Comparative determination of phosphate and silicate using molybdenum blue by radial basis function and feed-forward neural networks assisted by principal component analysis.

In the present study, chemometric analysis of visible spectral data of phospho-and silico-molybdenum blue complexes was used to develop artificial neural networks (ANNs) for the simultaneous determination of the phosphate and silicate. Combinations of principal component analysis (PCA) with feed-forward neural networks (FFNNs) and radial basis function networks (RBFNs) were built and investigated. The structures of the models were simplified by using the corresponding important principal components as input instead of the original spectra. Number of inputs and hidden nodes, learning rate, transfer functions and number of epochs and SPREAD values were optimized. Performances of methods were tested with root mean square errors prediction (RMSEP, %), using synthetic solutions. The obtained satisfactory results indicate the applicability of this ANN approach based on PCA input selection for determination in highly spectral overlapping. The results obtained by FFNNs and by RBF networks were compared. The applicability of methods was investigated for synthetic samples, for detergent formulations, and for a river water sample.     

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.     

Simultaneous determination of aniline and cyclohexylamine by principal component artificial neural networks.

A specterophotometric method for simultaneous determination of aniline and cyclohexylamine using principal component artificial neural networks is proposed. This method is based on the reactions involving aniline and/or cyclohexylamine, with bis(acetylacetoneethylendiamine)tributylphosphine cobalt(III) perchlorate as a complexing reagent. A nonionic surfactant, Triton X-100, was used for dissolving the complexes and intensifying the signals. The absorption data were based on the spectra registered in the range of 350 - 550 nm. 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. The predictive ability of artificial neural networks was examined for the determination of aniline and cyclohexylamine in synthetic mixtures.     

Sensitive ergotamine determination in pharmaceuticals and biological samples using cloud point preconcentration and spectrofluorimetric detection

A new cloud point extraction (CPE) method for ergotamine analysis using fluorimetric detection is described. Ergotamine from an aqueous solution was preconcentrated into a smaller surfactant-rich phase using nonionic surfactant polyoxyethylene(7.5)nonylphenylether (PONPE 7.5). Differently from the conventional CPE procedure in which the resulting surfactant-rich phase is diluted by a fluidificant before its analysis, in this method the fluorescence measurements were carried out directly onto the undiluted surfactant-rich phase. The high viscosity provided by the undiluted surfactant rich phase greatly improved the fluorescence emission of ergotamine, leading to a total enhancement factor of 1325. This spectral advantage plus the preconcentration factor achieved, contributed to the method sensitivity allowing the ergotamine determination at trace level concentration. Under optimal experimental conditions, a linear calibration curve was obtained from 3.81 × 10⁻⁷ to 1.10 μg mL⁻¹, with detection and quantification limits of 0.11 and 0.38 pg mL⁻¹, respectively. The accuracy and versatility of the present methodology were proved by analyzing ergotamine in real samples of different natures such as pharmaceuticals, urine and saliva.     

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.     

Simultaneous voltammetric determination of levodopa, carbidopa and benserazide in pharmaceuticals using multivariate calibration

An analytical methodology based on differential pulse voltammetry (DPV) on a glassy carbon electrode and the partial least-squares (PLS-1) algorithm for the simultaneous determination of levodopa, carbidopa and benserazide in pharmaceutical formulations was developed and validated. Some sources of bi-linearity deviation for electrochemical data are discussed and analyzed. The multivariate model was developed as a ternary calibration model and it was built and validated with an independent set of drug mixtures in presence of excipients, according with manufacturer specifications. The proposed method was applied to both the assay and the uniformity content of two commercial formulations containing mixtures of levodopa-carbidopa (10:1) and levodopa-benserazide (4:1). The results were satisfactory and statistically comparable to those obtained by applying the reference Pharmacopoeia method based on high performance liquid chromatography. In conclusion, the methodology proposed based on DPV data processed with the PLS-1 algorithm was able to quantify simultaneously levodopa, carbidopa and benserazide in its pharmaceuticals formulations using a ternary calibration model for these drugs in presence of excipients. Furthermore, the model appears to be successful even in the presence of slight potential shifts in the processed data, which have been taken into account by the flexible chemometric PLS-1 approach.     

