Artificial neural networks based on genetic input selection for quantification in overlapped capillary electrophoresis peaks

The application of multilayer perceptron artificial neural networks (MLP ANN) based on genetic input selection for quantification of the unresolved peaks in micellar electrokinetic capillary chromatography (MECC) is reported. An optimization strategy for genetic input selection was also proposed. When the corresponding CE peaks cannot be resolved completely only by separation techniques, MLP ANN based on genetic input selection can be a suitable tool to resolve the problem. Both the spectra and the electrophoretograms of the unseparated analytes were used as the multivariate input data. The two kinds of the data were suitable for quantification of overlapped CE peaks by MLP ANN based on genetic input selection. The study also shows that the applying of genetic input selection in MLP ANN can improve the precision of quantification in both completely and partially overlapped CE peaks to some extent.     

Combining classification and regression approaches for the quantification of highly overlapping capillary electrophoresis peaks by using evolutionary sigmoidal and product unit neural networks

This is a study of the potential of neural networks built by using different transfer functions (sigmoidal, product and sigmoidal–product units) designed by an evolutionary algorithm to quantify highly overlapping electrophoretic peaks. To test this approach, two aminoglycoside antibiotics, amikacin and paramomycin, were quantified from samples containing either only one component or mixtures of them though capillary zone electrophoresis (CZE) with laser‐induced fluorescence (LIF) detection. The three models assayed used as input data the four‐parameter Weibull curve associated with the profile of the electrophoretic peak and in some cases the class label for each sample estimated by cluster analysis. The combination of classification and regression approaches allowed the establishment of straightforward network topologies enabling the analytes to be quantified with great accuracy and precision. The best models for mixture samples were provided by product unit neural networks (PUNNs), 4:4:1 (14 weights) for both analytes, after discrimination by cluster analysis, allowing the analytes to be quantified with great accuracy: 8.2% for amikacin and 5.6% for paromomycin within the standard error of prediction for the generalization test, SEP_G. For comparison, partial least square regression was also used for the resolution of these mixtures; it provided a minor accuracy: SEP_G 11.8 and 15.7% for amikacin and paramomycin, respectively. The reduced dimensions of the neural networks models selected enabled the derivation of simple quantification equations to transform the input variables into the output variable. These equations can be more easily interpreted from a chemical point of view than those provided by other ANN models. Copyright © 2007 John Wiley & Sons, Ltd.     

Resolution of overlapping capillary electrophoresis peaks by using chemometric analysis: quantification of the components in compound reserpine tablets

The application of multivariate curve resolution with alternating least squares (MCR-ALS) methods to second-order data from capillary electrophoresis with diode array detector (CE-DAD) is reported. Initial qualitative solutions obtained by evolving factor analysis (EFA) and pure-variable detection method can be further optimized by a simultaneous analysis of multiple electrophoresis run data with ALS regression. While unknown samples are analyzed simultaneously against the corresponding standards in different composition ratios, the exact amounts of common components in different CE runs can be determined by the traditional calibration curve method, and quantification can thus be achieved. The above methods are applied to the determination of the components in compound reserpine tablets in overlapping peaks from CE. The quantification results are compared with those of the first derivative of the electropherogram method and artificial neural network (ANN) method.     

Artificial neural networks in analysis of indinavir and its degradation products retention

Artificial neural networks (ANN) are biologically inspired computer programs designed to simulate the way in which the human brain processes the information. In the past few years, coupling of experimental design (ED) and ANN became useful tool in the method optimization. This paper presents the application of ED-ANN in analysis of chromatographic behavior of indinavir and its degradation products. According to preliminary study, full factorial design 2⁴ was chosen to set input variables for network training. Experimental data (inputs) and results for retention factors from experiments (outputs) were used to train the ANN with aim to define correlation among variables. For networks training multi-layer perceptron (MLP) with back propagation (BP) algorithm was used. Network with the lowest root mean square (RMS) had 4-8-3 topology. Predicted data were in good agreement with experimental data (correlation was higher than 0.9713 for training set). Regression statistics confirmed good ability of trained network to predict compounds retention.     

Application of experimental design and radial basis function neural network to the separation and determination of active components in traditional Chinese medicines by capillary electrophoresis

Orthogonal design has been used to the optimization of separation and determination of two active components in traditional Chinese medicines by capillary electrophoresis. The concentration of phosphate, applied voltage, organic modifier content and buffer pH were selected as variable parameters. Their different effects on peak resolution were studied by the experimental design method. Optimized separation conditions were obtained and successfully applied to the separation and determination of aconitine and hypaconitine in Aconitum medicinal herbs. Good separation was achieved within 7 min using a buffer system composed of 20 mmol L⁻¹ phosphate and 35% acetonitrile at pH 9.5. The applied voltage was 14 kV and the detection was set at 235 nm. In addition, a radial basis function neural network with a “4-18-1” structure was developed based on the experimental results of orthogonal design and uniform design, and was applied to the prediction of peak resolution of the two active components under the optimum separation conditions given by orthogonal design. The predicted results were in good agreement with the experimental values, indicating that radial basis function neural network is a potential way for the selection of separation conditions in capillary electrophoresis.     

