Application of artificial neural networks in multifactor optimization of an FIA system for the determination of aluminium

A methodology based on the coupling of experimental design and artificial neural networks (ANNs) is proposed in the optimization of a new flow injection system for the spectrophotometric determination of Al(III) with Arsenazo DBM, which has for the first time been used as chromogenic reagent in the quantitative analysis of aluminium. An orthogonal design is utilized to design the experimental protocol, in which three variables are varied simultaneously. Feedforward-type neural networks with faster back propagation (BP) algorithm are applied to model the system, and then optimization of the experimental conditions is carried out in the neural network with 3-7-1 structure, which have been confirmed to be able to provide the maximum performance. In contrast to traditional methods, the use of this methodology has advantages in terms of a reduction in analysis time and an improvement in the ability of optimization. The method has been applied to the determination of Al(III) in steel samples and provided satisfactory results.     

Application of artificial neural networks in multifactor optimization of an FIA system for the determination of aluminium

A methodology based on the coupling of experimental design and artificial neural networks (ANNs) is proposed in the optimization of a new flow injection system for the spectrophotometric determination of Al(III) with Arsenazo DBM, which has for the first time been used as chromogenic reagent in the quantitative analysis of aluminium. An orthogonal design is utilized to design the experimental protocol, in which three variables are varied simultaneously. Feedforward-type neural networks with faster back propagation (BP) algorithm are applied to model the system, and then optimization of the experimental conditions is carried out in the neural network with 3-7-1 structure, which have been confirmed to be able to provide the maximum performance. In contrast to traditional methods, the use of this methodology has advantages in terms of a reduction in analysis time and an improvement in the ability of optimization. The method has been applied to the determination of Al(III) in steel samples and provided satisfactory results. Received: 26 May 1999 / Revised: 29 July 1999 / Accepted: 17 August 1999     

Application of artificial neural networks in multifactor optimization of an on-line microwave FIA system for catalytic kinetic determination of ruthenium (III)

A methodology based on the coupling of experimental design and artificial neural networks (ANNs) is proposed in the optimization of a flow injection system for the spectrophotometric determination of Ru (III) with m-acetylchlorophosphonazo (CPA-mA), which has been for the first time used for the optimization of high-performance capillary zone electrophoresis (J. Chromatogr. A 793 (1998) 317). And since it has been applied in many other regions like micellar electrokinetic chromatography, ion-interaction chromatography, HPLC, etc. (J. Chromatogr. A 850 (1999) 345; J. Chromatogr. A 799 (1998) 35; J. Chromatogr. A 799 (1998) 47). An orthogonal design is utilized to design the experimental protocol, in which five variables are varied simultaneously (Anal. Chim. Acta 360 (1998) 227). Feedforward-type neural networks with extended delta-bar-delta (EDBD) algorithm are applied to model the system, and the optimization of the experimental conditions is carried out in the neural network with 5-5-1 structure, which have been confirmed to be able to provide the maximum performance. In contrast to traditional methods, the use of this methodology has advantages in terms of a reduction in analysis time and an improvement in the ability of optimization. Under the optimum experimental conditions, Ru (III) can be determined in the range 0.040-0.60 mug ml(-1) with detection limit of 0.03 mug ml(-1) and the sampling frequency of 34 h(-1). The method has been applied to the determination of Ru (III) in refined ore as well as in secondary alloy and provided satisfactory results.     

Application of Artificial Neural Networks in Multifactor Optimization of Selectivity in Capillary Electrophoresis

ABSTRACT

A methodology based on the coupling of experimental design and artificial neural networks (ANNs) was proposed in the optimization of selectivity in capillary electrophoresis. The effect of the buffer composition, concentration, SDS concentration, ethanol percentage and the applied voltage on the separation of six choice solutes was examined by using orthogonal design. Feedforward-type neural networks with faster back propagation (BP) algorithm were applied to model the separation process, and then optimization of the experimental conditions was carried out in the modeled neural network with 5-7-1 structure, which had been confirmed to be able to provide the maximum performance. It was demonstrated that by combining ANN modeling with experimental design, the number of experiments necessary to search and find optimal separation conditions can be reduced significantly. Because of its general validity, the new proposed approach can also be applied in other separation conditions.     

