Determination of tin in marine sediment slurries by electrothermal atomic absorption spectrometry using palladium-magnesium nitrate as chemical modifier

 A method was optimized for the determination of tin in slurries of marine sediment samples using palladium-magnesium nitrate as chemical modifier and Triton X-100 (0.1%) surfactant as stabilizer. To obtain a complete pyrolysis of the slurry sample, two charring steps were used, the first at 480 °C and the second at 1300 °C. The precision and accuracy of the method were studied using the Reference Material PACS-1 (marine sediment) from National Research Council Canada. The detection limit (LOD) was 57.6 μg kg^-1. The method was applied to the determination of tin in marine sediment samples from the Galicia coast. Received: 16 February 1996/Revised: 26 March 1996/Accepted: 12 April 1996     

A comparative study of laser induced breakdown spectroscopy analysis for element concentrations in aluminum alloy using artificial neural networks and calibration methods

A comparative study of analysis methods (traditional calibration method and artificial neural networks (ANN) prediction method) for laser induced breakdown spectroscopy (LIBS) data of different Al alloy samples was performed. In the calibration method, the intensity of the analyte lines obtained from different samples are plotted against their concentration to form calibration curves for different elements from which the concentrations of unknown elements were deduced by comparing its LIBS signal with the calibration curves. Using ANN, an artificial neural network model is trained with a set of input data of known composition samples. The trained neural network is then used to predict the elemental concentration from the test spectra. The present results reveal that artificial neural networks are capable of predicting values better than traditional method in most cases.     

Measurement of lead isotope ratios in wine by ICP-MS and its applications to the determination of lead concentration by isotope dilution

 Methods are described for the accurate and precise determination of lead concentrations and the isotopic composition of lead in wine samples using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). This method needs little sample preparation. A comparison with lead isotope ratios measured by Thermal Ionization Mass Spectrometry (TIMS) in three wine samples reveals a good agreement between the two techniques. The lead concentration in three certified wine samples were measured by isotope dilution (ID) and the results are compared with those obtained by external calibration ICP-MS. Received: 10 June 1996/Revised: 23 September 1996/Accepted: 30 September 1996     

A pilot study on colonic mucosal tissues by fluorescence spectroscopy technique: Discrimination by principal component analysis (PCA) and artificial neural network (ANN) analysis

Pulsed laser‐induced autofluorescence spectra of pathologically certified normal and malignant colonic mucosal tissues were recorded at 325 nm excitation. The spectra were analysed using three different methods for discrimination purposes. First, all the spectra were subjected to the principal component analysis (PCA) and the discrimination between normal and malignant cases were achieved using parameters like, spectral residuals, Mahalanobis distance and scores of factors. Second, to understand the changes in tissue composition between the two classes (normal, and malignant), difference spectrum was constructed by subtracting mean spectrum of calibration set samples from simulated mean of all spectra of any one class (normal/malignant) and in third, artificial neural network (ANN) analysis was carried out on the same set of spectral data by training the network with spectral features like, mean, median, spectral residual, energy, standard deviation, number of peaks for different thresholds (100, 250 and 500) after carrying out 1st‐order differentiation of the training set samples and discrimination between normal and malignant conditions were achieved. The specificity and sensitivity were determined in PCA and ANN analyses and they were found to be 100 and 91.3% in PCA, and 100 and 93.47% in ANN, respectively. Copyright © 2008 John Wiley & Sons, Ltd.     

Catechol determination in compost bioremediation using a laccase sensor and artificial neural networks

An electrochemical biosensor based on the immobilization of laccase on magnetic core-shell (Fe₃O₄–SiO₂) nanoparticles was combined with artificial neural networks (ANNs) for the determination of catechol concentration in compost bioremediation of municipal solid waste. The immobilization matrix provided a good microenvironment for retaining laccase bioactivity, and the combination with ANNs offered a good chemometric tool for data analysis in respect to the dynamic, nonlinear, and uncertain characteristics of the complex composting system. Catechol concentrations in compost samples were determined by using both the laccase sensor and HPLC for calibration. The detection range varied from 7.5 × 10^–7 to 4.4 × 10^–4 M, and the amperometric response current reached 95% of the steady-state current within about 70 s. The performance of the ANN model was compared with the linear regression model in respect to simulation accuracy, adaptability to uncertainty, etc. All the results showed that the combination of amperometric enzyme sensor and artificial neural networks was a rapid, sensitive, and robust method in the quantitative study of the composting system. Figure Structure of the magnetic carbon paste electrode used in the electrochemical biosensor     

