Cu(I) catalyzed alkyne-azide 1,3-dipolar cycloaddition (CuAAC) and applied to the analysis of inorganic ions. The chromatographic evaluation demonstrated good performance (e.g. the plate number of thiocyanate is ～50,000 plates m(-1)) and effective separation ability for the common inorganic anions with aqueous Na(2)SO(4) eluent. The separation mechanism was observed to be mainly dominated by ion exchange interaction. The retention of these analytes is highly dependent on the pH value of eluent. Compared with the lysine stationary phase prepared via the conventional manner, the click lysine exchanger demonstrated shorter retention time and better ion separation characteristics under the same chromatographic conditions, which is a great advantage for rapid separation and analysis of inorganic ions.