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.     

A computer program for the optimal ion chromatographic determination of anions in waters

Summary A general form of computer program which can assist in method development for any natural water has been developed. Necessary input data, sequence of main operations, necessary mathematical relationships and the output data are specified. The development of the structure of the computer program was based on experiences with real samples.     

Multi-criteria decision making in micellar liquid chromatographic separation of chlorophenols

Simultaneous optimization of separation quality and analysis time of the micellar liquid chromatography of nine chlorophenol isomers was investigated. The effect on retention of three experimental parameters was studied using multivariate analysis. The factors studied were the concentration of sodium dodecyl sulfate, propanol content, and pH of the mobile phase. The experiments were performed according to the face-centered cube central composite design and the inverse form of the experimental retention times of analytes was fitted to polynomial models. The results of the analysis of variance showed that the models obtained explain over 99% of the variance observed in the chromatograms. The good predictive ability of the models was verified by high correlation coefficient (R2 > 0.99) and F ratio values for the plots of predicted cross-validated versus experimental retention times. The study showed that the use of the Pareto-Optimality method, an approach from multi-criteria decision making, allows selection of the best possible combinations of separation quality and analysis time in micellar liquid chromatography of chlorophenols.     

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.     

Optimization of a high-performance liquid chromatography system by artificial neural networks for separation and determination of antioxidants

A high-performance liquid chromatography (HPLC) system was used to determine the antioxidants tert-butyl-hydroquinone (TBHQ), tert-butylhydroxyanisole (BHA), and 3,5-di-tert-butylhydroxytoluene (BHT) simultaneously in oils. The paper presents a new methodology for the optimized separation of antioxidants in oils based on the coupling of experimental design and artificial neural networks. The orthogonal design and the artificial neural networks with extended delta-bar-delta (EDBD) learning algorithm were employed to design the experiments and optimize the variables. The response function (Rf) used was a weighted linear combination of two variables related to separation efficiency and retention time, according to which the optimized conditions were obtained. The above-mentioned antioxidants in rapeseed oils were separated and determined simultaneously under optimized conditions by HPLC with UV detection at 280 nm. Linearity was obtained over the range of 10-200 microg/mL with recoveries of 98.3% (TBHQ), 98.1% (BHT), and 96.2% (BHA).     

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.     

人工神经网络阻抑动力学光度法同时测定邻苯二酚和间苯二胺

在硫酸性介质中,Fe(Ⅲ)能够催化H2O2氧化中性红褪色反应,邻苯二酚和间苯二胺都能阻抑该催化氧化褪色反应的速度,研究发现：两者对Fe(Ⅲ)催化H2O2氧化中性红褪色反应阻抑作用不具有加和性,根据这一现象,用人工神经网络处理非线性体系的优势进行数据处理,从而建立了一种新的测定邻苯二酚和间苯二胺混合物的人工神经网络阻抑动力学光度法。对5组混合样品进行测定,回收率均在95%-105%之间。     

An accurate quantitative analysis of polymorphs based on artificial neural networks

Measurement precision based on homogeneous and accurate standard samples has been reported to result in significant improvement in the sensitivity and accuracy of the quantitative analysis of polymorphic mixtures. The purpose of this study was to further improve the accuracy of the quantitation based on data processing by artificial neural networks (ANNs), using such high quality standard samples. Homogeneous powder mixtures of - and γ-forms of indomethacin (IMC) at various ratios (0–50% -form content) were subjected to X-ray powder diffractometry. The two diffraction peaks selected as the best combination in multiple linear regression (MLR) were used in the ANN with an extended Kalman filter as a training algorithm. The results obtained by ANN had better predictive accuracy at lower contents (0–5%) compared to those of MLR. ANNs for the diffraction data based on high quality standard samples provide an extremely precise and accurate quantification for polymorphic mixtures.     

Response surface methodology to optimize gradient ion chromatographic separation of inorganic anions and organic acids in tobacco leaves

The separation optimization of nine organic and inorganic anions in tobacco leaves using gradient ion chromatography by response surface methodology was investigated.In order to achieve this goal the usefulness of the chromatographic response function(CRF) for the evaluation of the two different chromatographic performance goals(resolution and analysis time) was tested. The experiments were performed according to a Box-Behnken design response surface experimental design.     

人工神经网络阻抑动力学光度法同时测定对苯二胺和间苯二胺

在硫酸性介质中,Fe(Ⅲ)能够催化H2O2氧化甲基红褪色反应,对苯二胺和间苯二胺都能阻抑该催化氧化褪色反应的速度,两者对Fe(Ⅲ)催化H2O2氧化甲基红褪色反应阻抑作用不具有加和性,根据这一现象,用人工神经网络处理非线性体系的优势进行数据处理,从而建立了一种新的测定对苯二胺和间苯二胺混合物的人工神经网络阻抑动力学光度法。对6组混合样品进行测定,回收率均在95%～105%之间。该方法运用于实验室水样的分析。     

The study of the relationship between the new topological index A(m) and the gas chromatographic retention indices of hydrocarbons by artificial neural networks

The newly developed topological indices A_m1-A_m3 and the molecular connectivity indices ^mX were applied to multivariate analysis in structure-property correlation studies. The topological indices calculated from the chemical structures of some hydrocarbons were used to represent the molecular structures. The prediction of the retention indices of the hydrocarbons on three different kinds of stationary phase in gas chromatography can be achieved applying artificial neural networks and multiple linear regression models. The results from the artificial neural networks approach were compared with those of multiple linear regression models. It is shown that the predictive ability of artificial neural networks is superior to that of multiple linear regression method under the experimental conditions in this paper. Both the topological indices ²X and A_m1 can improve the predicted results of the retention indices of the hydrocarbons on the stationary phase studied.     

