Morteza Atabati

Bee algorithm (BA) is an optimization algorithm inspired by the natural foraging behaviour of honey bees to find the optimal solution which can be proposed to feature selection. In this paper, shuffling cross–validation–BA (CV–BA) was applied to select the best descriptors that could describe the retention factor (log k) in the biopartitioning micellar chromatography (BMC) of 79 heterogeneous pesticides. Six descriptors were obtained using BA and then the selected descriptors were applied for model development using multiple linear regression (MLR). The descriptor selection was also performed using stepwise, genetic algorithm and simulated annealing methods and MLR was applied to model development and then the results were compared with those obtained from shuffling CV–BA. The results showed that shuffling CV–BA can be applied as a powerful descriptor selection method. Support vector machine (SVM) was also applied for model development using six selected descriptors by BA. The obtained statistical results using SVM were better than those obtained using MLR, as the root mean square error (RMSE) and correlation coefficient (R) for whole data set (training and test), using shuffling CV–BA–MLR, were obtained as 0.1863 and 0.9426, respectively, while these amounts for the shuffling CV–BA−SVM method were obtained as 0.0704 and 0.9922, respectively. © 2017 Taylor & Francis Group, LLC.

Aims & Scope: Lipophilicity represents one of the most studied and most frequently used fundamental physicochemical properties. In the present work, harmony search (HS) algorithm is suggested to feature selection in quantitative structure-property relationship (QSPR) modeling to predict lipophilicity of neutral, acidic, basic and amphotheric drugs that were determined by UHPLC. Harmony search is a music-based metaheuristic optimization algorithm. It was affected by the observation that the aim of music is to search for a perfect state of harmony. Materials & Methods: Semi-empirical quantum-chemical calculations at AM1 level were used to find the optimum 3D geometry of the studied molecules and variant descriptors (1497 descriptors) were calculated by the Dragon software. The selected descriptors by harmony search algorithm (9 descriptors) were applied for model development using multiple linear regression (MLR). In comparison with other feature selection methods such as genetic algorithm and simulated annealing, harmony search algorithm has better results. The root mean square error (RMSE) with and without leave-one out cross validation (LOOCV) were obtained 0.417 and 0.302, respectively. Results & Conclusion: The results were compared with those obtained from the genetic algorithm and simulated annealing methods and it showed that the HS is a helpful tool for feature selection with fine performance. © 2017 Bentham Science Publishers

Remediation of water contaminated by organic pollutants is a major challenge, which could be improved by better knowledge on the aqueous solubility of organic compounds. Indeed, the aqueous solubility controls the fate and toxicity of pollutants. Here we performed a structure–property study based on a genetic algorithm for the prediction of aqueous solubility of chlorinated hydrocarbons. 1497 descriptors were calculated with the Dragon software. The variable selection method of the genetic algorithm was used to select an optimal subset of descriptors that have significant contribution to the overall aqueous solubility, from the large pool of calculated descriptors. The support vector machine was then employed to model the possible quantitative relationships between selected descriptors and aqueous solubility. Our results show that total size, polarizability and electronegativity modify the aqueous solubility of compounds. We also found that the support vector machine method gave better results than other methods such as principal component regression and partial least squares. © 2016, Springer International Publishing Switzerland.

Quantitative structure–property relationship (QSPR) studies based on ant colony optimization (ACO) were carried out for the prediction of λmax of 9,10-anthraquinone derivatives. ACO is a meta-heuristic algorithm, which is derived from the observation of real ants and proposed to feature selection. After optimization of 3D geometry of structures by the semi-empirical quantum-chemical calculation at AM1 level, different descriptors were calculated by the HyperChem and Dragon softwares (1514 descriptors). A major problem of QSPR is the high dimensionality of the descriptor space; therefore, descriptor selection is the most important step. In this paper, an ACO algorithm was used to select the best descriptors. Then selected descriptors were applied for model development using multiple linear regression. The average absolute relative deviation and correlation coefficient for the calibration set were obtained as 3.3% and 0.9591, respectively, while the average absolute relative deviation and correlation coefficient for the prediction set were obtained as 5.0% and 0.9526, respectively. The results showed that the applied procedure is suitable for prediction of λmax of 9,10-anthraquinone derivatives. © 2013

