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.     

Quantitative analysis of levodopa, carbidopa and methyldopa in human plasma samples using HPLC-DAD combined with second-order calibration based on alternating trilinear decomposition algorithm

An HPLC method combined with second-order calibration based on alternating trilinear decomposition (ATLD) algorithm has been developed for the quantitative analysis of levodopa (LVD), carbidopa (CBD) and methyldopa (MTD) in human plasma samples. Prior to the analysis of the analytes by ATLD algorithm, three time regions of chromatograms were selected purposely for each analyte to avoid serious collinearity. Although the spectra of these analytes were similar and interferents coeluted with the analytes studied in biological samples, good recoveries of the analytes could be obtained with HPLC-DAD coupled with second-order calibration based on ATLD algorithm, additional benefits are decreasing times of analysis and less solvent consumption. The average recoveries achieved from ATLD with the factor number of 3 (N = 3) were 100.1 ± 2.1, 96.8 ± 1.7 and 104.2 ± 2.6% for LVD, CBD and MTD, respectively. In addition, elliptical joint confidence region (EJCR) tests as well as figures of merit (FOM) were employed to evaluate the accuracy of the method.     

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.     

Kinetic approach for the enzymatic determination of levodopa and carbidopa assisted by multivariate curve resolution-alternating least squares

A combination of kinetic spectroscopic monitoring and multivariate curve resolution-alternating least squares (MCR-ALS) was proposed for the enzymatic determination of levodopa (LVD) and carbidopa (CBD) in pharmaceuticals. The enzymatic reaction process was carried out in a reverse stopped-flow injection system and monitored by UV-vis spectroscopy. The spectra (292-600 nm) were recorded throughout the reaction and were analyzed by multivariate curve resolution-alternating least squares. A small calibration matrix containing nine mixtures was used in the model construction. Additionally, to evaluate the prediction ability of the model, a set with six validation mixtures was used. The lack of fit obtained was 4.3%, the explained variance 99.8% and the overall prediction error 5.5%. Tablets of commercial samples were analyzed and the results were validated by pharmacopeia method (high performance liquid chromatography). No significant differences were found (α = 0.05) between the reference values and the ones obtained with the proposed method. It is important to note that a unique chemometric model made it possible to determine both analytes simultaneously.     

Simultaneous determination of rifampicin and isoniazid by continuous-flow chemiluminescence with artificial neural network calibration

In this paper a continuous-flow chemiluminescence (CL) system with artificial neural network calibration is proposed for simultaneous determination of rifampicin and isoniazid. This method is based on the different kinetic spectra of the analytes in their CL reaction with alkaline N-bromosuccinimide as oxidant. The CL intensity was measured and recorded every second from 1 to 300 s. The data obtained were processed chemometrically by use of an artificial neural network. The experimental calibration set was 20 sample solutions. The relative standard errors of prediction for both analytes were approximately 5%. The proposed method was successfully applied to the simultaneous determination of rifampicin and isoniazid in a combined pharmaceutical formulation.     

Simultaneous determination of three organophosphorus pesticides residues in vegetables using continuous-flow chemiluminescence with artificial neural network calibration

In this article, a continuous-flow chemiluminescence (CL) system with artificial neural network calibration is proposed for simultaneous determination of three organophosphorus pesiticides residues. This method is based on the fact that organophosphorus pesticides can be decomposed into orthophosphate with potassium peroxodisulphate as oxidant under ultraviolet radiation and that the decomposing kinetic characteristics of the organophosphorus pesticides with different molecular structure are significantly different. The produced orthophosphate can react with molybdate and vanadate to form the vanadomolybdophosphoric heteropoly acid, which can oxidize luminol to produce intense CL emission. The CL intensity of the solution was measured and recorded every 2 s in the range of 0-250 s. The obtained data were processed chemometrically by use of a three-layered feed-forward artificial neural network trained by back-propagation learning algorithm, in which input node, hidden node and output nodes were 65, 21 and 3, respectively. The proposed multi-residue analysis method was successfully applied to the simultaneous determination of the three organophosphorus pesticides residue in some vegetables samples.     

Spectrophotometric determination of metal ions in electroplating solutions in the presence of EDTA with the aid of multivariate calibration and artificial neural networks

Metal ions such as Co(II), Ni(II), Cu(II), Fe(III) and Cr(III), which are commonly present in electroplating baths at high concentrations, were analysed simultaneously by a spectrophotometric method modified by the inclusion of the ethylenediaminetetraacetate (EDTA) solution as a chromogenic reagent. The prediction of the metal ion concentrations was facilitated by the use of an orthogonal array design to build a calibration data set consisting of absorption spectra collected in the 370-760 nm range from solution mixtures containing the five metal ions earlier. With the aid of this data set, calibration models were built based on 10 different chemometrics methods such as classical least squares (CLS), principal component regression (PCR), partial least squares (PLS), artificial neural networks (ANN) and others. These were tested with the use of a validation data set constructed from synthetic solutions of the five metal ions. The analytical performance of these chemometrics methods were characterized by relative prediction errors and recoveries (%). On the basis of these results, the computational methods were ranked according to their performances using the multi-criteria decision making procedures preference ranking organization method for enrichment evaluation (PROMETHEE) and geometrical analysis for interactive aid (GAIA). PLS and PCR models applied to the spectral data matrix that used the first derivative pre-treatment were the preferred methods. They together with ANN-radial basis function (RBF) and PLS were applied for analysis of results from some typical industrial samples analysed by the EDTA-spectrophotometric method described. DPLS, DPCR and the ANN-RBF chemometrics methods performed particularly well especially when compared with some target values provided by industry.     

