Mobilization of electroosmotic flow markers in capillary zone electrophoresis

UV-absorbing neutral substances are commonly used as markers of mean electroosmotic flow in capillary electrophoresis for their zero electrophoretic mobility in an electric field. However, some of these markers can interact with background electrolyte components and migrate at a different velocity than the electroosmotic flow. Thus, we tested 11 markers primarily varying in their degree of methylation and type of central atom in combination with five background electrolyte cations differing in their ionic radii and surface charge density, measuring the relative electrophoretic mobility using thiourea as a reference marker. Our results from this set of experiments showed some general trends in the mobilization of the markers based on the effects of marker structure and type of background electrolyte cation on the relative electrophoretic mobility. As an example, the effects of an inadequate choice of marker on analyte identification were illustrated in the electrophoretic separation of glucosinolates. Therefore, our findings may help electrophoretists appropriately select electroosmotic flow markers for various electrophoretic systems.     

Modeling of combined electroosmotic and capillary flow in microchannels

In the present study, theoretical model for the transient response of a capillary flow under the combined effects of electroosmotic and capillary forces at low Reynolds number is presented. The governing equation is derived based on the balance among the electrokinetic, surface, viscous and gravity forces. A non-dimensional transient governing equation for the penetration depth as a function of time is obtained by normalizing the viscous, gravity and electroosmotic forces with surface tension force. A new non-dimensional group for the electroosmotic force, E_o, is obtained through the non-dimensional analysis. This new non-dimensional group is a representation of combined electroosmosis and surface tension, i.e., capillarity. The numerical solution of governing equation is obtained to study the effect of different operating parameters on the flow front transport. In a combined flow, it is observed that the flow with positive and low negative magnitude E_o numbers, the attainment of equilibrium penetration depth is similar to a capillary flow. In case of high negative magnitude E_o numbers, complete filling of the channel is observed. The electrolyte with lower permittivity delays the progress of the flow front whereas a large EDL transports the electrolyte quickly. Higher viscous and gravity forces also delay the transport process in the combined flow. This model suggests that in combined flow the electrokinetic parameters also play an important role on the capillary flow and experiments are required to confirm this electrokinetic effect on capillary transport.     

Evaluation of electroosmotic markers in aqueous and nonaqueous capillary electrophoresis

The most common method to determine the EOF in CE is to measure the migration time for a neutral marker. In this study, 12 compounds (three novel and some previously used) were investigated as EOF markers in aqueous and nonaqueous BGEs. In the aqueous buffer systems (ammonium acetate, sodium phosphate, and sodium borate) the evaluation included a wide pH range (2–12). Two BGEs contained chiral selectors (sulphated‐β‐CD, (?)‐diketogulonic acid) and one that contained a micellar agent (SDS) were included in the study. The majority of the evaluated compounds were found to migrate with the EOF in the water‐based BGEs and are thus useful as EOF markers. However, in the SDS‐based BGE only four of the compounds (acetone, acrylamide, DMSO, and ethanol) were found to be applicable. In the nonaqueous BGEs 11 markers (acetone, acetophenone, acrylamide, anthracene, benzene, 4‐(4‐methoxybenzylamino)‐7‐nitro‐2,1,3‐benzoxadiazole, benzyl alcohol, 2,5‐diphenyloxazole, ethanol, flavone, and mesityl oxide) seemed to be functional as EOF markers. Even though several of the evaluated compounds can be used as EOF markers in the investigated BGEs, the authors would recommend the use of acrylamide as a general marker for UV detection. Furthermore, the four fluorescent markers (of which three were novel) gave RSD values equal to the other markers and can be used for the determination of the EOF in CE or microchip CE with fluorescence detection.     

An experimental design approach employing artificial neural networks for the determination of potential endocrine disruptors in food using matrix solid-phase dispersion

Matrix solid-phase dispersion (MSPD) as a sample preparation method for the determination of two potential endocrine disruptors, linuron and diuron and their common metabolites, 1-(3,4-dichlorophenyl)-3-methylurea (DCPMU), 1-(3,4-dichlorophenyl) urea (DCPU) and 3,4-dichloroaniline (3,4-DCA) in food commodities has been developed. The influence of the main factors on the extraction process yield was thoroughly evaluated. For that purpose, a 3^(4–1) fractional factorial design in further combination with artificial neural networks (ANNs) was employed. The optimal networks found were afterwards used to identify the optimum region corresponding to the highest average recovery displaying at the same time the lowest standard deviation for all analytes. Under final optimal conditions, potato samples (0.5 g) were mixed and dispersed on the same amount of Florisil. The blend was transferred on a polypropylene cartridge and analytes were eluted using 10 ml of methanol. The extract was concentrated to 50 μl of acetonitrile/water (50:50) and injected in a high performance liquid chromatography coupled to UV–diode array detector system (HPLC/UV–DAD). Recoveries ranging from 55 to 96% and quantification limits between 5.3 and 15.2 ng/g were achieved. The method was also applied to other selected food commodities such as apple, carrot, cereals/wheat flour and orange juice demonstrating very good overall performance.     