Fluorimetric determination of fluoride in a flow assembly integrated on-line to an open/closed FIA system to remove interference by solid phase extraction

A simple flow injection fluorimetric method for fluoride determination is proposed. The method is based on the enhanced fluorescence of quercitin-Zr(IV) complex when fluoride ion is present in the sample. An open/closed FIA manifold with a mini-column of Dowex 50W X8 resin was used to remove the most important interference (aluminum). The two FIA assemblies were integrated on-line to automate the pretreatment of the water sample and fluoride determination. The calibration graph was linear over the range 0.1-3.0 mug ml(-1) of fluoride with a correlation coefficient of 0.999 and LOD 0.06 mug ml(-1). The relative standard deviation was 2.5% and the sample throughput was 52 h(-1) without pretreatment and 10 h(-1) with pretreatment of the sample. The method was applied to the determination of fluoride in water samples.     

Evaluation of nonlinear modeling based on artificial neural networks for the spectrophotometric determination of Pd(II) with CPA-mK

A new method is proposed for the spectrophotometric determination of Pd(II), based on the reaction of Pd(II) with 2-(4-chloro-2-phosphonophenylazo)-7-(3-carboxyphenylazo)-1,8-dihydroxynaphthalene-3,6-disulfonic acid(CPA-mK) in sulfuric acid without heating. Beer’s law is obeyed for 1.0–4.0 μg of Pd (II) in 10 mL of solution. The calibration curve from 1.0 to 42.0 μg in 10 mL of solution is modeled successfully by artificial neural networks (ANNs). The maximum relative error between experimental values and the values predicted by ANNs is 1.5%. In comparison with some mathematical functions, ANNs show better ability for curve fitting, thus greatly extending the applicable range of the calibration curve of this new system. The method has been applied to determine Pd (II) in ore and catalyst samples with a relative error of less than 4% and with a recovery range between 94% and 103%.     

人工神经元网络法提高毛细管电泳药物控制分析准确度的研究

研究了人工神经元网络法在毛细管电泳定量测定memantine中提高测定准确度 的可行性。在毛细管电泳法定量测定memantine的过程中，其浓度与峰高或峰面积 以及与二者和内标的比值均没有良好的线性关系。人工神经元网络具有很强的非线 性校正能力，其最大优点是无须对分离体系及组分的迁移行为预先予以了解。人工 神经元网络的输为memantine的峰高和峰面积，输出为memantine的浓度。通过实验 确定的网络结构为2：1：1型。由于人工神经元网络的通用性，该法也可用于毛细 管电泳在其他药物控制分析中改善定量分析的准确度。     

Evaluation of nonlinear modeling based on artificial neural networks for the spectrophotometric determination of Pd(II) with CPA-mK

A new method is proposed for the spectrophotometric determination of Pd(II), based on the reaction of Pd(II) with 2-(4-chloro-2-phosphonophenylazo)-7-(3-carboxyphenylazo)-1,8-dihydroxynaphthalene-3,6-disulfonic acid(CPA-mK) in sulfuric acid without heating. Beer’s law is obeyed for 1.0–4.0 μg of Pd (II) in 10 mL of solution. The calibration curve from 1.0 to 42.0 μg in 10 mL of solution is modeled successfully by artificial neural networks (ANNs). The maximum relative error between experimental values and the values predicted by ANNs is 1.5%. In comparison with some mathematical functions, ANNs show better ability for curve fitting, thus greatly extending the applicable range of the calibration curve of this new system. The method has been applied to determine Pd (II) in ore and catalyst samples with a relative error of less than 4% and with a recovery range between 94% and 103%. Received: 2 November 1999 / Revised: 5 January 2000 / Accepted: 10 January 2000     

Predicting and evaluating separation quality of micellar electrokinetic capillary chromatography by artificial neural networks

