Artificial neural networks for simultaneous spectrophotometric differential kinetic determination of Co(II) and V(IV)

A method for simultaneous analysis of V(IV) and Co(II) has been developed by using artificial neural network (ANN). This method is based on the difference of the chemical reaction rate of V(IV) and Co(II) with Fe(III) in the presence of chromogenic reagent, 1,10-phenanthroline. The reduced product of the reaction, Fe(II), can form a colored complex with 1,10-phenanthroline and make a visible spectrophotometric signal for indirect monitoring of the V(IV) and Co(II) concentrations. Feed forward neural networks have been trained to quantify considered metal ions in mixtures under optimum conditions. The networks were shown to be capable of correlating reduced spectral kinetic data using principal component analysis (PCA) of mixtures with individual metal ion. In this way an ANN containing three layers of nodes was trained. Sigmoidal and linear transfer functions were used in the hidden and output layers, respectively, to facilitate nonlinear calibration. Both V(IV) and Co(II) were analyzed in the concentration range of 0.1-4.0 μg ml⁻¹. The proposed method was also applied satisfactorily to the determination of considered metal ions in several synthetic and water samples.     

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。     

Equilibrium and Kinetic Aspects of Hg(II) Extraction from Natural Waters with Diphenylthiocarbazone

A diphenylthiocarbazone (dithizone) based liquid-liquid extraction method of general utility is proposed for small-scale Hg(II) extraction from natural waters. The quantitative aspects related to the extraction are studied in terms of distribution of mercury dithizonate in carbon tetrachloride-water system at 293 K (20±1°C). Various distribution constants related to the reagent and the complex formed are evaluated. The equilibrium and kinetic studies of extraction and complex formation are analytically based on atomic absorption and colorimetric methods. Local natural waters were subjected to the proposed method for Hg(II) removal. The method is found to be capable of removing above 98% mercury in a single-step extraction under the conditions evaluated in terms of matrix pH, equilibration time and amount of reagent.     

The morphology modulation of graphene to produce wrinkles and folds for effective electrochemical determination of Hg(II)

A highly efficient electrode material, three-dimensional reduced graphene oxide with varying wrinkles and folds (WRGO), applicable for electrochemical determination of Hg(II) was obtained by treating graphene oxide (GO) with KOH aqueous solution. After alkaline etching, the relatively flat graphene was altered and its self-aggregation was significantly alleviated, producing more wrinkles and folds, which provided more active adsorption sites for heavy metal ions. WRGO-5 modified electrode system herein offers a highest sensitivity of (31.83 μAμM⁻¹) and a lowest LOD of (16.28 nM). Moreover, the electrode sensor possesses good stability and reproducibility.     

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。     

A kinetic spectrophotometric method for the determination of ternary mixtures of reducing sugars with the aid of artificial neural networks and multivariate calibration

A differential spectrophotometric method has been developed for the simultaneous quantitative determination of glucose (GLU), fructose (FRU) and lactose (LAC) in food samples. It relies on the different kinetic rates of the analytes in their oxidative reaction with potassium ferricyanide (K₃Fe(CN)₆) as the oxidant. The reaction data were recorded at the analytical wavelength (420 nm) of the K₃Fe(CN)₆ spectrum. Since the kinetic runs of glucose, fructose and lactose overlap seriously, the condition number was calculated for the data matrix to assist with the optimisation of the experimental conditions. Values of 80 °C and 1.5 mol l⁻¹ were selected for the temperature and concentration of sodium hydroxide (NaOH), respectively. Linear calibration graphs were obtained in the concentration range of 2.96-66.7, 3.21-67.1 and 4.66-101 mg l⁻¹ for glucose, fructose and lactose, respectively. Synthetic mixtures of the three reducing sugar were analysed, and the data obtained were processed by chemometrics methods, such as partial least square (PLS), principal component regression (PCR), classical least square (CLS), back propagation-artificial neural network (BP-ANN) and radial basis function-artificial neural network (RBF-ANN), using the normal and the first-derivative kinetic data. The results show that calibrations based on first-derivative data have advantages for the prediction of the analytes and the RBF-ANN gives the lowest prediction errors of the five chemometrics methods. Following the validation of the proposed method, it was applied for the determination of the three reducing sugars in several commercial food samples; and the standard addition method yielded satisfactory recoveries in all instances.     

汞(II)与牛血红蛋白相互作用的研究

本文采用紫外光谱(UV/VIS)、荧光光谱和圆二色谱等方法,对汞(II)与牛血红蛋白(BHb)的相互作用进行了研究。结果表明:Hg2 处理导致BHb紫外吸收的增加,出现LMCT带,并随Hg2 浓度的增加LMCT带强度显著增强。BHb分子中Soret带的吸收随着Hg2 作用时间的增加而持续降低,表明Hg2 使部分血红素辅基从BHb中脱离出来。蛋白内源荧光光谱显示,Hg2 与BHb的结合会影响蛋白质的三级结构和四级结构。远紫外圆二色谱表明,Hg2 处理会导致BHb蛋白的α-螺旋含量减少。     

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).     