Determination of total polyphenol index in wines employing a voltammetric electronic tongue

This work reports the application of a voltammetric electronic tongue system (ET) made from an array of modified graphite-epoxy composites plus a gold microelectrode in the qualitative and quantitative analysis of polyphenols found in wine. Wine samples were analyzed using cyclic voltammetry without any sample pretreatment. The obtained responses were preprocessed employing discrete wavelet transform (DWT) in order to compress and extract significant features from the voltammetric signals, and the obtained approximation coefficients fed a multivariate calibration method (artificial neural network-ANN-or partial least squares-PLS-) which accomplished the quantification of total polyphenol content. External test subset samples results were compared with the ones obtained with the Folin–Ciocalteu (FC) method and UV absorbance polyphenol index (I₂₈₀) as reference values, with highly significant correlation coefficients of 0.979 and 0.963 in the range from 50 to 2400 mg L⁻¹ gallic acid equivalents, respectively. In a separate experiment, qualitative discrimination of different polyphenols found in wine was also assessed by principal component analysis (PCA).     

Differential Kinetic Spectrophotometric Determination of Methamidophos and Fenitrothion in Water and Food Samples by Use of Chemometrics

A spectrophotometric method for simultaneous analysis of methamidophos and fenitrothion was proposed by application of chemometrics to the spectral kinetic data, which was based upon the difference in the inhibitory effect of the two pesticides on acetylcholinesterase (AChE) and the use of 5,5′‐dithiobis(2‐nitrobenzoic acid) (DTNB) as a chromogenic reagent for the thiocholine iodide (TChI) released from the acetylthiocholine iodide (ATChI) substrate. The absorbance of the chromogenic product was measured at 412 nm. The different experimental conditions affecting the development and stability of the chromogenic product were carefully studied and optimized. Linear calibration graphs were obtained in the concentration range of 0.5–7.5 ng·mL^?1 and 5–75 ng·mL^?1 for methamidophos and fenitrothion, respectively. Synthetic mixtures of the two pesticides were analysed, and the data obtained processed by chemometrics, such as partial least square (PLS), principal component regression (PCR), back propagation‐artificial neural network (BP‐ANN), radial basis function‐artificial neural network (RBF‐ANN) and principal component‐radial basis function‐artificial neural network (PC‐RBF‐ANN). The results show that the RBF‐ANN gives the lowest prediction errors of the five chemometric methods. Following the validation of the proposed method, it was applied to the determination of the pesticides in several commercial fruit and vegetable samples; and the standard addition method yielded satisfactory recoveries.     

Fluorescent sensing layer for the determination of<Emphasis Type="SmallCaps"> L</Emphasis>-malic acid in wine

An enzymatic method for determining L-malic acid in wine based on an L-malate sensing layer with nicotinamide adenine dinucleotide (NAD⁺), L-malate dehydrogenase (L-MDH) and diaphorase (DI), immobilized by sol-gel technology, was constructed and evaluated. The sol-gel glass was prepared with tetramethoxysilane (TMOS), water and HCl. L-MDH catalyzes the reaction between L-malate and NAD⁺, producing NADH, whose fluorescence (λ _exc = 340 nm, λ _em = 430 nm) could be directly related to the amount of L-malate. NADH is converted to NAD⁺ by applying hexacyanoferrate(III) as oxidant in the presence of DI. Some parameters affecting sol-gel encapsulation and the pH of the enzymatic reaction were studied. The sensing layer has a dynamic range of 0.1–1.0 g/L of L-malate and a long-term storage stability of 25 days. It exhibits acceptable reproducibility [s _r(%)≈10] and allows six regenerations. The content of L-malic acid was determined for different types of wine, and polyvinylpolypyrrolidone (PVPP) was used as a bleaching agent with red wine. The results obtained for the wine samples using the sensing layer are comparable to those obtained from a reference method based on UV-vis molecular absorption spectrometry, if the matrix effect is corrected for.     