A single-probe strip test for the rapid and sensitive detection of miRNA-21 mimics is reported herein. Highly specific structurally responsive bi-functional, thiol and biotin, DNA/LNA oligonucleotide probes (molecular beacons-MB) were designed and conjugated with gold nanoparticles (AuNPs) (i.e. biotin-MB-AuNPs). The proposed design had the ability to modulate the accessibility of the biotin group as a function of the presence of a miRNA target allowing the interaction of the boilable with the streptavidin test zone only in the presence of the miRNA-21 mimics. For quantitative evaluation, images of the strip tests were recorded using a flatbed scanner (Epson Perfection V370 Photo). The colour intensities of the test zones of the strip tests were analysed with the ImageJ software (Scion Corp., USA) and quantified as a function of pixel intensity. The response of the strip test was linear over the range 0.5 to 20 nM miRNA-21 (limit of detection of 115 pM) and showed good reproducibility (intra and inter CVs below 8 %); furthermore, the assay was shown to be highly selective, discriminating other interference miRNAs mimics (e.g. miRNA-221 and miRNA-205). Finally, the proposed strip test was used for detection of miRNA-21 mimics in spiked serum samples, demonstrating its potential for point-of-care clinical applications. Main advantages of the single-probe strip test design are its versatility, simplicity and robustness, which can be easily extended to other miRNA targets by tuning the sequence of the single probe. Furthermore, the use of the structurally responsive single probe is particularly relevant in the case of short-length targets, such as miRNA, whereas a conventional sandwich approach might require a careful control of assay conditions such as hybridization temperature and salt concentration. © 2015 Springer-Verlag Berlin Heidelberg.

Three-dimensional quantitative structure-activity relationship has been performed on 28 aminopyrazolopyridine ureas derivatives to correlate their chemical structures with their observed VEGFR kinase inhibitory activity. The studies include comparative molecular field analysis (CoMFA), CoMFA region focusing and comparative molecular similarity indices analysis (CoMSIA). An alignment rule for the compounds was defined using Distill in SYBYL. Data set was divided into training and test sets using diversity to validate the models. The constructed CoMFA, CoMFA region-focusing and CoMSIA models produced statistically significant results with the cross-validated correlation coefficients (q2) of 0.858, 0.884, and 0.794, noncross-validated correlation coefficients (r 2) of 0.990, 0.991, and 0.930 and predicted correlation coefficients (rpred2) of 0.796, 0.785, and 0.910, respectively. These results ensure the CoMFA and CoMSIA models as a tool to guide the design of series of new potent VEGFR kinase inhibitors. © 2014 Springer Science+Business Media.

A quantitative structure-activity relationship was developed to predict the rate constants for radical degradation of aromatic pollutants in water. A set of 1,508 zero-to three-dimensional descriptors was used for each molecule in the data set. Multiple linear regression was used as a descriptor selection method and the multivariate adaptive regression spline (MARS) method was successfully applied for the mapping model. The root-mean-square error and coefficient of determination were obtained as 0.0996 and 0.8998, respectively. In comparison with other models, the results show that MLR-MARS can be used as a powerful model for prediction of rate constants for radical degradation of aromatic pollutants in water. © 2014 Springer Science+Business Media New York.

A quantitative structure-Activity relationship (QSAR) was developed to predict the toxicity of substituted benzenes to Tetrahymena pyriformis. A set of 1,497 zero-to three-dimensional descriptors were used for each molecule in the data set. A major problem of QSAR is the high dimensionality of the descriptor space; therefore, descriptor selection is one of the most important steps. In this paper, bee algorithm was used to select the best descriptors. Three descriptors were selected and used as inputs for adaptive neuro-fuzzy inference system (ANFIS). Then the model was corrected for unstable compounds (the compounds that can be ionized in the aqueous solutions or can easily metabolize under some conditions). Finally squared correlation coefficients were obtained as 0.8769, 0.8649 and 0.8301 for training, test and validation sets, respectively. The results showed bee-ANFIS can be used as a powerful model for prediction of toxicity of substituted benzenes to T. pyriformis. © Springer Science+Business Media New York 2014.