Simultaneous kinetic spectrophotometric determination of Cu(II), Co(II) and Ni(II) using partial least squares (PLS) regression

A partial least squares (PLS-1) calibration model based on kinetic—spectrophotometric measurement, for the simultaneous determination of Cu(II), Ni(II) and Co(II) ions is described. The method was based on the difference in the rate of the reaction between Co(II), Ni(II) and Cu(II) ions with 1-(2-pyridylazo)2-naphthol in a pH 5.8 buffer solution and in micellar media at 25°C. The absorption kinetic profiles of the solutions were monitored by measuring the absorbance at 570 nm at 2 s intervals during the time range of 0–10 min after initiation of the reaction. The experimental calibration matrix for the partial least squares (PLS-1) model was designed with 30 samples. The cross-validation method was used for selecting the number of factors. The results showed that simultaneous determination could be performed in the range 0.1-2 μg mL⁻¹ for each cation. The proposed method was successfully applied to the simultaneous determination of Cu(II), Ni(II) and Co(II) ions in water and in synthetic alloy samples.      

Development of a multi-component chemically reactive detection conjugate for the determination of Hg(II) in water samples

Mercury ions (Hg(II)) are considered highly toxic and hazardous element even at low levels. The contamination of Hg(II) is a global problem. To develop selective and sensitive technique for the detection of Hg(II) has attracted considerable attention. In this study, a multi-component chemically reactive detection conjugate for determination of Hg(II) has been synthesized and a competitive format assay was proposed. In the technique, the chemically reactive capture conjugate was coated on the plate. The reactive detection conjugate was then captured by the capture conjugate. TMB solution was added and catalyzed by HRP molecules immobilized on AuNPs. Finally, the developed enzymatic signal was measured at 450 nm. The linear range of the assay was 0.35–350 ppb with a detection limit of 0.1 ppb. The average recoveries of Hg(II) from mineral water, tap water and lake water were 100.03%, 103.13% and 102.03%, respectively. All coefficients of variation (CVs) were less than 10%. The results are closely correlated with those from inductively coupled plasma mass spectrometry (ICP-MS), which indicated that the developed technique is a reliable method for and sensitive detection of Hg(II) in water samples.     

Artificial neural networks study of the catalytic reduction of resazurin: stopped-flow injection kinetic-spectrophotometric determination of Cu(II) and Ni(II)

An artificial neural network (ANN) procedure was used in the development of a catalytic spectrophotometric method for the determination of Cu(II) and Ni(II) employing a stopped-flow injection system. The method is based on the catalytic action of these ions on the reduction of resazurin by sulfide. ANNs trained by back-propagation of errors allowed us to model the systems in a concentration range of 0.5-6 and 1-15 mg l⁻¹ for Cu(II) and Ni(II), respectively, with a low relative error of prediction (REP) for each cation: REP_Cu(II) = 0.85% and REP_Ni(II) = 0.79%. The standard deviations of the repeatability (s_r) and of the within-laboratory reproducibility (s_w) were measured using standard solutions of Cu(II) and Ni(II) equal to 2.75 and 3.5 mg l⁻¹, respectively: s_r[Cu(II)] = 0.039 mg l⁻¹, s_r[Ni(II)] = 0.044 mg l⁻¹, s_w[Ni(II)] = 0.045 mg l⁻¹ and s_w[Ni(II)] = 0.050 mg l⁻¹. The ANNs-kinetic method has been applied to the determination of Cu(II) and Ni(II) in electroplating solutions and provided satisfactory results as compared with flame atomic absorption spectrophotometry method. The effect of resazurin, NaOH and Na₂S concentrations and the reaction temperature on the analytical sensitivity is discussed.     

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.     

Hg(II) immobilized MWCNT graphite electrode for the anodic stripping voltammetric determination of lead and cadmium

The preparation of Hg(II)-modified multi walled carbon nanotube (MWCNT) by reaction of oxidized MWCNT with aqueous HgCl₂ was carried out. The Hg(II)-modified multi walled carbon nanotube (Hg(II)/MWCNT) dispersed in Nafion solution was used to coat the polished graphite electrode surface. The Hg(II)/MWCNT modified graphite electrode was held at a cathodic potential (−1.0 V) to reduce the coordinated Hg(II) to Hg forming nanodroplets of Hg. The modified electrode was characterized by FESEM/EDAX which provided useful insights on the morphology of the electrode. The SEM images showed droplets of Hg in the size of around 260 nm uniformly distributed on the MWCNT. Differential pulse anodic stripping voltammetry (DPASV) and electrochemical impedance spectroscopy were used to study the Hg(II) binding with MWCNT. Differential pulse anodic stripping voltammetry of ppb levels of cadmium and lead using the modified electrode yielded well-defined peaks with low background current under a short deposition time. Detection limit of 0.94 and 1.8 ng L⁻¹ were obtained following a 3 min deposition for Pb(II) and Cd(II), respectively. Various experimental parameters were characterized and optimized. High reproducibility was observed from the RSD values for 20 repetitive measurements of Pb(II) and Cd(II) (1.7 and 1.9%, respectively). The determination of Pb(II) and Cd(II) in tap water and Pb(II) in human hair samples was carried out. The above method of fabrication of Hg(II)/MWCNT modified graphite electrode clearly suggests a safe route for preparing Hg immobilized electrode for stripping analysis.     