Liquid chromatography time-of-flight mass spectrometry following sorptive microextraction for the determination of fungicide residues in wine

This work evaluates the suitability of sorptive microextraction, using disposable silicone sorbents, and liquid chromatography time-of-flight mass spectrometry (LC-TOF-MS) for the determination of 15 fungicides in wine. Under optimized conditions, wine samples (10 mL) were diluted with the same volume of ultrapure water and poured in a glass vessel containing a magnetic stirrer and 4 g of sodium chloride. Extractions were performed at room temperature for 4 h, using an inexpensive silicone disk (12 μL volume) exposed directly to the sample. Thereafter, analytes were recovered with 0.2 mL of acetonitrile. The electrospray ionization (ESI) source was operated in the fast polarity switching mode obtaining, in the same injection, selective LC-MS records (extracted with a mass window of 10 ppm) of compounds rendering [M + H]⁺ and [M-H]⁻ ions. The method provided limits of quantification (LOQs) between 0.1 and 2.2 ng mL⁻¹, linear response ranges up to 500 ng mL⁻¹, relative recoveries from 75% to 117% and an inter-day variability below 15% for all analytes in red and white wine samples. The feasibility of in situ sample enrichment followed by delayed desorption and analysis is also assessed.     

Single-step calibration, prediction and real samples data acquisition for artificial neural network using a CCD camera

An artificial neural network (ANN) model is developed for simultaneous determination of Al(III) and Fe(III) in alloys by using chrome azurol S (CAS) as the chromogenic reagent and CCD camera as the detection system. All calibration, prediction and real samples data were obtained by taking a single image. Experimental conditions were established to reduce interferences and increase sensitivity and selectivity in the analysis of Al(III) and Fe(III). In this way, an artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. Sigmoid transfer functions were used in the hidden and output layers to facilitate nonlinear calibration. Both Al(III) and Fe(III) can be determined in the concentration range of 0.25-4 μg ml⁻¹ with satisfactory accuracy and precision. The proposed method was also applied satisfactorily to the determination of considered metal ions in two synthetic alloys.     

Colorimetric determination of iron in infant fortified formulas by sequential injection analysis

 A procedure is described for the colorimetric determination of iron in infant fortified formulas based on sequential injection analysis (SIA). Iron(III) complexation with thiocyanate is used as colour developing reaction. The system enables the determination of iron in the samples (after digestion by dry ashing and treatment with 0.2 mol/L nitric acid in the range of 0.50–20.0 mg/L, consuming 140 μL of the sample and 8 mg thiocyanate per determination. The reactor geometry and the adjustment of the ionic content of the calibration solutions is important for the accuracy of the results. A regression line according to the equation [Fe(III) (mg/L)]_SIA=−0.3(±0.4)+1.03(±0.04) [Fe(III) (mg/L)]_FAAS was obtained after comparative analysis of a set of 12 samples. The measurement rate was 34 s, thus allowing to analyze 100 samples per hour with a relative standard deviation lower than 2%. Received: 30 July 1996/Revised: 1 October 1996/Accepted: 4 October 1996     

Artificial neural network for the evaluation of CO2 corrosion in a pipeline steel

This paper presents a predictive model for the determination of different types of corrosion by using electrochemical impedance spectroscopy curves and artificial neural network. This proposed model obtains predictions for three different types of corrosion by using Nyquist impedance curves from four input variables: inhibitor concentration, time of exposure, and the real and imaginary experimental component of these curves. The model takes into account the variations of inhibitor concentration over steel to decrease the corrosion rate. For the network, the Levenberg–Marquardt learning algorithm, the hyperbolic tangent sigmoid transfer function and the linear transfer function were used. The best fitting training data set was obtained with five neurons in the hidden layer, which made possible to predict satisfactory efficiency (R > 0.99). On the validation of the data set, simulations and theoretical data tests were in good agreement (R > 0.9905). The developed model can be used for the determination of the type of curves related to the nature phenomena and rate of corrosion at the metal surface.     