A new method was developed for prediction of the heats of combustion of important classes of energetic compounds including polynitro arene, polynitro heteroarene, acyclic and cyclic nitramine, nitrate ester and nitroaliphatic compounds. A set of 1497 zero- to three-dimensional descriptors was generated for each molecule in the data set. A major problem of modeling is the high dimensionality of the descriptor space; therefore, descriptor selection is one of the most important steps. In this paper, bee algorithm (BA) was used to select the best descriptors. Bee algorithm is a new population-based optimization algorithm, which is derived from the observation of real bees and proposed to feature selection. Four descriptors were selected and used as inputs for adaptive neuro-fuzzy inference system (ANFIS). Squared correlations of coefficients were obtained as 0.9980, 0.9996 and 0.9988 for training, test and validation sets, respectively. In comparison with genetic algorithm (GA)-ANFIS and multiple linear regression (MLR)-ANFIS, the results showed that Bee-ANFIS can be used as a powerful model for prediction of heats of combustion of these compounds. © 2013 Elsevier B.V.

Many structure-property/activity studies use graph theoretical indices, which are based on the topological properties of a molecule viewed as a graph. Since topological indices can be derived directly from the molecular structure without any experimental effort, they provide a simple and straightforward method for property prediction. In this work the flash point of alkanes was modeled by a set of molecular connectivity indices (χ), modified molecular connectivity indices (mχh t) and valance molecular connectivity indices (mχv), with mχv calculated using the hydrogen perturbation. A stepwise Multiple Linear Regression (MLR) method was used to select the best indices. The predicted flash points are in good agreement with the experimental data, with the average absolute deviation 4.3 K. © 2013 Chemical Industry and Engineering Society of China (CIESC) and Chemical Industry Press (CIP).

A quantitative structure-infinite dilution activity relationship was developed to predict the infinite dilution activity coefficients of halogenated hydrocarbons, γ ∞, in water at 298.15 K. A set of 1,497 zero-to three-dimentional descriptors were used for each molecule in the data set. Classification and regression tree (CART) were successfully used as a descriptor selection method. Three descriptors were selected and used as inputs for the adaptive neuro-fuzzy inference system (ANFIS). The root mean square errors for both calibration and prediction sets are 0.48. The results were compared with those obtained from other models. The results showed that CART-ANFIS can be used as a powerful model for prediction of the infinite dilution activity coefficients of halogenated hydrocarbons. © 2013 Springer Science+Business Media New York.

Although chemical graphs do not show the difference between various atoms and electron lone pairs, the use of pseudo-graphs is a remedy. Modified molecular connectivity indices (mMCIs) have been suggested as showing the role of hydrogen atoms that are also useful in distinguishing isomers. A new algorithm for the δ v number, the basic parameter of molecular connectivity indices (MCIs), has recently been proposed. This algorithm, which is centered on graph concepts such as complete graphs and general graphs, encodes the information of the bonded hydrogen atom on different atoms through a perturbation parameter that requires no new graph concepts. In this study, hydrogen perturbations in valence molecular connectivity indices were applied as structural descriptors for organic compounds in quantitative structure property relationship studies on the molar volume and molar refraction of liquid alkanes, alkenes and alcohols. The results show that, in most cases, these indices give improved correlations compared with the original MCIs. © 2012 Springer Science+Business Media New York.

A quantitative structure-property relationship (QSPR) study was suggested for the prediction of λ max of azo dyes. After optimization of 3D geometry of structures, different descriptors were calculated by the HyperChem and Dragon softwares. A major problem of QSPR is the high dimensionality of the descriptor space; therefore, descriptor selection is the most important step for these studies. In this paper, an ant colony optimization (ACO) algorithm was proposed to select the best descriptors. © 2012 Morteza Atabati. Published by Elsevier B.V. on behalf of Chinese Chemical Society. All rights reserved.

