Capillary electrophoresis of inorganic ions: an update

This review as a sequel of three earlier similar reports gives a summary of the progress and significant methodological developments, starting from 2002, in the use of capillary electrophoresis (CE) for inorganic ion analysis. As substantiated by the illustrative number of relevant references, improvements in sensitivity achieved both in and outside a CE system, advances in manipulating the separation selectivity, novel hardware configurations, and system performance innovations are continually being reported over the review period. Specifically viewed are the recent advancements in elemental (bio)speciation analysis, which remains one of the most fertile areas of CE research, as well as in three recently booming research topics: contactless conductivity detection, separations on microchips, and transient isotachophoretic preconcentration. A state-of-the-art picture of technique's potentialities within the field of interest presented here demonstrates that CE has become recognized and is growing in acceptance as a reliable alternative to traditional analytical methods such as high-performance liquid chromatography (HPLC).     

Artificial neural networks for computer-aided modelling and optimisation in micellar electrokinetic chromatography

The separation process in capillary micellar electrochromatography (MEKC) can be modelled using artificial neural networks (ANNs) and optimisation of MEKC methods can be facilitated by combining ANNs with experimental design. ANNs have shown attractive possibilities for non-linear modelling of response surfaces in MEKC and it was demonstrated that by combining ANN modelling with experimental design, the number of experiments necessary to search and find optimal separation conditions can be reduced significantly. A new general approach for computer-aided optimisation in MEKC has been proposed which, because of its general validity, can also be applied in other separation techniques.     

Quantitation in multianalyte overlapping peaks from capillary electrophoresis runs using artificial neural networks

The potentiality of artificial neural networks for multicomponent analysis in unresolved peaks from capillary electrophoresis (CE) is evaluated. The system chosen consists of mixtures of three ebrotidine metabolites, which cannot be successfully separated by CE. Data selected for analysis consist of UV spectra taken at the maximum of the CE peak. The most dissimilar analyte, in terms of spectral differences, is accurately quantitated in any type of mixture with an overall prediction error of 5%. Because of the strong interference of the two most overlapped compounds, a preliminary procedure for spectral data filtering based on principal component analysis is performed to improve their quantitation.     

Prediction of electrophoretic mobilities of sulfonamides in capillary zone electrophoresis using artificial neural networks

Artificial neural networks (ANNs) were successfully developed for the modeling and prediction of electrophoretic mobility of a series of sulfonamides in capillary zone electrophoresis. The cross-validation method was used to evaluate the prediction ability of the generated networks. The mobility of sulfonamides as positively charged species at low pH and negatively charged species at high pH was investigated. The results obtained using neural networks were compared with the experimental values as well as with those obtained using the multiple linear regression (MLR) technique. Comparison of the results shows the superiority of the neural network models over the regression models.     

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.     

"In-house likeness": comparison of large compound collections using artificial neural networks

Binary classification models able to discriminate between data sets of compounds are useful tools in a range of applications from compound acquisition to library design. In this paper we investigate the ability of artificial neural networks to discriminate between compound collections from various sources aiming at developing an "in-house likeness" scoring scheme (i.e. in-house vs external compounds) for compound acquisition. Our analysis shows atom-type based Ghose-Crippen fingerprints in combination with artificial neural networks to be an efficient way to construct such filters. A simple measure of the chemical overlap between different compound collections can be derived using the output scores from the neural net models.     

Prediction of electrophoretic mobilities of alkyl- and alkenylpyridines in capillary electrophoresis using artificial neural networks

A gas chromatographic method to determine thymol, eucalyptol (cineole), menthol and camphor residues in honey and beeswax is proposed. To isolate the compounds, three methods involving liquid-liquid extraction with methylene chloride, distillation, or solid-phase extraction on octadecylsilica cartridges can be used. The GC separation is carried out on a 60 m x 0.53 mm Stabilwax DA capillary column, using a flame ionization detector. The method is applied to the analysis of natural honey and also honey and beeswax samples from beehives treated with the above compounds.     

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.     

Evaluation of calibration data in capillary electrophoresis using artificial neural networks to increase precision of analysis

An RP-HPLC study for the pKa determination of a series of basic compounds related to caproctamine, a dibenzylaminediamide reversible inhibitor of acetylcholinesterase, is reported. The 2-substituted analogues, bearing substituents with different electronegativity, were analysed by RP-HPLC by using C18 C4 stationary phases with a mobile phase consisting of mixture of acetonitrile and triethylamine phosphate buffer (pH range comprised between 4 and 10). Typical sigmoidal curves were obtained, showing the dependence of the capacity factors upon pH. In general, the retention of the investigated basic analytes increased with increasing of the pH. The inflection point of the pH sigmoidal dependence was used for the dissociation constant determination at a fixed acetonitrile percentage. When plotting pKa vs. percent of acetonitrile in the mobile phase for two representative compounds, linear regression were obtained: the y intercept gave the aqueous pKa(w). The pKa estimation by HPLC method was found to be useful to underline the difference of benzylamine basicity produced by the ortho aromatic substituents. The variation of pKa values (6.15-7.80) within the series of compounds was correlated with the electronic properties of the ortho-substituents through the Hammett sigma parameter, whereas the ability of substituents to accept H-bond was found to play a role in determining the conformational behavior of the molecules.     