Solid-phase spectrophotometric microdetermination of iron with ascorbic acid and ferrozine

 A very sensitive and selective method for the determination of trace amounts of iron has been developed, based on the reduction of Fe(III) to Fe(II) by ascorbic acid, followed by chromogenic chelation of Fe(II) with ferrozine. The complex Fe(II)-ferrozine is easily sorbed on a dextran-type anion-exchange gel packed in a 1 mm cell, and the absorbance of the gel is measured directly at 569 and 800 nm. The extended linear range of the determination is 0.5–10 ng ml^-1 of iron (apparent molar absorptivity=4.4×10⁷ l mol^-1 cm^-1) and the precision (RSD) 1.3% for a concentration of 5 ng ml^-1 of iron (n=10). The detection limit for a sample volume of 1000 ml, using 0.040 g of anion-exchanger, corresponds to 0.12 ng ml^-1. The method has been successfully applied to the determination of iron in natural and waste waters, wine, soil extract and previously digested vegetal tissues, drugs and human hair. Received: 20 November 1995/Revised: 23 January 1996/Accepted: 26 January 1996     

Artificial Neural Network and Fuzzy Clustering – New Tools for Evaluation of Depth Profile Data?

 Depth profiling has been performed by using Auger electron spectrometry (AES) and X-ray photoelectron spectrometry (XPS) in combination with Ar-ion sputtering. The data obtained by both surface-analytical methods have been evaluated by means of factor analysis and afterwards by applying an artificial neural network or fuzzy clustering in order to determine the compositional layering of different samples such as a Cr₂O₃/CrN sandwich layer, tarnish layers on a nickel based alloy and on steel, and the coating of a Si₃N₄ ceramic powder. The applied artificial neural network was a Kohonen network. It turned out that the method of fuzzy c-means clustering was more successful than Kohonen network due to the fact that fuzzy c-means clustering starts with more input information which can be obtained from factor analysis.     

Nondestructive Quantitative Analysis of Analgini Powder Pharmaceutical by Near-Infrared Spectroscopy and Artifical Neural Network Technique

ABSTRACT

This paper demonstrates the usefulness of near-infrared (NIR) spectra and artificial neural network (ANN) in nondestructive quantitative analysis of pharmaceuticals. Real data sets from near-infrared reflectance spectra of analgini powder pharmaceutical were used to build up an artificial neural network to predict unknown samples. The parameters affecting the network were discussed. A new network evaluation criterion, the degree of approximation, was employed. The overfitting was discussed. Owing to the good nonlinear multivariate calibration nature of ANN, the predicted result was reliable and precise. The relative error of unknown samples was less than 2.5%     

Evaluation of on-line coupling size exclusion chromatography electrothermal atomic absorption spectrometry for selenium speciation

 Chromatographic effluents were on-line analyzed by Zeeman-ETAAS, using a flow-through cell placed in a graphite furnace autosampler as interface. To obtain high sampling rates, the use of fast graphite furnace programmes was studied. Conventional programmes of 96 s were reduced to 18 s by using hot injection (120 °C) and reducing the charring step to 2 s. The increase of the injection volume from 20 to 60 μl lengthened the programme to 46 s. Nickel had to be added to get a comparable response for both inorganic and organic selenium species (selenite and selenomethionine) and to reduce the interferent effect produced in presence of the chromatographic eluent (TRIS 0.01 mol l^-1, NH₄NO₃ 0.1 mol l^-1, pH 7). The optimized conditions were applied to the speciation of selenium in human erythrocyte lysates by size exclusion LC-ETAAS. Using a high performance size exclusion column selenium could be assigned to proteins of 100 and 35 kDa. Detection limits in the range of 1 ng (2 μg l^-1 for 500 μl injection volume) were obtained for the combined technique. Received: 9 October 1996/Revised: 8 July 1996/Accepted: 14 July 1996     

Artificial neural network for simultaneous determination of two components of compound paracetamol and diphenhydramine hydrochloride powder on NIR spectroscopy

Diffuse reflectance near-infrared (NIR) spectroscopy is a technique widely used for rapid and non-destructive analysis of solid samples. A method for simultaneous analysis of the two components of compound paracetamol and diphenhydramine hydrochloride powdered drug has been developed by using artificial neural network (ANN) on near-infrared (NIR) spectroscopy. An ANN containing three layers of nodes was trained. Various ANN models based on pretreated spectra (first-derivative, second-derivative and standard normal variate; SNV) were tested and compared, respectively. In the models the concentration of paracetamol and caffeine as active principles of compound paracetamol and diphenhydramine hydrochloride powder was determined simultaneously. Partial least squares regression (PLS) multivariate calibrations were also used, which were compared with ANN. The best model was obtained at first-derivative spectra. We have also discussed the parameters that affected the networks and predicted the test set (unknown) specimens. The degree of approximation, a new evaluation criteria of the network were employed, which proved the accuracy of the predicted results.