Computer-aided optimization of micellar electrokinetic capillary chromatography (MEKC) separations was demonstrated by artificial neural networks (ANNs) using a Levenberg-Marquardt algorithm and an orthogonal experimental design. A novel criterion, named Q, for evaluating the separation quality of MEKC was firstly presented, which considered both separation selectivity and analysis time. MEKC separation conditions of seven plant hormones were then simulated and optimized using ANNs based on this novel criterion. The result was further compared to that obtained using ANNs based on a traditionally used criterion of overall normalization resolution (named r). Finally, the separation under optimum conditions predicted by ANNs using the criterion Q was compared to, and proved to be better than that obtained by empirical step-by-step optimization procedures. This method may also be adapted to other separation methods due to its generality.     

Prediction of the antimicrobial activity of quaternary ammonium salts against Staphylococcus aureus using artificial neural networks

The study of the quantitative structure–activity relationship (QSAR) on antibacterial activity in a series of new imidazole derivatives against Staphylococcus aureus was conducted using artificial neural networks (ANNs). Antibacterial activity against S. aureus was associated with a number of physicochemical and structural parameters of the examined imidazole derivatives. The designed regression and classification models were useful in determining the antibacterial properties of quaternary ammonium salts against S. aureus. The developed models of artificial neural networks were characterized by high predictability (93.57% accuracy of classification, regression model: training data R = 0.92, test data R = 0.92, validation data R = 0.91). ANNs are considered to be a useful tool in supporting the design of synthesis and further biological experiments in the logical search for new antimicrobial substances. Data analysis using ANNs enables the optimization and reduction of labor costs by narrowing the compound synthesis to achieve the desired properties.     

Application of artificial neural networks as a technique for interference removal: kinetic-spectrophotometric determination of trace amounts of Se(IV) in the presence of Te(IV)

Artificial neural networks (ANNs) are among the most popular techniques for nonlinear multivariate calibration in complicated mixtures using spectrophotometric data. In this study we propose a computer-based method for removing Te(IV) interference in the determination of Se(IV) using artificial neural networks. In this way, an artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. The resulting RMSE of prediction for selenium was obtained as 0.108.     

人工神经元网络方法在毛细管电泳和色谱分析中的应用

综述了人工神经元网络方法在毛细管电泳和色谱分析中的应用,内容包括迁移(或保留)行为的预测,分离优化,模式识别及分类,重叠峰定量解析,非线性过程的模型化,峰纯度的判断等。还对人工神经元网络在色谱和毛细管电泳中将来可能的应用进行了探讨。引用文献52篇。     

Monitoring substrate and products in a bioprocess with FTIR spectroscopy coupled to artificial neural networks enhanced with a genetic-algorithm-based method for wavelength selection

An experiment was developed as a simple alternative to existing analytical methods for the simultaneous quantitation of glucose (substrate) and glucuronic acid (main product) in the bioprocesses Kombucha by using FTIR spectroscopy coupled to multivariate calibration (partial least-squares, PLS-1 and artificial neural networks, ANNs). Wavelength selection through a novel ranked regions genetic algorithm (RRGA) was used to enhance the predictive ability of the chemometric models. Acceptable results were obtained by using the ANNs models considering the complexity of the sample and the speediness and simplicity of the method. The accuracy on the glucuronic acid determination was calculated by analysing spiked real fermentation samples (recoveries ca. 115%).     

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.     

Usage of Artificial Neural Network (Back Propagation) in Optimising Salicylic Acid Determination with Ferric(III) Nitrate

Abstract

The use of artificial neural networks (ANN) in optimizing salicylic acid (SA) determination is presented in this paper. A simple and rapid spectrophotometric method for salicylic acid (SA) determination was carried out based on the complexation of salicylic acid–ferric(III) nitrate, SAFe(III). The SA forms a stable purple complex with ferric(III) nitrate at pH 2.45. The useful dynamic linear range is 0.01–0.35 g/L. It has a maximum absorption at 524 nm and the stability is more than 50 hours. The results were used for artificial neural networks (ANNs) training to optimize data. For training and validation purposes, a back‐propagation (BP) artificial neural network (ANN) was used. The results showed that ANN technique was very effective and useful in broadening the limited dynamic linear response range mentioned to an extensive calibration response (0.01–0.70 g/L). It was found that a network with 22 hidden neurons was highly accurate in predicting the determination of SA. This network scores a summation of squared error (SSE) skill and low average predicted error of 0.0078 and 0.00427 g/L, respectively.     

