Simultaneous spectrophotometric determination of four preservatives in foodstuffs by multivariate calibration and artificial neural networks

Benzoic acid(BA),methylparaben(MP),propylparaben(PP)and sorbic acid(SA)are food preservatives,and they have well defined UV spectra.However,their spectra overlap seriously,and it is difficult to determine them individually from their mixtures without preseparation.In this paper,seven different chemometric approaches were applied to resolve the overlapping spectra and to determine these compounds simultaneously.With respect to the criteria of%relative prediction error(RPE)and%recovery, principal component...     

Copper interference on the spectrophotometric determination of iron and their simultaneous determination using bathophenantroline-disulfonic acid disodium salt

The interference of copper on the spectrophotometric determination of iron with bathophenantroline-disulfonic acid disodium salt was studied using an experimental design. Copper interferes even below pH 5 [1], forming a yellow complex with bathophenantroline which turned to green after 5 min. This complex showed a maximum at 425 nm with a molar absorptivity of 7.5 × 10³ L mol^–1 cm^–1. Microgram quantities of iron and copper were determined simultaneously in water standard samples using bathophenantroline-disulfonic acid disodium salt at pH 4.8 and measuring the absorbances at two wavelengths. The interference of iron on the copper determination was also estimated. The RSDs of the method for both Cu and Fe were below 1.7%. Recoveries for Cu and Fe were within the ranges 97.2% to 98% and 99.7 to 100.5%, respectively. The method was applied to the determination of copper and iron in the waste water from a water treatment plant. The results obtained by spectrophotometry were compared with those obtained by flame AAS.     

Citrate and calcium determination in flavored vodkas using artificial neural networks

The development of multianalyte sensing schemes by combining indicator-displacement assays with artificial neural network analysis (ANN) for the evaluation of calcium and citrate concentrations in flavored vodkas is presented. This work follows a previous report where an array-less approach was used for the analysis of unknown solutions containing the structurally similar analytes, tartrate and malate. Herein, a two component sensor suite consisting of a synthetic host and the commercially available complexometric dye, xylenol orange, was created. Differential UV-Visible spectral responses result for solutions containing various concentrations of calcium and citrate. The quantitation of the relative calcium and citrate concentrations in unknown mixtures of flavored vodka samples was determined through ANN analysis. The calcium and citrate concentrations in the flavored vodka samples provided by the sensor suite and the ANN methodology described here are compared to values reported by NMR of the same flavored vodkas. We expect that this multianalyte sensing scheme may have potential applications for the analysis of other complex fluids.     

Copper interference on the spectrophotometric determination of iron and their simultaneous determination using bathophenantroline-disulfonic acid disodium salt

The interference of copper on the spectrophotometric determination of iron with bathophenantroline-disulfonic acid disodium salt was studied using an experimental design. Copper interferes even below pH 5 [1], forming a yellow complex with bathophenantroline which turned to green after 5 min. This complex showed a maximum at 425 nm with a molar absorptivity of 7.5 × 10³ L mol^–1 cm^–1. Microgram quantities of iron and copper were determined simultaneously in water standard samples using bathophenantroline-disulfonic acid disodium salt at pH 4.8 and measuring the absorbances at two wavelengths. The interference of iron on the copper determination was also estimated. The RSDs of the method for both Cu and Fe were below 1.7%. Recoveries for Cu and Fe were within the ranges 97.2% to 98% and 99.7 to 100.5%, respectively. The method was applied to the determination of copper and iron in the waste water from a water treatment plant. The results obtained by spectrophotometry were compared with those obtained by flame AAS. Received: 8 July 1996 / Revised: 2 June 1997 / Accepted: 6 June 1997     

Simultaneous kinetic determination of sulfite and sulfide using artificial neural networks

Artificial neural networks (ANNs) are proposed for the determination of sulfite and sulfide simultaneously. The method is based on the reaction between Brilliant Green (BG) as a colored reagent and sulfite and/or sulfide in buffered solution (pH 7.0) and monitoring the changes of absorbance at maximum wavelength of 628 nm. Experimental conditions such as pH, reagents concentrations, and temperature were optimized and training the network was performed using principal components (PCs) of the original data. The network architecture (number of input, hidden and output nodes), and some parameters such as learning rate (η) and momentum (α) were also optimized for getting satisfactory results with minimum errors. The measuring range was 0.05-3.6 μg ml⁻¹ for both analytes. The proposed method has been successfully applied to the quantification of the sulfite and sulfide in different water samples.     

Simultaneous spectrophotometric determination of phosphate and silicate by using principal component artificial neural network

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 microg mL(-1) for phosphate and 0.01-5.00 microg 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.     