Optimization of the separation of a group of triazine herbicides by micellar capillary electrophoresis using experimental design and artificial neural networks

The micellar electrokinetic chromatography separation of a group of triazine compounds was optimized using a combination of experimental design (ED) and artificial neural network (ANN). Different variables affecting separation were selected and used as input in the ANN. A chromatographic exponential function (CEF) combining resolution and separation time was used as output to obtain optimal separation conditions. An optimized buffer (19.3 mM sodium borate, 15.4 mM disodium hydrogen phosphate, 28.4 mM SDS, pH 9.45, and 7.5% 1-propanol) provides the best separation with regard to resolution and separation time. Besides, an analysis of variance (ANOVA) approach of the MEKC separation, using the same variables, was developed, and the best capability of the combination of ED-ANN for the optimization of the analytical methodology was demonstrated by comparing the results obtained from both approaches. In order to validate the proposed method, the different analytical parameters as repeatability and day-to-day precision were calculated. Finally, the optimized method was applied to the determination of these compounds in spiked and nonspiked ground water samples.     

The analysis of seasonal air pollution pattern with application of neural networks

Air pollution monitoring includes measuring the concentrations of air contaminants such as nitrogen dioxide, sulfur dioxide, some polycyclic aromatic hydrocarbons(PAHs), suspended particulate matter (PM) and tar substances. The purpose of this study was to determine the possibility of using artificial neural networks for identification of any patterns occurring during heating and nonheating seasons. The samples included in the study were collected over a period of 5 years (1997–2001) in the area of the city of Gdansk and the levels of pollutants measured in the samples collected were used as inputs to two different types of neural networks: multilayer perceptron (MLP) and self-organizing map (SOM). The MLP was used as a tool to predict in what heating season a certain sample was collected, and the SOM was applied for mapping all samples to recognize any similarities between them. This study also presents the comparison between two projection methods—linear (principal component analysis, PCA) and nonlinear (SOM)—in extracting valuable information from multidimensional environmental data. In the research the MLP model with 13-12-1 topology was developed and successfully trained for classification of air samples from different seasons. The sensitivity analysis on the inputs to the MLP indicated benz[α]anthracene, benzo[α]pyrene, PM₁, SO₂, tar substances and PM₁₀ as the most distinctive variables, while PCA pointed to PAHs and PM₁.     

Correlation of solubility data of ammonia in ionic liquids for gas separation processes using artificial neural networks

Artificial neural networks have been used for the correlation and prediction of solubility data of ammonia in ionic liquids. This solubility of ammonia is highly variable for different types of ionic liquids at the same temperature and pressure, its correlation and prediction is of special importance in the removal of ammonia from flue gases for which effective and efficient solvents are required. Nine binary ammonia + ionic liquids mixtures were considered in the study. Solubility data (P–T–x) of these systems were taken from the literature (208 data points for training and 50 data points for testing). The training variables are the temperature and the pressure of the binary systems (T, P), being the target variable the solubility of ammonia in the ionic liquid (x). The study shows that the neural network model is a good alternative method for the estimation of solubility for this type of mixtures. Absolute average deviations were below 5.6%, for each isothermal data set and overall absolute average deviations were below 3.0%. Only in the range of low solubility (below 0.2 in mole fraction) did predicted solubility give deviations higher than 10%.     

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.     

The recognition of similarities in trace elements content in medicinal plants using MLP and RBF neural networks

The objective of the paper was to verify if the content of some elements provides enough information for proper classification of the medicinal plant raw materials. Such information could be helpful in standardization process of herbal products. Four elements—zinc, copper, lead and cadmium were determined using inverse voltammetry in commercially available medicinal herbal raw materials. Initially, principal component analysis (PCA) was employed to investigate the relationships among the analyzed trace elements. In the next stage of the study, two different types of feed-forward artificial neural networks (FANNs)—multilayer perceptron (MLP) and radial basis function (RBF) were applied. The concentrations of the elements were used as input variables to neural networks models, which were to recognize the taxonomy of the plant and the anatomical part it originated from. Although full recognition of the samples with use of FANNs on the basis of some trace elements content was not achieved, it was possible to identify two elements—cadmium and lead as the most important in the classification analysis of medicinal plants.     