A quantitative structure-melting point relationship was developed to predict the melting point of some pyridinum bromides. A set of 1497 zero- to three-dimensional descriptors were used for each molecule in the data set. Multivariate adaptive regression spline (MARS) was successfully used as a descriptor selection method and also for mapping model. The root mean square error and coefficient of determination were obtained as 17.36 and 0.8750, respectively. The results were compared with those obtained from other model, which after selection of descriptors by MARS, multiple linear regression (MLR) was applied for modeling. The results showed MARS can be used as a powerful model for prediction of melting point of pyridinum bromides.

A sensitive method is proposed for the determination of palladium, based on its catalytic effect on the reduction of malachite green by hypophosphite. The reaction rate is monitored by measuring the current of product of reaction at -0.79 V vs. Ag/AgCl reference electrode. The linear dynamic range is 30.0-300.0 ng/mL with a limit of detection of 10.0 ng/mL. The interference effects of many ions were studied. The method was used for the determination of Pd(II) in synthetic samples of dental alloys with satisfactory results. © 2011 Pleiades Publishing, Ltd.

The H-point standard addition method was applied to kinetic data for simultaneous determination of V(IV) and V(V) or selective determination of V(IV) in presence of V(V). The method is based on the difference in the rate of complex formation between V(IV) and V(V) with methyl thymol blue. The linear dynamic ranges for the two analytes of V(IV) and V(V) are 0.18-3.15 and 0.25-4.00 μg mL-1, respectively. The proposed method was successfully applied for the determination of vanadium in two different oxidation states in several synthetic mixtures and also in blood serum and water samples.

In this work, the applicability of H-point standard addition method (HPSAM) to the kinetic voltammetry data is verified. For this purpose, a procedure is described for the determination of Sb(III) and Sb(V) by adsorptive linear sweep voltammetry using pyrogallol as a complexing agent. The method is based on the differences between the rate of complexation of pyrogallol with Sb(V) and Sb(III) at pH 1.2. The results show that the H-point standard addition method is suitable for the speciation of antimony. Sb(III) and Sb(V) can be determined in the ranges of 0.003-0.120 and 0.010-0.240μgmL-1, respectively. Moreover, the solution is analyzed for any possible effects of foreign ions. The obtained results show that the HPSAM in combination to electroanalytical techniques is a powerful method with high sensitivity and selectivity. The procedure is successfully applied to the speciation of antimony in water samples. © 2010 Elsevier B.V.

Quantitative structure-property relationship (QSPR) studies based on ant colony optimization (ACO) were carried out for the prediction of infinite dilution activity coefficients of hydrocarbons, γ∞, in water at 298.15 K. Semi-empirical quantum-chemical calculations at AM1 level were used to find the optimum 3D geometry of the studied hydrocarbons and different descriptors (1514 descriptors) were calculated by the HyperChem and Dragon softwares. A major problem of QSPR is the high dimensionality of the descriptor space; therefore, descriptor selection is the most important step. In this paper, an ant colony optimization (ACO) algorithm is proposed to select the best descriptors (five descriptors). The root-mean-square deviation using multiple linear regression method for calibration and prediction sets were 0.40 and 0.39, respectively. The resulting data indicate that the proposed method can be used to predict the infinite dilution activity coefficients of hydrocarbons in water, and also reveal that the ACO is a useful tool for descriptor selection with nice performance. © 2010 Elsevier B.V. All rights reserved.

A highly sensitive procedure is presented for the determination of ultra-trace concentration of tungsten by catalytic adsorptive stripping voltammetry. The method is based on adsorptive accumulation of the tungsten-pyrocatechol violet complex onto a hanging mercury drop electrode, followed by reduction of the adsorbed species by voltammetric scan using differential pulse modulation. The reduction current is enhanced catalytically by chlorate. The influence of variables was completely studied by factorial design analysis. Optimum analytical conditions for the determination of tungsten were established. Tungsten can be determined in the range 0.06-12.0 ng/mL with a limit of detection of 0.02 ng/mL. The influence of potentially interfering ions on the determination of tungsten was studied. The procedure was applied to the determination of tungsten in one sandwich polyoxometalate and some synthetic samples similar to alloy compounds with satisfactory results. © Pleiades Publishing, Ltd., 2010.