毛细管区带电泳技术快速分离检测爆炸残留物中的无机离子

利用间接紫外毛细管区带电泳方法完成了对爆炸残留物中7种无机离子(K+,NH+4,NO-2,NO-3,SO2-4,ClO-3,ClO-4)的分离检测。阳离子测定采用的缓冲体系为10 mmol/L吡啶（pH 4.5）-3 mmol/L冠醚,K+和NH+4在2.6 min内达到基线分离,检出限分别为0.25 mg/L和0.10 mg/L(S/N=3)。阴离子测定采用的缓冲体系为40 mmol/L硼酸-1.8 mmol/L重铬酸钾-2 mmol/L硼酸钠（pH 8.6）,氢氧化四甲铵为电渗流改性剂,5种阴离子在4.6 min内达到基线分离,检出限为0.10～1.85 mg/L。该方法已成功地应用于实际爆炸物样品种类的判定分析,取得了很好的结果。     

Flow-injection potentiometric determination of trace concentrations of chloride ions in low-mineralized natural waters

A procedure is developed for flow-injection potentiometric determination of trace chloride ions in low-mineralized waters and atmospheric precipitations. The procedure excludes the stage of preliminary ion-exchange concentration. The quantification limit in a 5-min analysis is 0.4 mg/L.     

Capillary electrophoresis analysis of inorganic cations in post-blast residue extracts applying a guanidinium-based electrolyte and bilayer-coated capillaries

A new CE method was developed for the identification and quantitation of inorganic cations in post‐blast residues. The simultaneous analysis in 20 min total runtime of eight cations in post‐blast residues (ammonium, potassium, monomethylammonium, calcium, sodium, magnesium, strontium), plus lithium cation as the internal reference, was carried out with a BGE involving a non‐CMR (carcinogenic, mutagenic, and harmful to reproduction) chromophore (guanidinium cation) and a double‐layer modified capillary (hexadimethrine bromide/polyvinylsulfonate). A study of UV detection conditions using guanidinium ion as the probe led us to set the analysis and reference wavelengths and their associated bandwidths as well as the probe concentration fixed at 15 mM. The successive multiple ionic‐polymer layer approach limited the cation adsorption on capillary wall and improved the EOF stability. These caused a significant improvement in method repeatability. Intermediate precisions were 2.4% for corrected areas and 1.3% for normalized migration times. Limits of detection close to 1 mg/L for all cations were obtained. The matrix effects were studied with chemometric approach for different matrices representative of those collected after explosion. Tests with blank matrix extracts of soil, cloth, and with simulated matrix extract containing 800 mg/L Ca²⁺ and 500 mg/L Fe²⁺ were carried out and no significant matrix effects were observed. Finally, analyses of real residues collected after cash dispenser and homemade firework explosions demonstrate excellent correlation between the CE results and those obtained with the ion chromatography method used routinely.     

Capillary zone electrophoresis in non-aqueous solutions: pH of the background electrolyte

Although the establishment of a pH scale and the determination of the pH in water is not problematic, it is not a straightforward task in non-aqueous solvents. As capillary zone electrophoresis (CZE) in organic solvents has gained increasing interest, it seems to be valuable to re-discuss the concept of the pH in such media, especially pointing to those aspects, which make pH measurement uncertain in non-aqueous solvents. In this review, the relevant aspects when dealing with primary standard (PS) and secondary standard (SS) as recommended by the International Union of Pure and Applied Chemistry (IUPAC), and the usage of the operational pH are discussed with special emphasis to non-aqueous solvents. Here, different liquid junction potentials, incomplete dissociation of the electrolytes (especially in solvents with low or moderate relative permittivity) and the occurrence of homo- and heteroconjugation must be taken into account. Problems arising in capillary zone electrophoresis practice are addressed, e.g. when the background electrolyte (BGE) consists of organic solvents, but the measuring electrode (normally the glass electrode) is calibrated with aqueous buffers, and the liquid junction potentials between the solvents do not cancel each other. The alternative concept of establishing a certain pH is described, using mixtures of reference acids or bases with known pKa in the organic solvent, and their respective salts, at a certain concentration ratio, relying to the Henderson-Hasselbalch equation. Special discussion is directed to those organic solvents most common in capillary zone electrophoresis, methanol (MeOH) and acetonitrile (ACN), but other solvents are included as well. The potential significance of small amounts of water present in the organic solvent on changes in pKa values, and thus on the pH of the buffering components is pointed out.     