Analysis of polyphenols in wines: correlation between total polyphenolic content and antioxidant potential from photometric measurements. Prediction of cultivars and vintage from capillary zone electrophoresis fingerprints using artificial neural network

The polyphenols (some of them are also called phytoalexins, flavonols, flavanons, flavanonols, flavons, flavanols, and anthocyanines) are usually marked as potent antioxidants or radical scavengers which assist the body cells against oxidation. Polyphenols in wine are also considered to explain so called French paradox (long life aging and low number of coronary diseases despite of high alcohol and fat consumption). The total polyphenolic content (TPC) and total antioxidant potential (TAP) were determined by photometry and found strongly correlated. This finding suggests that the determination of TAP can be replaced by a more simple procedure of TPC determination. Capillary zone electrophoresis (CZE) with preconcentration by solid phase extraction (SPE) was applied for some polyphenols determination and for obtaining electropherograms of the SPE extracts (fingerprints). From mathematical evaluation of the fingerprints, prediction of cultivars and vintage using artificial neural networks (ANN) was done with more than 90% correct prediction. The study was performed on a set of 47 samples of young wines (vintage 1999-2002) from south Moravia (Czech Republic) and New South Wales (Australia).     

Catechol determination in compost bioremediation using a laccase sensor and artificial neural networks

An electrochemical biosensor based on the immobilization of laccase on magnetic core-shell (Fe₃O₄–SiO₂) nanoparticles was combined with artificial neural networks (ANNs) for the determination of catechol concentration in compost bioremediation of municipal solid waste. The immobilization matrix provided a good microenvironment for retaining laccase bioactivity, and the combination with ANNs offered a good chemometric tool for data analysis in respect to the dynamic, nonlinear, and uncertain characteristics of the complex composting system. Catechol concentrations in compost samples were determined by using both the laccase sensor and HPLC for calibration. The detection range varied from 7.5 × 10^–7 to 4.4 × 10^–4 M, and the amperometric response current reached 95% of the steady-state current within about 70 s. The performance of the ANN model was compared with the linear regression model in respect to simulation accuracy, adaptability to uncertainty, etc. All the results showed that the combination of amperometric enzyme sensor and artificial neural networks was a rapid, sensitive, and robust method in the quantitative study of the composting system. Figure Structure of the magnetic carbon paste electrode used in the electrochemical biosensor     

Prediction dielectric constant of different ternary liquid mixtures at various temperatures and compositions using artificial neural networks

Artificial neural networks (ANNs) were successfully developed for the modeling and prediction dielectric constant of different ternary liquid mixtures at various temperatures (?10°C?≤?t?≤?80°C) and over the complete composition range (0?≤?x ₁,?x ₂,?x ₃?≤?1). A three-layered feed forward ANN with architecture 7-16-1 was generated using seven parameters as inputs and its output is dielectric constant of media. It was found that properly selected and trained neural network could fairly represent the dependence of dielectric constant of different ternary liquid mixtures on temperature and composition. For the evaluation of the predictive power of the generated ANN, an optimized network was applied for predicting the dielectric constant in the prediction set, which were not used in the modeling procedure. Squared correlation coefficient (R ²) and root mean square error for prediction set are 0.9997 and 0.2060, respectively. The mean percent deviation (MPD) for the property in the prediction set is 0.8892%. The results show nonlinear dependence of dielectric constant of ternary mixed solvent systems on temperature and composition is significant.     

Determination of nerve agent degradation products by capillary electrophoresis using field-amplified sample stacking injection with the electroosmotic flow pump and contactless conductivity detection

In the present study, field-amplified sample stacking injection using the electroosmotic flow pump (FAEP) was developed for the capillary electrophoretic separation of the four nerve agent degradation products methylphosphonic acid (MPA), ethyl methylphosphonic acid (EMPA), isopropyl methylphosphonic acid (IMPA) and cyclohexyl methylphosphonic acid (CMPA). Coupled to contactless conductivity detection, direct quantification of these non-UV active compounds could be achieved. Sensitivity enhancement of up to 500 to 750-fold could be obtained. The newly established approach was applied to the determination of the analytes in river water and aqueous extracts of soil. Detection limits of 0.5, 0.7, 1.4 and 2.7 ng/mL were obtained for MPA, EMPA, IMPA and CMPA, respectively, in river water and 0.09, 0.14, 0.44 and 0.22 μg/g, respectively, in soil.     