PLS regression using real sample calibration for aluminum and iron determination in plant extracts

Real samples were used for PLS model calibration and validation steps, showing that this approach can be of value in preventing deviations in the results caused by the matrix effects for the simultaneous spectrophotometric determination of aluminum and iron in plant extracts. One hundred UV-vis spectra, obtained from samples of the 1997 to 2000 International Plant-Analytical Exchange (IPE) program (The Netherlands), were used for model development, with ICP-AES aluminum and iron determinations as reference values for model calculation. The plant extracts were analyzed both by ICP-AES and by the PLS models developed in this work, using calibrations with both aqueous standard solutions and with real sample extracts. In addition, since the use of smaller calibration sets could be of value in reducing both the cost and the time of analysis, sets with fewer calibration samples were also investigated, with the help of the Kennard and Stone algorithm for sample selection. Comparison of the predictability of the best model obtained with each calibration set was made using the ratio of their relative root mean square error (%RMSEV) for samples in the validation set, for aluminum or iron determinations, and were compared against F-test tabulated values. For all the models developed with real samples, the differences in the %RMSEV values for the aluminum or iron determinations were found not to be statistically significant, at a confidence level of 95%. Although it was observed that the aluminum, but not the iron, determinations with the PLS 2 model prepared with aqueous standards tend to be slightly lower than the ICP-AES determinations, this model has a good global prediction ability, as observed through the correlation curves presented, and can be used for screening determinations or for other agricultural purposes.     

Fast determination of 13C NMR chemical shifts using artificial neural networks

Nine different artificial neural networks were trained with the spherically encoded chemical environments of more than 500000 carbon atoms to predict their 13C NMR chemical shifts. Based on these results the PC-program "C_shift" was developed which allows the calculation of the 13C NMR spectra of any proposed molecular structure consisting of the covalently bonded elements C, H, N, O, P, S and the halogens. Results were obtained with a mean deviation as low as 1.8 ppm; this accuracy is equivalent to a determination on the basis of a large database but, in a time as short as known from increment calculations, was demonstrated exemplary using the natural agent epothilone A. The artificial neural networks allow simultaneously a precise and fast prediction of a large number of 13C NMR spectra, as needed for high throughput NMR and screening of a substance or spectra libraries.     

Simple spectrophotometric determination of Fe in oxalate and HCl soil extracts

We describe a spectrophotometric method to determine the sum of Fe(II) plus Fe(III) in HCl and oxalate extracts. The principle of the method is to reduce Fe(III) by ascorbate in near-neutral solution and to sequester the Fe(II) formed as a tri-ferrozine complex, which is then determined photometrically at 562 nm. Because the complex is stable, the reaction is irreversible and complete. Fe(III) in HCl solution reacted very rapidly, whereas oxalate decelerated the overall reaction so that pseudo first-order kinetics with respect to Fe(III) was detected. However, when extractions were conducted at the recommended soil:solution ratio, the absorption reached 98% of its final value within a few minutes. To test the method, four soils differing considerably in texture and carbonate, organic matter, and Fe(III)(hydr)oxide contents were extracted with oxalate in the dark for amorphous (Fe(o)), and with boiling oxalate for total Fe(III)(hydr)oxide (Fe(bo)). This newly developed spectrophotometric method showed excellent correspondence with the conventional atomic absorption spectroscopy (AAS) method. The method presented here can therefore be used as an alternative method to determine the Fe content of oxalate and hydrochloric acid extracts if AAS is not available. Oxalate extracts low in Fe content, which cannot be diluted, are easier to determine by the photometric method than by AAS.     

Simultaneous spectrophotometric determination of Fe and Ni with xylenol orange using principal component analysis and artificial neural networks in some industrial samples

Artificial neural networks (ANNs) are among the most popular techniques for nonlinear multivariate calibration in complicated mixtures using spectrophotometric data. In this study, Fe and Ni were simultaneously determined in aqueous medium with xylenol orange (XO) at pH 4.0. In this way, after reducing the number of spectral data using principal component analysis (PCA), 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. Adjustable experimental and network parameters were optimized, 30 calibration and 20 prediction samples were prepared over the concentration ranges of 0-400 mug l(-1) Fe and 0-300 mug l(-1) Ni. The resulting R.S.E. of prediction (S.E.P.) of 3.8 and 4.7% for Fe and Ni were obtained, respectively. The method has been applied to the spectrophotometric determination of Fe and Ni in synthetic samples, some Ni alloys, and some industrial waste waters.     