Determination of the composition of sialoliths composed of carbonate apatite and albumin using artificial neural networks

The determination of the components of the sialoliths is important both from the point of view of chances for a successful medical treatment of the patients and because the prevention of further re-occurrence of sialolithiasis depends upon the knowledge of the nature of the constituents of the concrements. Despite the fact that infrared spectroscopy is widely used for the determination of the composition of sialoliths, urinary calculi and bladder stones, we found no data for any chemometric method developed for such purposes. Here, a method is presented for quantitative determination of the content of salivary calculi composed of albumin and carbonate apatite (one of the most often found constituents in the analyzed calculi from the patients from Macedonia) using artificial neural networks (ANN). The results were checked on real samples using the standard addition method. The precision of the method was estimated using the relative standard deviation, which shows that it is suitable for routine analysis.     

Capillary electrophoresis using copolymers of different composition as physical coatings: a comparative study

In this work, a comparative study on the use of different polymers as physically adsorbed coatings for CE is presented. It is demonstrated that the use of ad hoc synthesized polymers as coatings allows tailoring the EOF in CE increasing the flexibility of this analytical technique. Namely, different polymers were synthesized at our laboratory using different percentages of ethylpyrrolidine methacrylate (EpyM) and N,N-dimethylacrylamide (DMA). Thus, by modifying the percentage of EpyM and DMA monomers it is possible to manipulate the positive charge of the copolymer, varying the global electrical charge on the capillary wall and with that the EOF. These coated capillaries are obtained by simply flushing a given EpyM-DMA aqueous solution into bare silica capillaries. It is shown that by using these coated capillaries at adequate pHs, faster or more resolved CE separations can be achieved depending on the requirements of each analysis. Moreover, it is demonstrated that these coated capillaries reduce the electrostatic adsorption of basic proteins onto the capillary wall. Furthermore, EpyM-DMA coatings allow the reproducible chiral separation of enantiomers through the partial filling technique (PFT). The EpyM-DMA coated capillaries are demonstrated to provide reproducible EOF values independently of the pH and polymer composition with%RSD values lower than 2% for the same day. It is also demonstrated that the coating procedure is reproducible between capillaries. The compatibility of this coating protocol with CE in microchips is discussed.     

Sustained modelling ability of artificial neural networks in the analysis of two pharmaceuticals (dextropropoxyphene and dipyrone) present in unequal concentrations

An improvement is presented on the simultaneous determination of two active ingredients present in unequal concentrations in injections. The analysis was carried out with spectrophotometric data and non-linear multivariate calibration methods, in particular artificial neural networks (ANNs). The presence of non-linearities caused by the major analyte concentrations which deviate from Beer's law was confirmed by plotting actual vs. predicted concentrations, and observing curvatures in the residuals for the estimated concentrations with linear methods. Mixtures of dextropropoxyphene and dipyrone have been analysed by using linear and non-linear partial least-squares (PLS and NPLSs) and ANNs. Notwithstanding the high degree of spectral overlap and the occurrence of non-linearities, rapid and simultaneous analysis has been achieved, with reasonably good accuracy and precision. A commercial sample was analysed by using the present methodology, and the obtained results show reasonably good agreement with those obtained by using high-performance liquid chromatography (HPLC) and a UV-spectrophotometric comparative methods.     

Optimization of solid-phase extraction using artificial neural networks in combination with experimental design for determination of resveratrol by capillary zone electrophoresis in wines

Solid-phase extraction (SPE) is often used for preconcentration of analytes from biological samples. Such an analytical step requires optimization for obtaining reliable results. Optimization in analytical chemistry is traditionally still often done with relaxation method, when an optimal value of a single variable is searched for (single variable approach (SVA)). However, if the optimized procedure is complex, there is a danger not to find the real optimum by SVA. Therefore, more advanced optimization approaches should be applied-multivariable approach (MVA). Applying MVA optimization and finding the real optimum, better experimental conditions are obtained and thus, time, chemicals and analytical procedure cost can be served. Nowadays, using artificial neural networks (ANN's) in combination with MVA is rapidly expanding. In this work, the optimization of SPE using relaxation method (SVA) and optimization by ANN's in combination with experimental design (MVA) are compared and latter approach is practically illustrated. Advantages of MVA over SVA for optimization are discussed. The prediction of the optimal SPE conditions for determination cis- and trans-resveratrol in Australian wines by capillary zone electrophoresis is described and the improvement of efficiency of SPE using MVA is confirmed.     

A comparative study of laser induced breakdown spectroscopy analysis for element concentrations in aluminum alloy using artificial neural networks and calibration methods

A comparative study of analysis methods (traditional calibration method and artificial neural networks (ANN) prediction method) for laser induced breakdown spectroscopy (LIBS) data of different Al alloy samples was performed. In the calibration method, the intensity of the analyte lines obtained from different samples are plotted against their concentration to form calibration curves for different elements from which the concentrations of unknown elements were deduced by comparing its LIBS signal with the calibration curves. Using ANN, an artificial neural network model is trained with a set of input data of known composition samples. The trained neural network is then used to predict the elemental concentration from the test spectra. The present results reveal that artificial neural networks are capable of predicting values better than traditional method in most cases.