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.     

Simultaneous kinetic-spectrophotometric determination of carbidopa, levodopa and methyldopa in the presence of citrate with the aid of multivariate calibration and artificial neural networks

The kinetic methodology based on the difference of reaction rates, is based on the reaction between a common oxidizing agents such as tris(1,10-phenanthroline) and iron(III) complex (ferriin, [Fe (phen)₃]³⁺) in the presence of citrate and spectrophotometrically, monitoring the changes of absorbance at the maximum wavelength of 511 nm. Experimental conditions such as pH, reagents and citrate concentrations were optimized, and the data obtained from the experiments were processed by several chemometric approaches, such as artificial neural network (ANN) and partial least squares (PLS). A set of synthetic mixtures of carbidopa (CD), levodopa (LD) and methyldopa (MD) was evaluated and the results obtained by the applications of these chemometric approaches were discussed and compared. It was found that the back propagation artificial neural network (BP-ANN) method afforded better precision relatively than those of radial basis function artificial neural networks (RBF-ANN) and PLS. The proposed method was also applied satisfactorily to the determination of carbidopa, levodopa and methyldopa in real samples.     

Determination of propranolol enantiomers in plasma and urine by spectrofluorimetry and second-order standard addition method

The determination of propranolol enantiomers in human plasma and urine by spectrofluorimetry and a second-order standard addition method is described. The methodology is based on chiral recognition of propranolol by formation of an inclusion complex with β-cyclodextrin, a chiral auxiliary, in the presence of 1-butanol. The adopted strategy combines the use of PARAFAC, for extraction of the pure analyte signal, with the standard addition method, for determinations in the presence of an individual matrix effect caused by the quenching action of the proteins present in the plasma and urine. A specific PARAFAC model was built for each sample, in triplicate, and the scores were related to (R)-propranolol mole fraction using a linear regression in the standard addition method. Using a propranolol with concentration of 260 ng mL⁻¹, good results were obtained for determinations in the mole fraction range from 50 to 80% of (R)-propranolol, providing absolute errors between 0.4 and 3.6% for plasma and between 0.9 and 6.0% for urine.     

Photoinduced spectrofluorimetric determination of fluoroquinolones in human urine by using three- and two-way spectroscopic data and multivariate calibration

Multivariate methods comprise of a group of chemometric tools allowing the analysis of different analytical data, i.e., spectroscopic, chromatographic obtained from multichannel detector systems. Second-way data are widely used in analytical applications in combination with multivariate calibration methods, but three- and higher-way data are yet not as widely applied. In complex biological samples, the employment of the three-way data is of special interest, as they may be combined with methods that exploit the second-order advantage allowing calculating individual concentrations of the analytes of interest in the presence of unknown interferences in untreated samples. A very sensitive and selective method is proposed, by coupling photoinduced fluorescence and multivariate analysis of the three-way data excitation-emission fluorescence matrices (EEMs), of the photoproducts obtained from UV irradiation of three fluoroquinolones: enoxacin (ENO), norfloxacin (NOR) and ofloxacin (OFLO). The application of a previous photoirrradiation process allows the determination of mixtures of ENO, NOR and OFLO, in urine samples at biological levels without sample pretreatments. The resolution ability of N-way partial least squares (N-PLS), parallel factor analysis (PARAFAC) and self weighted alternating trilinear decomposition (SWATLD), is compared with partial least squares (PLS) and unfolded-PLS (U-PLS), in the analysis of ENO, NOR and OFLO in human urine samples.     

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.     