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...     

Simultaneous spectrofluorimetric determination of levodopa and propranolol in urine using feed-forward neural networks assisted by principal component analysis

The simultaneous determination of levodopa (LD) and propranolol (PRO) using fluorescence spectrometric technique is described. The method involves measuring the natural fluorescence of these drugs in the micellar media of sodium dodecyl sulfate (SDS) using principal component analysis-feed-forward neural networks (PC-FFNNs). Experimental conditions such as effect of pH and SDS concentration were optimized. Under the optimum conditions, the linear determination ranges of LD and PRO are 2.0 × 10⁻⁸ to 1.0 × 10⁻⁵ mol L⁻¹ and 3.6 × 10⁻⁹ to 1.8 × 10⁻⁶ mol L⁻¹, respectively. A set of synthetic binary mixtures of LD and PRO was prepared and their concentrations were predicted by the proposed method. Satisfactory results were obtained by the combination of fluorescence technique with chemometrics methods. The method was successfully applied to the determination of LD and PRO in tap water and in urine samples.     

Simultaneous determination of Co, Ni, Cu and Zn ions in foodstuffs and vegetables with a new Schiff base using artificial neural networks

New complexes of Co²⁺, Ni²⁺, Cu²⁺ and Zn²⁺ with a recently synthesized Schiff base derived from 3,6-bis((aminoethyl)thio)pyridazine were applied for their simultaneous determination with artificial neural networks. The analytical data show the ratio of metal to ligand in all metal complexes is 1:1. The absorption spectra were evaluated with respect to Schiff base concentration, pH and time of the color formation reactions. It was found that at pH 10.0 and 60 min after mixing, the complexation reactions are completed and the colored complexes exhibited absorption bands in the wavelength range 300-500 nm. Spectral data was reduced using principal component analysis and subjected to artificial neural networks. The data obtained from synthetic mixtures of four metal ions were processed by principal component-feed forward neural networks (PCFFNNs) and principal component-radial basis function networks (PCRBFNs). Performances of the proposed methods were tested with regard to root mean square errors of prediction (RMSEP%), using synthetic solutions. Under the working conditions, the proposed methods were successfully applied to simultaneous determination of Co²⁺, Ni²⁺, Cu²⁺ and Zn²⁺ in different vegetable, foodstuff and pharmaceutical product samples.     

Simultaneous determination of Al(III) and Fe(III) in post-hemodialysis fluids by spectrophotometry and multivariate calibration

A multivariate calibration model (PLS) was developed for the simultaneous spectrophotometric determination of Al(III) and Fe(III) in post-hemodialysis fluids with pyrocathecol violet (PCV) as chromogenic reagent. The analytes build stable complexes with PCV in presence of hexamine buffered medium at pH 6.1. The complexes show overlapped absorption bands in the spectral range of 220-800 nm so that absorptions of 580 wavelengths were necessary for the calibrations. Determinations of Al(III) and Fe(III) were done without masking agents. The best calibration model was obtained by using PLS-1 regression with three components after data mean centering. The spectrophotometric method applied to assay the analytes in real post-hemodialysis samples containing no desferrioxamine B presented good agreement with voltammetric measurements used as reference. Concentrations ranging from 0.20 to 0.60 mg L⁻¹ for Al(III) and for Fe(III) were determined in real samples. The multivariate detection limits for Al(III) and Fe(III) were 0.044 and 0.052 mg L⁻¹, respectively, and the calculated values of sensitivity were 6.33 for Al(III) and 3.44 for Fe(III). The proposed method showed to be straightforward and useful to follow the hemodialysis progress for patients under treatment. Interferents were also investigated.     

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

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

Simultaneous determination of methylene blue and new methylene blue by slab optical waveguide spectroscopy and artificial neural networks

A slab optical waveguide (SOWG) has been used for study of adsorption of both methylene blue (MB) and new methylene blue (NMB) in liquid-solid interface. Adsorption characteristics of MB and NMB on both bare SOWG and silanized SOWG by octadecyltrichlorosilane (ODS) were compared. The simultaneous determinations of both MB and NMB were explored by flow injection SOWG spectrophotometric analysis and artificial neural networks (ANNs) for the first time. Concentrations of MB and NMB were estimated simultaneously with the ANNs. Results obtained with SOWG were compared with those got by conventional UV-visible spectrophotometry.     