Ant colony optimization (ACO) is a meta-heuristic algorithm, which is derived from the observation of real ants. In this paper, ACO algorithm is proposed to feature selection in quantitative structure property relationship (QSPR) modeling and to predict λmax of 1,4-naphthoquinone derivatives. Feature selection is the most important step in classification and regression systems. The performance of the proposed algorithm (ACO) is compared with that of a stepwise regression, genetic algorithm and simulated annealing methods. The average absolute relative deviation in this QSPR study using ACO, stepwise regression, genetic algorithm and simulated annealing using multiple linear regression method for calibration and prediction sets were 5.0%, 3.4% and 6.8%, 6.1% and 5.1%, 8.6% and 6.0%, 5.7%, respectively. It has been demonstrated that the ACO is a useful tool for feature selection with nice performance. © 2010 Elsevier B.V.

The use of the classification and regression tree (CART) methodology was studied in a quantitative structure-activity relationship (QSAR) context on a data set consisting of the binding affinities of 39 imidazobenzodiazepines for the α1 benzodiazepine receptor. The 3-D structures of these compounds were optimized using HyperChem software with semiempirical AM1 optimization method. After optimization a set of 1481 zero-to three-dimentional descriptors was calculated for each molecule in the data set. The response (dependent variable) in the tree model consisted of the binding affinities of drugs. Three descriptors (two topological and one 3D-Morse descriptors) were applied in the final tree structure to describe the binding affinities. The mean relative error percent for the data set is 3.20%, compared with a previous model with mean relative error percent of 6.63%. To evaluate the predictive power of CART cross validation method was also performed.

In this work, the applicability of mean centering (MC) of ratio kinetic profiles method to the kinetic voltammetry data is verified. For this purpose, a procedure is described for the determination of Sb(III) and Sb(V) by adsorptive linear sweep voltammetry using pyrogallol (py) as a complexing agent. The method is based on the differences between the rate of complexation of pyrogallol with Sb(V) and Sb(III) at pH 1.2. The results show that the mean centering of ratio kinetic profiles method is suitable for the speciation of antimony. Sb(III) and Sb(V) can be determined in the ranges of 3.0-120.0 and 10.0-240.0 ng mL-1, respectively. Moreover, the solution is analyzed for any possible effects of foreign ions. The obtained results show that the method of MC in combination to electroanalytical techniques is a powerful method with high sensitivity and selectivity. The procedure is successfully applied to the speciation of antimony in pharmaceutical preparations. © 2009 Elsevier B.V. All rights reserved.

A quantitative structure activity relationship analysis (QSAR) has been applied to a data set of 23 derivatives of l-2-[(hydroxyethoxy)methyl]-6- (phenylthio)thymine (HEPT) with their anti-HIV activities. Semi-empirical quantum chemical calculations at the AMI level were used to find the 3D geometry of the studied molecules. Whole numbers of descriptors were calculated with Dragon software, and a subset of calculated descriptors was selected from 1481 Dragon descriptors with the forward stepwise multiple linear regression (MLR) method. Then anti-HIV activities against HIV-1 and four HIV-1 mutant strains containing single mutations in their reverse transcriptases (RTs) were studied, and five equations with excellent statistical qualities were obtained using multiple linear regression. The activities of these compounds were also calculated in predictions, and good correlation coefficients were obtained.

In this study, a very simple spectrophotometric method for the simultaneous determination of citric and ascorbic acid based on the reaction of these acids with a copper(II)-ammonia complex is presented. The Cu2+-NH 3 complex (with λmax = 600 nm) was decomposed by citrate ion and formed a Cu2+-citrate complex (with λmax = 740 nm). On the other hand, during the reaction of ascorbic acid with copper(II)-ammonia complex, ascorbic acid is oxidized and the copper(II)-ammonia complex is reduced to the copper(I)-ammonia complex and the absorbance decreases to 600 nm. Although there is a spectral overlap between the absorbance spectra of complexes Cu2+-NH3 and Cu 2+-citrate, they have been simultaneously determined using an artificial neural network (ANN). The absorbances at 600 and 740 nm were used as the input layer. The ANN architectures were different for citric and ascorbic acid. The output of the citric acid ANN architecture was used as an input node for the ascorbic acid ANN architecture. This modification improves the capability of the ascorbic acid ANN model for the prediction of ascorbic acid concentrations. The dynamic ranges for citric and ascorbic acid were 1.0-125.0 and 1.0-35.0 mM, respectively. Finally, the proposed method was successfully applied to the determination of citric and ascorbic acids in vitamin C tablets and some powdered drink mixes. © Pleiades Publishing, Ltd., 2008.