Simultaneous kinetic determination of thiocyanate and sulfide using eigenvalue ranking and correlation ranking in principal component-wavelet neural network

In this work, two toxic compound, sulfide and thiocyanate were determined simultaneously using kinetic spectrophotometry. These anions have shown the catalytic effects on the reaction between iodine and azide. Since the system was nonlinear, a nonlinear model, principal component-wavelet neural network (PC-WNN) was used as the multivariate calibration method. The principal component analysis was used to decrease the dimension of the original matrix. In other words, the scores of the PCs, 5, instead of the original variables, 301, were used as the input for the model. Two methods were used to select the most relevant principal components: eigenvalue ranking and correlation ranking. In this work, eigenvalue and correlation ranking methods have shown better results for thiocyanate and sulfide, respectively, and it can be concluded that these methods are complementary. The WNN has several advantages relative to other types of neural network such as better convergence ability. The data set was divided to calibration, prediction and validation sets. Each set was selected so that the concentrations of the analytes were approximately covered the entire ranges of the analytes. Mean relative error for thiocyanate and sulfide in validation set were 8.5 and 10.6, respectively. Thiocyanate and sulfide can be determined in the range of 60–700 ng ml⁻¹ and 20–400 ng ml⁻¹, respectively. The proposed method was applied for the determination of sulfide and thiocyanate in real samples such as tap, waste and river waters with satisfactory results.     

Experimental design methodologies to optimise the spectrofluorimetric determination of Losartan and Valsartan in human urine

A spectrofluorimetric method has been developed for the determination of two angiotensin II receptor antagonists (ARA II): Losartan and Valsartan. A fractional factorial design and a central composite design were used. The key factors considered in the optimization process were pH, temperature and emission slit width. Maximum fluorescent intensity was established as response for each experiment. The response surfaces confirmed the robustness of the method. A clean-up procedure was used for urine samples that consisted of a solid-phase extraction using C8 cartridges. The total analysis time was lower than 30 min. This method proved to be accurate (RE, 8%), precise (intra- and inter-day coefficients of variation were lower than 8% and sensitive enough (LOQ c.a. 0.5 μg ml⁻¹) to be applied to the determination of Losartan and Valsartan in urine samples.     

Reprint of: Extraction of fluoxetine from aquatic and urine samples using sodium dodecyl sulfate-coated iron oxide magnetic nanoparticles followed by spectrofluorimetric determination

A new method based on the combination of magnetic solid phase extraction (MSPE) and spectrofluorimetric determination was developed for isolation and preconcentration of fluoxetine form aquatic and biological samples using sodium dodecyl sulfate (SDS) coated Fe₃O₄ nanoparticles (NPs) as a sorbent. The unique properties of Fe₃O₄ NPs including high surface area and strong magnetism were utilized effectively in the MSPE process. Effect of different parameters influencing the extraction efficiency of fluoxetine including the amount of Fe₃O₄ and SDS, pH value, sample volume, extraction time, desorption solvent and time were optimized. Under optimized condition, the method was successfully applied to the extraction of fluoxetine from water and urine samples and absolute recovery amount of 85%, detection limit of 20 μg L⁻¹ and a relative standard deviation (RSD) of 1.4% were obtained. The method linear response was over a range of 50–1000 μg L⁻¹ with R² = 0.9968. The relative recovery in different aquatic and urine matrices were investigated and values of 80% to 104% were obtained. The whole procedure showed to be conveniently fast, efficient and economical for extraction of fluoxetine from environmental and biological samples.     

Rapid spectrofluorimetric determination of lisinopril in pharmaceutical tablets using sequential injection analysis

The present work reports for the first time a simple and rapid method for the spectrofluorimetric determination of lisinopril (LSP) in pharmaceutical formulations using sequential injection analysis (SIA). The method is based on reaction of LSP with o-phthalaldehyde (OPA) in the presence of 2-mercaptoethanol (borate buffer medium, pH=10.6). The emission of the derivative is monitored at 455 nm upon excitation at 346 nm. The various chemical and physical conditions that affected the reaction were studied. The calibration curve was linear in the range 0.3–10.0 mg L^–1 LSP, at a sampling rate of 60 injections h^–1. Consumption of OPA reagent was significantly reduced compared with conventional flow injection (FI) systems, because only 50 L of OPA was consumed per run. The method was found to be adequately precise (s_r=2% at 5 mg L^–1 LSP, n=10) and the 3 detection limit was 0.1 mg L^–1. The method was successfully applied to the analysis of two pharmaceutical formulations containing LSP. The results obtained were in good agreement with those obtained by use of high-performance liquid chromatography (HPLC), because the mean relative error, e_r, was <1.8%.     