Process optimization of preparing spherical titania colloids with uniform distribution using artificial neural networks

Optimal operating variables for preparing submicron uniform titania colloids were estimated using the artificial neural networks (ANN) modeling and the process optimization algorithms. Titania colloids were synthesized by a sol–gel method using mixture recipes of titanium tetraisopropoxide (TTIP), NH₃, and H₂O with ethanol/acetonitrile under temperature-controlled conditions. Different sets of the operating variables, such as [NH₃], [H₂O], and reaction temperature, were selected within an operating range to carry out Design of Experiment to evaluate the prepared particle size (PS) and the particle size distribution (PSD) data. The relationship between the operating variables and PS and PSD of the prepared samples can be constructed by an ANN modeling approach. The built ANN model was then used to predict PS and PSD values corresponding to the operating variables. The optimal operating conditions to fabricate different PS values with narrow PSD were determined by the ANN model with the optimization method. Meanwhile, the monodispersed colloids between 150 and 400 nm were fabricated using the determined optimal operating conditions.     

Quantitative chiral analysis in capillary electrophoresis from unresolved peaks using derivative electropherograms,experimental design,and artificial neural networks

Quantitative capillary electrophoretic analysis of chiral compounds might be difficult or even impossible when baseline separation is not reached. In this work, the use of n-th derivative of the electropherogram was studied and examined on model and experimental data. The electropherograms should be first smoothed using Savitzky-Golay method and the quantitative analysis is then possible using either a graphical method or multivariate calibration applying a combination of experimental design (ED) and artificial neural networks (ANNs). The best results were obtained for the first derivative, higher derivatives are not suitable because of noise accumulation. The method was applied to real experimental data to quantify chiral amino acids from unresolved peaks, but it is applicable for quantitative analysis of any other chiral analytes from poorly resolved peaks. Precision of analysis from partially resolved peaks reached was about +/- 3.2% relative standard deviation.     

Application of probabilistic neural networks in qualitative analysis of near infrared spectra: determination of producing area and variety of loquats

Near infrared (NIR) spectra of a sample can be treated as a signature, allowing samples to be grouped on basis of their spectral similarities. Near infrared spectroscopy (NIRS) combined with probabilistic neural networks (PNN) have been used to discriminate producing area and variety of loquats. Two varieties of loquats (‘Dahongpao’ and ‘Jiajiaozhong’) picked from two producing areas of ‘Tangxi’ and ‘Cunan’ in Zhejiang province were analyzed in this study. Principal component analysis (PCA) was applied before PNN modeling and the results indicated that the dimension of the vast spectral data can be effectively reduced. For each model, half samples were used to train the network and the remaining half were used to test the network. The results of the PCA-PNN models for discriminating the variety of samples from the same producing area or for discriminating the producing area of the same variety samples were much better than those of the PCA-PNN models for discriminating variety or producing area of all loquat samples. The results of this study show that NIRS combined with PCA-PNN is a feasible way for qualitative analysis of discriminating fruit producing areas and varieties.     

The effect of electric field strength,buffer type and concentration on separation parameters in capillary zone electrophoresis

Summary Probe solutes were used to investigate the effect of buffer type, concentration and applied voltage on solute mobility, column efficiency and resolution in capillary zone electrophoresis. With low conductivity buffers higher concentrations and/or higher voltages could be used to improve column efficiency and resolution. Doubling the concentration of the buffer doubles the amount of heat generated inside the column while doubling the applied voltage cause a 4-fold increase. Solute migration time is approximately an inverse function of the charge density of the buffer's cation. Analysis time is increased by about 30% if the buffer concentration is doubled while it is cut in half if the applied voltage is doubled. Column efficiency is improved (higher theoretical plate count) with increasing buffer concentration and/or applied voltage as long as the heat generated is efficiently dissipated. The separation factor is directly related to analysis time and, therefore, selectivity improves with increasing buffer concentration but decreases with increasing applied voltage. Hence, resolution is optimized by increasing buffer concentration at a moderate applied voltage.