Adaptation of a commercial ion selective fluoride electrode to a tubular configuration for analysis by flow methodologies

The determination of fluoride ions in water samples is accomplished by using a tubular flow through detector constructed by drilling a channel through a commercially available LaF(3) crystal electrode in such a way that the original contacts of the non-modified unit are maintained. Its performance when incorporated in both FIA and SIA systems was evaluated and the results show that the tubular unit retains the characteristics of the non-modified electrode. In SIA conditions an extended linear range of response and lower detection limit were achieved when compared with the electrode performance in FIA conditions. These aspects together with the additional advantage of low sample and reagent consumptions in SIA when compared to FIA, makes the incorporation of the proposed tubular ISE in a SIA system the preferred approach for on line determination and monitoring of fluoride content in natural water samples.     

Spectrophotometric determination of iron with ferrozine by flow-injection analysis

Two methods for the determination of iron by normal FIA and reversed FIA were developed using sodium 3-(2-pyridyl)-5,6-diphenyl-1,2,4-triazine-4',4'-disulphonate (ferrozine). The reagent formed a chelate with Fe(II) in hexamethylentetramine buffered medium at pH 5.5. In one previous reaction coil Fe(III) was reduced to Fe(II) by ascorbic acid and in the other reaction coil the complexation reaction was developed. The linear range of the determination was 0.5-6 and 0.1-5 mug ml(-1) of iron for normal FIA and reversed FIA respectively. The proposed method was sensitive (detection limit 0.012 and 0.010 mug ml(-1)), rapid and reproducible (RSD 0.3 and 0.28%). The method was satisfactorily applied to the determination of iron in waste water, toadstool tissue, potato leaves, human hair and bauxites at a sampling rate of 90 and 50 samples h(-1) for normal FIA and reversed FIA respectively.     

Enzymatic methods for the determination of alpha-glycerophosphate and alpha-glycerophosphate oxidase with an automated FIA system

A procedure for the enzymatic determination of alpha-glycerophosphate (alpha-GP) has been developed, using an automated in-house FIA system, with immobilized glycerol-3-phosphate oxidase (GPO) on non-porous glass beads, following optimization of the immobilization and analytical parameters. Fabricated single bead string reactors (SBSR) were used in connection with the FIA system, following optimization of its parameters. The half-life of GPO-SBSR regarding reduction of the enzyme activity was found to be 110 days for its use in 20 triplicate measurements daily and storage at 4 degrees C in the appropriate buffer. The regression equation of the calibration graph for the determination of alpha-GP was: A(max)=(10+/-2)x10(-4)+(22 134+/-12)x10(-4) (mmol l(-1)alpha-GP). The lower limit of quantitation was 0.74 mumol l(-1)alpha-GP and the RSD of the method 0.05% (r=0.9999). The same FIA system and procedure can be also used for the determination of the GPO activity, with the alpha-GP as substrate. The regression equation for this calibration graph was: A(max)=(23+/-18)x10(-4)+(190+/-1)x10(-4) (mug ml(-1) GPO), the lower limit of quantitation was 0.782x10(-3) mg ml(-1) (0.782 ppm) GPO and the RSD of the method 0.53% (r=0.9999). Serum samples obtained from hospitalized patients were deproteinized by gel filtration and analyzed under pseudo-first order conditions, at various concentrations of alpha-GP. A kinetic study of the reduction of alpha-GP in serum versus time is given and an observed reaction rate constant k(ob)=106.5x10(-4) min(-1) was determined.