Simultaneous determination of erlotinib and metabolites in human urine using capillary electrophoresis

The purpose of this study was to develop a simple and sensitive CE‐UV method to quantify erlotinib and metabolites in urine. Following liquid–liquid extraction, erlotinib, and metabolites were separated with a BGE whose composition was phosphate buffer (pH 2.5, 65 mM) with 0.5% Tween 20. The applied voltage was 22 kV, capillary temperature 25°C and the sample injection was performed in the hydrodynamic mode. All the analyses were carried out in a fused silica capillary with an internal diameter of 75 μm and a total length of 37 cm. The detection of target compounds was performed at 240 nm. The calibration was linear in the range 0.15–20 mg/L for erlotinib and metabolites. Inter‐and intraday imprecision were less than 4%. This simple, sensitive, accurate, and cost‐effective method can be used in routine clinical practice to monitor erlotinib concentrations in urine from nonsmall cell lung cancer patients.     

人工神经网络法同时测定苯二酚异构体

研究了人工神经网络法与紫外分光光度分析相结合，用于邻苯二酚、间苯二酚、对苯二酚异构体的含量测定。本研究采用人工神经网络法，直接对苯二酚异构体混合液的紫外吸收光谱数据进行预测，不需预先分离，即可得到各异构体的浓度。     

Automatic identification of terpenoid skeletons by feed-forward neural networks

Feed-forward neural networks (FFNNs) were used to predict the skeletal type of molecules belonging to six classes of terpenoids. A database that contains the ¹³C NMR spectra of about 5000 compounds was used to train the FFNNs. An efficient representation of the spectra was designed and the constitution of the best FFNN input vector format resorted from an heuristic approach. The latter was derived from general considerations on terpenoid structures.     

Calibration in non-linear NIR spectroscopy using principal component artificial neural networks

Near-infrared (NIR) spectroscopy was used in simultaneous, non-destructive analysis of antipyriine and caffeine citrate tablets. Principal component artificial neural networks (PC-ANNs) were used to construct models for the analytes, using the testing set for external validation. Four pretreated spectra, namely, first-derivative, second-derivative, standard normal variate (SNV) and multiplicative scatter correction (MSC) spectra led to simplified and more robust models than conventional spectra. In PC-ANNs models, the spectra data were analyzed by principal component analysis (PCA) firstly. Then the scores of the principal compounds (PCs) were chosen as input nodes for input layer instead of the spectra data. The artificial neural networks (ANNs) models using the spectra data as input nodes were also established, which were compared with the PC-ANNs models. The result shows the SNV model of PC-ANNs multivariate calibration has the lowest training error and predicting error. The concept of the degree of approximation was introduced and performed as the selective criterion of the optimum network parameters.     

Simultaneous determination of thiocyanate and salicylate by a combined UV-spectrophotometric detection principal component artificial neural network

A modified principle component artificial neural network (PC-ANN) model is developed for simultaneous determination of thiocyanate and salycilate concentration after passing through the bulk of a liquid membrane by tri-phenyl benzyl phosphonium chloride. All calibration, and test samples data were obtained using UV-Vis spectrophotometer. In this way, a modified PC-ANN consisting of three layers of nodes was trained by combination of Bayesian-Levenberg-Marquardt as training rule. Sigmoid and liner transfer functions were used in the hidden and output layers respectively to facilitate nonlinear calibration. The model could accurately estimate the concentration of components with acceptable precision and accuracy, for mixtures. The PC-ANN model exhibits a good ability for the simultaneous determination of the thiocyanate and salycilate in concentration range 0.5 x 10(-4) mol.l(-1) up to 5.0 x 10(-4) mol.l(-1) with Root Mean square error (2.22% and 2.20%, for thiocyanate and salycilate, respectively) and high correlation coefficients (R2= 0.998 or greater). Results obtained with modified trained PC-ANN were compared with stepwise linear regression (SMLR) model. Validation of the two models shows a better ability in estimation of the modified PC-ANN as compared with the SMLR model (MSRE given are 3.12%, 6.31%.).