A quantitative structure-property relationship (QSPR) study based on the wavelet neural network (WNN) technique was performed for the prediction of gas chromatography retention indexes of methyl-substituted alkanes produced by insects. In addition to the simple structural descriptors, semi-empirical quantum chemical calculations at the AMI (Austin Model 1) level were used to find the optimum 3D geometry of the studied molecules and a numbers of descriptors were calculated with HyperChem and Dragon software. A stepwise MLR (Multiple Linear Regression) method was used to select the best descriptors, and the selected descriptors were used as input neurons in a wavelet neural network model. The average relative error was 2.2%.

Quantitative structure-property relationship (QSPR) studies based on artificial neural network (ANN) and wavelet neural network (WNN) techniques were carried out for the prediction of solvent polarity. Experimental S′ values for 69 solvents were assembled. This set included saturated and unsaturated hydrocarbons, solvents containing halogen, cyano, nitro, amide, sulfide, mercapto, sulfone, phosphate, ester, ether, etc. Semi-empirical quantum chemical calculations at AM1 level were used to find the optimum 3D geometry of the studied molecules and different quantum-chemical descriptors were calculated by the HyperChem software. A stepwise MLR method was used to select the best descriptors and the selected descriptors were used as input neurons in neural network models. The results obtained by the two methods were compared and it was shown that in WNN, the convergence speed was faster and the root mean square error of prediction set was also smaller than ANN. The average relative error in WNN was 7.9 and 6.8% for calibration and prediction set, respectively, and the results showed the ability of the WNN developed here to predict solvent polarity. 2007 © The Japan Society for Analytical Chemistry.

A very sensitive, simple and selective spectrophotometric method for simultaneous determination of phosphate and silicate based on formation of phospho- and silicomolybdenum blue complexes in the presence of ascorbic acid is described. Although the complexes of phosphate and silicate with reagent in the presence of ascorbic acid show a spectral overlap, they have been simultaneously determined by principal component artificial neural network (PC-ANN). The PC-ANN architectures were different for phosphate and silicate. The output of phosphate PC-ANN architecture was used as an input for silicate PC-ANN architecture. This modification improves the capability of silicate PC-ANN model for prediction of silicate concentrations. The linear range was 0.01-3.00 μg mL-1 for phosphate and 0.01-5.00 μg mL -1 for silicate. Interference effects of common anions and cations were studied and the proposed method was also applied satisfactorily to the determination of phosphate and silicate in detergents.

The H-point standard addition method was applied to kinetic data for simultaneous determination of citric and ascorbic acid or selective determination of ascorbic acid in presence of citric acid. The method is based on the difference in the rate of reaction of citric and ascorbic acid with copper(II)-ammonia complex. The linear dynamic ranges for the two analytes of citric and ascorbic acid are 0.80-1.15 × 102 and 0.70-10.00 mM, respectively. The proposed method was successfully applied for the determination of citric and ascorbic acid in some powdered drink mixtures and vitamin C tablet.

A very simple spectrophotometric method for simultaneous determination of aluminum(III) and iron(III) based on formation of their complexes with pyrocatechol violet (PCV) in micellar media, using the H-point standard addition method (HPSAM), is described. In micellar media, the metal complexes of Al-PCV and Fe-PCV are formed very fast. Formation of both of the complexes was complete within 5 min at pH 8.5. The linear ranges for aluminum and iron were 0.05-2.50 and 0.10-4.00 μg mL-1, respectively. The relative standard deviation (R.S.D.) for the simultaneous determination 0.40 μg mL-1 of Al(III) and 0.20 μg mL-1 of Fe(III) were 3.24% and 4.22%, respectively. Interference effects of common anions and cations were studied. Themethod was applied to simultaneous determination of Al(III) and Fe(III) in standard reference material and alloy samples.