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     

Simultaneous dual wavelength spectrophotometric determination of citric and ascorbic acids using an artificial neural network

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⁺-NH₃ complex (with λ_max = 600 nm) was decomposed by citrate ion and formed a Cu²⁺-citrate complex (with λ_max = 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 Cu²⁺-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. The text was submitted by the authors in English.     

Peptide sequence determination from high-energy collision-induced dissociation spectra using artificial neural networks

This paper reports a newly developed technique that uses artificial neural networks to aid in the automated interpretation of peptide sequence from high-energy collision-induced dissociation (CID) tandem mass spectra of peptides. Two artificial neural networks classify fragment ions before the commencement of an iterative sequencing algorithm. The first neural network provides an estimation of whether fragment ions belong to 1 of 11 specific categories, whereas the second network attempts to determine to which category each ion belongs. Based upon numerical results from the two networks, the program generates an idealized spectrum that contains only a single ion type. From this simplified spectrum, the program’s sequencing module, which incorporates a small rule base of fragmentation knowledge, directly generates sequences in a stepwise fashion through a high-speed iterative process. The results with this prototype algorithm, in which the neural networks were trained on a set of reference spectra, suggest that this method is a viable approach to rapid computer interpretation of peptide CID spectra.     

人工神经网络光度法同时测定土壤中铅-镉-镍

研究了以4－（2－吡啶偶氮）－间苯二酚（PAR）为显色剂、十二烷基硫酸钠（SDS）为增敏剂、乙酸－乙酸钠为缓冲液，在水相中对铅、镉、镍进行同时测定。利用二次回归正交组合设计，对体系因子进行了优化。以苏州吴县水稻田土壤为研究对象，利用反向人工神经网络对其中铅、镉、镍的全量、有效态、活化态分别进行了同时测定，预测结果与AAS法所测结果基本一致。     

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%.     

Simultaneous determination of iodate and periodate by kinetic spectrophotometric method using principal component artificial neural network

A kinetic spectrophotometric method for the simultaneous determination of iodate and periodate in mixtures was proposed. The method was based on the reaction of periodate and iodate with pyrogallol red in sulfuric acid media. The reaction was monitored spectrophotometrically by measuring the decrease in absorbance of pyrogallol red at 470 nm. Kinetic data collected at 470 nm were processed by principle component artificial neural network (PC-ANN) method. The constructed model was able to predict the concentration of two species in the range of 0.1?C15.0 and 0.1?C17.0 ??g/mL for iodate and periodate, respectively. The proposed method was applied to the simultaneous determination of iodate and periodate in several real samples with satisfactory results.     

Potentiometric sensor arrays for the individual determination of penicillin class antibiotics using artificial neural networks

Arrays of potentiometric sensors with plasticized polymer membranes based on tetraalkylammonium organic ion exchanges with anions of penicillin class antibiotics (benzylpenicillin, ampicillin, oxacillin, and amoxicillin) have been proposed for the individual determination of antibiotics in model mixtures and pharmaceutical preparations. The following cross-sensitivity parameters of sensors have been estimated: the average slope of the electrode function (54 < S _av < 61), the nonselectivity factor (2.8 < F < 74.6), and the reproducibility factor (31.9 < K < 61.2). Artificial neural networks have been applied to the treatment of analytical signals from the multisensor system in the concentration range 2.5 × 10⁻⁴–1 × 10⁻¹ M. The average error of the individual determination of penicillin class antibiotics is 5–7%.     

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     

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.     

Spectrophotometric determination of iron and boron in soil extracts using a multi-syringe flow injection system

In the last decade, significant advances in flow analysis have been reported, namely the extensive use of computer-controlled devices to enhance the autonomy and performance of analysers. In the present work, computer-controlled multi-syringe flow injection systems are proposed to perform the spectrophotometric determination of available iron and boron in soil extracts. The methodologies were based on the formation of ferroin complex (determination of iron) and azomethine-H reaction (determination of boron). Both determinations were performed in manifolds with similar configurations by changing the reagents present in the different syringes. In the determination of iron, elimination of Schlieren effect in the detection system was achieved through the binary sampling approach, where a three-way valve was actuated to intercalate small slugs of sample and reagent, promoting better mixing conditions for solutions with different values of refractive index. In the determination of boron, in-line sample blank measurement was attained by omitting the introduction of reagent through software control, without manifold reconfiguration. Linear calibration curves were established between 0.50 and 10.0 mg Fe l⁻¹ and between 0.20 and 4.0 mg B l⁻¹. No systematic difference was found when soil extracts were analysed by the proposed methodologies and compared to the respective reference procedures.