A technique for capillary joining in capillary electrophoresis

Summary Aspects of cracking and joining capillaries have been investigated. Capillary coupling was achieved using various methods. The most successful used hydrofluoric acid-etched capillaries to form male and female ends which were then joined together. This type of joint was used to connect sections of capillary of similar and different internal diameters with minimal loss in resolution, peak width and number of theoretical plates. (Uridine and hypoxanthine was used as a test mixture). For hypoxanthine on a 50 m/50 m etched joined capillary 10 cm from the detector window the number of theoretical plates was 96.6% of that for hypoxanthine on an unbroken capillary. Following the relative success of capillary joining, a coupled capillary flowcell (50 m/200 m) was produced and evaluated.     

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).     

An automatic sampling device for capillary zone electrophoresis

An automatic sampling device has been developed for capillary zone electrophoresis. The sample is introduced to the column electrokinetically by rapidly exchanging buffer for sample in a narrow channel drilled in a plexiglass block. The autosampler is capable, under computer control, of performing multiple sample injections from a large volume sample source (such as a reaction vessel) as the sample concentration changes, and thus presents the possibility of analyzing time-varying processes by CZE. Peak area reproducibility in electropherograms obtained after use of the sampler is less than 1% and efficiency is more than 2.2 × 10⁵ theoretical plates.     

毛细管区带电泳用于酶微反应器酶解条件的研究

将氨基酰化酶通过戊二醛固定在毛细管内壁,制备毛细管酶微反应器,用毛细管区带电泳对毛细管酶微反应器的酶解产物进行分离,以生成物的峰面积优化底物N-乙酰-DL-蛋氨酸的酶解条件。实验结果表明,在温度37℃的条件下,10μg/mL N-乙酰-DL-蛋氨酸磷酸盐缓冲溶液(pH7.5)以4μL/min的速度通过15 cm长的毛细管酶微反应器,具有良好的酶解效果。利用毛细管酶微反应器对底物N-乙酰-DL-蛋氨酸进行酶解,每天酶解5次,10天后酶活仅下降了8.66%,说明制备的毛细管酶微反应器具有良好的稳定性。     

A new mathematical function for describing electrophoretic peaks

A new model is proposed for characterizing skewed electrophoretic peaks, which is a combination of leading and trailing edge functions, empirically modified to get a rapid recovery of the baseline. The peak model is a sum of square roots and is called thereby "combined square roots (CSR) model". The flexibility of the model was checked on theoretical and experimental peaks with asymmetries in the range of 0-10 (expressed as the ratio of the distance between the center and the trailing edge, and the center and the leading edge of the chromatographic peak, measured at 10% of peak height). Excellent fits were found in all cases. The new model was compared with other three models that have shown good performance in modelling chromatographic peaks: the empirically transformed Gaussian, the parabolic Lorentzian-modified Gaussian, and the Haarhoff-van der Linde function. The latter model was proposed recently to describe electrophoretic peaks. The CSR model offered the highest flexibility to describe electrophoretic peak profiles, even those extremely asymmetrical with long tails. The new function has the advantage of using measurable parameters that allow the direct estimation of peak areas, which is useful for quantitative purposes.     

Analytical potential of enzyme-coated capillary reactors in capillary zone electrophoresis

Enzymes immobilized on the inner surface of an electrophoretic capillary were used to increase sensitivity and resolution in capillary zone electrophoresis (CZE). Sensitivity is enhanced by inserting a piece of capillary containing the immobilized enzyme into the main capillary, located before the detector, in order to transform the analyte into a product with a higher absorptivity. This approach was used to determine ethanol. In order to improve resolution, capillary pieces containing immobilized enzymes were inserted at various strategic positions along the electrophoretic capillary. On reaching the enzyme, the analyte was converted into a product with a high electrophoretic mobility, the migration time for which was a function of the position of the enzyme reactor. This approach was applied to the separation and determination of acetaldehyde and pyruvate. Finally, the proposed method was validated with the determination of ethanol, acetaldehyde, and pyruvate in beer and wine samples.     

毛细管区带电泳法测定粉针剂中头孢拉定的含量

用毛细管区带电泳法测定头孢拉定的含量 ,未涂层毛细管柱 (75 μm×48.5cm ,有效长度 40cm) ,电压 2 8kV ,检测波长 2 3 0nm ,温度 2 0℃ ,进样 5×1 0 3Pa× 3s。运行缓冲液为 2 5mmol/L硼砂缓冲液。方法的线性范围 3 1 .2 2μg/mL～ 749.2 8μg/mL ,检测限为 1 .1 7μg/mL。     

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.     