A highly sensitive procedure is presented for the determination of ultra-trace concentration of molybdenum by catalytic adsorptive stripping voltammetry. The method is based on adsorptive accumulation of the molybdenum (Mo)-pyrocatechol violet (PCV) complex on to a hanging mercury drop electrode, followed by reduction of the adsorbed species by voltammetric scan using differential pulse modulation. The reduction current is enhanced catalytically by chlorate. The influence of variables was completely studied by factorial design analysis. Optimum analytical conditions for the determination of molybdenum were established. Molybdenum can be determined in the range 1.0 × 10-3-100.0 ng ml-1 with a limit of detection of 0.2 pg ml-1. The influence of potential interfering ions on the determination of molybdenum was studied. The procedure was applied to the determination of molybdenum in mineral water and some analytical grade substances with satisfactory results. © 2005 Elsevier B.V. All rights reserved.

Principal component-artificial neural network (PC-ANN) and principal component-wavelet neural network (PC-WNN) are applied for simultaneous determination of iron(II), nickel(II), and cobalt(II). A simple and selective spectrophotometric method for simultaneous determination of iron(II), nickel(II), and cobalt(II) based on formation of their complexes with 1-(2-pyridylazo)-2-naphtol (PAN) in micellar media is described. Although the complexes of Fe(II), Ni(II), and Co(II) with reagent show a spectral overlap, they have been simultaneously determined by PC-ANN and PC-WNN. The results obtained by the two methods were compared and it was shown that in PC-WNN, the convergence speed was faster and the root mean square error of prediction set was also smaller than PC-ANN. Interference effects of common anions and cations were studied and the proposed method was also applied satisfactorily to the determination of Fe(II), Ni(II) and Co(II) in synthetic samples. Copyright © Taylor & Francis Group, LLC.

A very simple and selective spectrophotometric method for simultaneous determination of iron(II), nickel(II) and cobalt(II) based on formation of their complexes with 1-(2-pyridylazo)-2-naphtol (PAN) in micellar media is described. Although the complexes of Fe(II), Ni(II) and Co(II) with reagent show a spectral overlap, they have been simultaneously determined by partial least squares (PLS) with and without preprocessing step using direct orthogonal signal correction (DOSC). The linear range was 0.30-4.50 μg ml-1 for Co(II), 0.20-3.00 μg ml-1 for Ni(II) and 0.30-5.00 μg ml -1 for Fe(II). The results obtained by the PLS and DOSC-PLS were statistically compared. Interference effects of common anions and cations were studied and the proposed method was also applied satisfactorily to the determination of Fe(II), Ni(II) and Co(II) in synthetic samples. © 2005 Elsevier B.V. All rights reserved.

A quantitative structure-property relationship (QSPR) study based on the artificial neural network (ANN) technique was performed for the prediction of gas chromatography retention indexes of methyl-substituted alkanes produced by insects. Simple descriptors such as the total number of carbons in the backbone, the number of the multiple methyl groups attached to the carbon chain, and their relative positions were selected, and an ANN with a 9 : 8 : 1 architecture was generated using the nine descriptors in the input layer. The average relative error was 3.3%. The method was also compared with the QSPR method, which utilizes topological and quantum chemical descriptors. © 2005 Pleiades Publishing, Inc.

The use of chemometric approaches for the simultaneous determination of Fe(II) and Fe(III) ions has been explored by means of a two component reagent. Mixed reagents of 1,10-phenanthroline and thiocyanate were used as a selective chromogenic system for speciation of Fe(II) and Fe(III). Although the complexes of Fe(II) and Fe(III) with mixed reagent show a spectral overlap, they have been simultaneously determined with chemometric approaches, such as principal component artificial neural network (PC-ANN), principal component regression (PCR) and partial least squares (PLS). A set of synthetic mixtures of Fe(II) and Fe(III) was evaluated and the results obtained by the applications of these chemometric approaches were discussed and compared. It was found that the PC-ANN and PLS methods afforded better precision relatively than its of PCR. PC-ANN and PLS methods were also applied satisfactorily in determination of Fe(II) and Fe(III) in pharmaceutical samples. © 2004 Elsevier SAS. All rights reserved.