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 multivariable optimization of an on-line microwave FIA system for catalytic kinetic determination of iridium(III)

A new rapid, selective and sensitive on-line microwave flow injection-kinetic method was developed for spectrophotometric determination of micro amounts of Ir(III), based on its catalytic effect on the m-acetylchlorophosphonazo (CPA-mA) and KIO(4) reaction in NaOH media. An on-line microwave oven was employed to accelerate the reaction. The reaction was followed spectrophotometrically by measuring the decrease of the absorbance of CPA-mA at 580 nm. The effect of five variables for the determination of Ir(III) was optimized by means of a multilayer artificial neural network using extended delta-bar-delta (EDBD) algorithms. Under the optimum experimental conditions, Ir(III) could be determined in the range 0.060-0.60 micro gZZZ;mL(-1) with detection limit of 0.02 micro gZZZ;mL(-1) and the sampling frequency of 34 h(-1). The proposed method was applied to the determination of micro amounts of Ir(III) in refined ore and secondary alloy with the recoveries from 91.4% to 109%.     

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.     

Electrochemical performance of a three-layer electrode based on Bi nanoparticles,multi-walled carbon nanotube composites for simultaneous Hg(II) and Cu(II) detection

Electrochemical analysis is a promising technique for detecting biotoxic and non-biodegradable heavy metals. This article proposes a novel composite electrode based on a polyaniline (PANi) framework doped with bismuth nanoparticle@graphene oxide multi-walled carbon nanotubes (Bi NPs@GO-MWCNTs) for the simultaneous detection of multiple heavy metal ions. Composite electrodes are prepared on screen-printed electrodes (SPCEs) using an efficient dispensing technique. We used a SM200SX-3A dispenser to load a laboratory-specific ink with optimized viscosity and adhesion to draw a pattern on the work area. The SPCE was used as substrate to facilitate cost-effective and more convenient real-time detection technology. Electrochemical techniques, such as cyclic voltammetry and differential pulse voltammetry, were used to demonstrate the sensing capabilities of the proposed sensor. The sensitivity, limit of detection, and linear range of the PANi-Bi NPs@GO-MWCNT electrode are 2.57 × 10² μA L μmol⁻¹ cm⁻², 0.01 nmol/L, and 0.01 nmol/L–5 mmol/L and 0.15 × 10⁻¹ μA L μmol⁻¹ cm⁻², 0.5 nmol/L, and 0.5 nmol/L–5 mmol/L for mercury ion (Hg(II)) and copper ion (Cu(II)) detection, respectively. In addition, the electrode exhibits a good selectivity and repeatability for Hg(II) and Cu(II) sensing when tested in a complex heavy metal ion solution. The constructed electrode system exhibits a detection performance superior to similar methods and also increases the types of heavy metal ions that can be detected. Therefore, the proposed device can be used as an efficient sensor for the detection of multiple heavy metal ions in complex environments.     

Application of silver nanoparticles and principal component-artificial neural network models for simultaneous determination of levodopa and benserazide hydrochloride by a kinetic spectrophotometric method

A multicomponent analysis method based on principal component analysis-artificial neural network model (PC-ANN) is proposed for the simultaneous determination of levodopa (LD) and benserazide hydrochloride (BH). The method is based on the reaction of levodopa and benserazide hydrochloride with silver nitrate as an oxidizing agent in the presence of PVP and formation of silver nanoparticles. The reaction monitored at analytical wavelength 440 nm related to surface plasmon resonance band of silver nanoparticles. Differences in the kinetic behavior of the levodopa and benserazide hydrochloride were exploited by using principal component analysis, an artificial neural network (PC-ANN) to resolve concentration of analytes in their mixture. After reducing the number of kinetic data using principal component analysis, an artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. The optimized ANN allows the simultaneous determination of analytes in mixtures with relative standard errors of prediction in the region of 4.5 and 6.3 for levodopa and benserazide hydrochloride respectively. The results show that this method is an efficient method for prediction of these analytes.     

Rapid and sensitive spectrofluorimetric determination of trace amounts of Hg(II) with o-vanillin-8-aminoquinoline

The fluorimetric determination of mercury ions with o-vanillin-8-aminoquinoline (OVAQ) in aqueous solutions was investigated. Hg(II) could react with the fluorescent reagent OVAQ (λ_ex/em = 278/314 nm) to form a nonfluorescent complex in an ethanol-water medium of pH 6.00. The linear range of the proposed method was from 2.5 to 80 μg/L, and the detection limit was 0.80 μg/L. The interferences of 24 foreign ions were also studied. The method was successfully applied to the determination of Hg(II) in sludge. The text was submitted by the authors in English.