Separation of organic and inorganic arsenic species by capillary zone electrophoresis

Summary Capillary zone electrophoresis has been used to separate arsenite, arsenate, dimethylarsinic acid, and phenyl-,p-aminophenyl-, ando-aminophenylarsinic acids. Identification and quantification of the arsenic species at mg L⁻¹ levels was possible by use of direct UV detection at 200 nm. The relative standard deviation (n=7) ranged from 0.97 to 1.52% for migration times and from 2.08 to 4.31% for peak areas. A method for rapid separation of inorganic arsenic species was also developed; by use of this method arsenite and arsenate could be separated within 2 min. Presented at Balaton Symposium on High-Performance Separation Methods, Siófok, Hungary, September 1–3, 1999     

Rapid capillary zone electrophoresis method for the determination of metal cations in beverages

A rapid and reliable capillary zone electrophoresis method for the determination of inorganic cations was developed. The complete separation of K⁺, Ba²⁺, Ca²⁺, Na⁺, Mg²⁺, Mn²⁺, Ni²⁺, Cd²⁺, Li⁺ and Cu²⁺ can be achieved in 4 min with a simple electrolyte composed by 10 mM imidazole as the carrier buffer and background absorbance provider and acetic acid as the complexing agent (pH 3.60). Injection was performed hydrostatically by elevating the sample at 10 cm for 30 s. The running voltage was +25 kV at room temperature. Indirect UV-absorption detection was achieved at 185 nm. The detection limit was in the range between 0.06 mg/l (Mg²⁺) and 0.57 mg/l (K⁺) and the quantification limits ranged from 0.10 mg/l (Ni²⁺) to 0.80 mg/l (Cu²⁺). The calibration graphs were linear in the concentration range from the quantification limit till at least 1 g/l in K⁺, 10 mg/l in Ba²⁺, Ca²⁺, Mg²⁺, Mn²⁺, Ni²⁺ and Cd²⁺, 40 mg/l in Na⁺ and 12 mg/l in Li⁺ and Cu²⁺. The repeatability, intraday and interday analysis were ≤1.55% and ≤3.64% for migration time and ≤3.38% and ≤3.63% for peak area. The method developed has been applied to several beverage samples with only a simple dilution and filtration treatment of the sample. The proposed method is simple, fast, cheap and it is achieved with common products in either laboratory. For these reasons, it is a very useful method for routine analysis.     

Determination of egg white lysozyme by on-line coupled capillary isotachophoresis with capillary zone electrophoresis

An on-line coupled capillary isotachophoresis - capillary zone electrophoresis method for the determination of lysozyme in selected food products is described. The optimized electrolyte system consisted of 10 mM NH(4)OH + 20 mM acetic acid (leading electrolyte), 5 mM epsilon -aminocaproic acid +5 mM acetic acid (terminating electrolyte), and 20 mM epsilon -aminocaproic acid +5 mM acetic acid +0.1% m/v hydroxypropylmethylcellulose (background electrolyte). A clear separation of lysozyme from other components of acidic sample extract was achieved within 15 min. Method characteristics, i.e., linearity (0-50 micrograms/mL), accuracy (recovery 96+/-5%), intra-assay (3.8%), quantification limit (1 microgram/ml), and detection limit (0.25 microgram/mL) were determined. Low laboriousness, sufficient sensitivity and low running costs are important attributes of this method. The developed method is suitable for the quantification of the egg content in egg pasta.     

Formamide as solvent for capillary zone electrophoresis

A comprehensive investigation of a number of aspects when using formamide as background electrolyte solvent in capillary zone electrophoresis was presented. It included (i) the change of the ion mobility with ionic strength, (ii) the influence of the ionic strength on diffusion coefficients, and (iii) on the separation efficiency expressed by the maximum reachable plate numbers (when only longitudinal diffusion contributed to zone broadening), (iv) the effect of the solvent on pKa values (taken from the literature) of neutral and cation acids, (v) the establishment of the a pH scale in formamide by dissolving acids with known pKa values and their salts at defined proportion (thus circumventing the problem of calibrating the pH meter), (vi) the agreement between the experimentally derived and the theoretical dependence of the effective mobility on pH, (vii) the uptake of water of this hygroscopic solvent from the humidity of the environment and its consequence to the ion mobilities, pKa values, and the chemical stability of the solvent (e.g., hydrolysis), and finally (viii) the use of conductivity and indirect UV absorption to enable detection of analytes below the optical cutoff of formamide.     

Mechanism of sequence-based separation of single-stranded DNA in capillary zone electrophoresis

Separation of DNA by length using CGE is a mature field. Separation of DNA by sequence, in contrast, is a more difficult problem. Existing techniques generally rely upon changes in intrinsic or induced differences in conformation. Previous work in our group showed that sets of ssDNA of the same length differing in sequence by as little as a single base could be separated by CZE using simple buffers at high ionic strength. Here, we explore the basis of the separation using circular dichroism spectroscopy, fluorescence anisotropy, and small angle X-ray scattering. The results reveal sequence-dependent differences among the same length strands, but the trends in the differences are not correlated to the migration order of the strands in the CZE separation. They also indicate that the separation is based on intrinsic differences among the strands that do not change with increasing ionic strength; rather, increasing ionic strength has a greater effect on electroosmotic mobility in the normal direction than on electrophoretic mobility of the strands in the reverse direction. This increases the migration time of the strands in the normal direction, allowing more time for the same-length strands to be teased apart based on very small differences in the intrinsic properties of the strands of different sequence. Regression analysis was used to model the intrinsic differences among DNA strands in order to gain insight into the relationship between mobility and sequence that underlies the separation.