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。     

A multivariate approach for the simultaneous determination of losartan potassium and hydrochlorothiazide in a combined pharmaceutical tablet formulation

A convenient new method for the simultaneous determination of losartan potassium and hydrochlorothiazide, with minimum sample pretreatment, is described. The procedure, based on the multivariate analysis of spectral data in the 220−274 nm region by the partial least squares algorithm, is linear in the concentration range 1.06−5.70 mg L⁻¹ for hydrochlorothiazide and 4.0−22.2 mg L⁻¹ for losartan. It is simple, rapid and robust, allowing accurate and precise results, with drug recovery rates of 99.3 and 100.4% and relative standard deviations of 1.7 and 1.0% obtained for hydrochlorothiazide and losartan, respectively. The method was applied to the simultaneous determination of both analytes in tablets, and it provided good results which were in statistical agreement with those provided by independent HPLC analyses of the samples. The method has also been successfully applied for the construction of drug dissolution profiles of a commercial pharmaceutical preparation containing both analytes. Figure A UV-PLS method for the simultaneous determination of losartan potassium and hydrochlorothiazide in pharmaceutical tablet formulations has been developed and validated     

H nuclear magnetic resonance spectroscopy analysis for simultaneous determination of levodopa, carbidopa and methyldopa in human serum and pharmaceutical formulations

A method utilizing NMR spectroscopy has been developed to confirm the identity and quantity of levodopa, carbidopa and methyldopa in human serum and pharmaceutical preparations. The method is based on 500 MHz proton NMR spectra of individual catecholamine molecules. Qualitative and quantitative analyses are based on resonance characteristics of the functional groups present in their structures and the integral ratio of selected signals belonging to different compounds with respect to those of an internal standard, respectively. Experiments are performed to validate the quantitative NMR method, and the linearity and reproducibility of the proposed method are verified. The detection limit of the proposed method was estimated as 4.2, 1.7 and 1.6  μg ml⁻¹ for levodopa, carbidopa and methyldopa, respectively. The recovery studies performed on human serum samples ranged from about 82-96% with relative standard deviations of <4%. The method was also applied successfully to the determination of each active compound in real pharmaceutical samples, and compared with the results obtained by the reference methods. The method is rapid, precise, accurate, and suitable for routine analyses.     

Deconvolution of the dielectric spectra of microbial cell suspensions using multivariate calibration and artificial neural networks

Dielectric spectroscopy at radiofrequencies has been widely used for the on-line and real-time estimation of cellular biomass. However, the presence of substantial amounts of non-biomass insoluble solids, such as wheatgerm, may interfere with these measurements in certain industrial media. Dielectric spectroscopy was combined with artificial neural networks (ANNs) to provide an estimation of the cellular biomass present in suspensions of yeast that had been contaminated in some cases with much higher concentrations of wheatgerm, so as to deconvolute the dielectric properties of the mixtures. It was found that an ANN, trained by backpropagation on the dielectric spectra produced by suspensions of varying amounts of yeast and wheatgerm, was able successfully to predict both yeast and wheatgerm content from unseen mixture data. Multivariate statistical methods, such as partial least squares (PLS) and principal component regression (PCR), could also be used successfully to deconvolute such dielectric spectra. It is concluded that such methods provide a powerful adjunct to the conventional quantitative analyses of dielectric data.     

Simultaneous spectrophotometric determination of drugs in pharmaceutical preparations using multiple linear regression and partial least-squares regression, calibration and prediction methods

Two new methods for the simultaneous determination of acetylsalicylic acid, acetaminophen and caffeine based on total absorbance measurements and their processing by multiple linear regession and partial least-squares regression are proposed. The concentration ranges used to construct the calibration matrix were 4.0-12.0, 2.0-10.0 and 0.9-6.0 μg ml⁻¹ for acetylsalicylic acid, acetaminophen and caffeine respectively. The proposed methods were validated by using a set of synthetic sample mixtures and subsequently applied to the determination of the three active principles in three different pharmaceutical preparations.     

Sustained modelling ability of artificial neural networks in the analysis of two pharmaceuticals (dextropropoxyphene and dipyrone) present in unequal concentrations

An improvement is presented on the simultaneous determination of two active ingredients present in unequal concentrations in injections. The analysis was carried out with spectrophotometric data and non-linear multivariate calibration methods, in particular artificial neural networks (ANNs). The presence of non-linearities caused by the major analyte concentrations which deviate from Beer's law was confirmed by plotting actual vs. predicted concentrations, and observing curvatures in the residuals for the estimated concentrations with linear methods. Mixtures of dextropropoxyphene and dipyrone have been analysed by using linear and non-linear partial least-squares (PLS and NPLSs) and ANNs. Notwithstanding the high degree of spectral overlap and the occurrence of non-linearities, rapid and simultaneous analysis has been achieved, with reasonably good accuracy and precision. A commercial sample was analysed by using the present methodology, and the obtained results show reasonably good agreement with those obtained by using high-performance liquid chromatography (HPLC) and a UV-spectrophotometric comparative methods.