Use of neural networks and diode-array detection to develop an isocratic HPLC method for the analysis of nitrophenol pesticides and related compounds

Summary We have used an artificial neural network to optimize the composition of the mobile phase for an isocratic HPLC method for the analysis of nitrophenol pesticides and related compounds, on the basic of different response functions, and have compared the results with those obtained by application of response-surface methodology. These studies resulted in the selection the mobile phase 10:30:15:45 methanol-acetonitrile-tetrahydrofuran-buffer solution (0.1m acetic acid and 0.1m sodium perchlorate); the flow-rate was 1 mL min⁻¹. Under these conditions a chromatogram showing twelve well-resolved peaks was obtained in 14 min. Although the peaks corresponding to ethylparathion and medinoterb acetate overlapped severely, it was possible, by use, of a diode-array spectrophotometer for detection, and by combining the absorbance measured at different wavelengths as the signal, to separate the peaks corresponding to one or other of the compounds. Calibration plots were constructed for the concentration range 2–10 ppm. Detection limits, calculated by the method of Clayton et al., were approximately 0.32–0.69 ppm. The method has been applied to the analysis of these compounds in fortified river water samples, after previous preliminary preconcentration by solid-liquid extraction on a C₁₈ cartridge.     

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.     

高压离子色谱法快速测定饮用水中7种无机阴离子

建立了使用高压离子色谱快速测定饮用水中7种无机阴离子的方法。环境水样经0.22 μm尼龙滤膜过滤后可直接进样分析。采用Dionex Integrion高压离子色谱仪和AS22-Fast-4 μm阴离子交换柱（150 mm×4 mm）,可在5 min内完成对F^-、Cl^-、Br^-、NO₂^-、NO₃^-、SO₄^2-和PO₄^3-这7种阴离子的分析。以4.5 mmol/L碳酸钠和1.4 mmol/L碳酸氢钠为淋洗液,流速为2mL/min。7种阴离子的检出限为0.007~0.07 mg/L（S/N=3）,在较宽范围内有良好的线性关系（相关系数不小于0.999）和重现性（相对标准偏差不大于0.48%,n=8）。实际样品加标回收率为91.4%~109.7%,相对标准偏差为0.30%~0.45%（n=5）。将该方法应用于饮用水厂进出水的分析,结果表明在进出水中检出6种阴离子,以Cl^-、NO₃^-和SO₄^2-为主。该方法简便快速、灵敏准确,尤其适合高通量样品中阴离子的快速分析。     

Traceability of honey origin based on volatiles pattern processing by artificial neural networks

Head-space solid-phase microextraction (HS-SPME)-based procedure, coupled to comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry (GC × GC–TOF-MS), was employed for fast characterisation of honey volatiles. In total, 374 samples were collected over two production seasons in Corsica (n = 219) and other European countries (n = 155) with the emphasis to confirm the authenticity of the honeys labelled as “Corsica” (protected denomination of origin region). For the chemometric analysis, artificial neural networks with multilayer perceptrons (ANN-MLP) were tested. The best prediction (94.5%) and classification (96.5%) abilities of the ANN-MLP model were obtained when the data from two honey harvests were aggregated in order to improve the model performance compared to separate year harvests.     

Non-destructive determination of metronidazole powder by using artificial neural networks on short-wavelength NIR spectroscopy

The present study aimed at providing a new method in sight into short-wavelength near-infrared (NIR) spectroscopy of in pharmaceutical quantitative analysis. To do that, 124 experimental samples of metronidazole powder were analyzed using artificial neural networks (ANNs) in the 780-1100 nm region of short-wavelength NIR spectra. In this paper, metronidazole was as active component and other two components (magnesium stearate and starch) were as excipients. Different preprocessing spectral data (first-derivative, second-derivative, standard normal variate (SNV) and multiplicative scatter correction (MSC)) were applied to establish the ANNs models of metronidazole powder. The degree of approximation, a new evaluation criterion of the networks was employed to prove the accuracy of the predicted results. The results presented here demonstrate that the short-wavelength NIR region is promising for the fast and reliable determination of major component in pharmaceutical analysis.     

Organic solvent and temperature-enhanced ion chromatography-high resolution mass spectrometry for the determination of low molecular weight organic and inorganic anions

There has recently been increased interest in coupling ion chromatography (IC) to high resolution mass spectrometry (HRMS) to enable highly sensitive and selective analysis. Herein, the first comprehensive study focusing on the direct coupling of suppressed IC to HRMS without the need for post-suppressor organic solvent modification is presented. Chromatographic selectivity and added HRMS sensitivity offered by organic solvent-modified IC eluents on a modern hyper-crosslinked polymeric anion-exchange resin (IonPac AS18) are shown using isocratic eluents containing 5–50 mM hydroxide with 0–80% methanol or acetonitrile for a range of low molecular weight anions (<165 Da). Comprehensive experiments on IC thermodynamics over a temperature range between 20–45 °C with the eluent containing up to 60% of acetonitrile or methanol revealed markedly different retention behaviour and selectivity for the selected analytes on the same polymer based ion-exchange resin. Optimised sensitivity with HRMS was achieved with as low as 30–40% organic eluent content. Analytical performance characteristics are presented and compared with other IC-MS based works. This study also presents the first application of IC-HRMS to forensic detection of trace low-order anionic explosive residues in latent human fingermarks.