Chemometric determination of naproxen sodium and pseudoephedrine hydrochloride in tablets by HPLC

A new chemometric determination by high-performance liquid chromatography (HPLC) with photodiode array (PDA) detection was implemented for the simultaneous determination of naproxen sodium and pseudoephedrine hydrochloride in tablets. Three chemometric calibration techniques, classical least squares (CLS), principle component regression (PCR) and partial least squares (PLS) were applied to the peak area at multiwavelength PDA detector responses. The combinations of HPLC with chemometric calibration techniques were called HPLC-CLS, HPLC-PCR and HPLC-PLS. For comparison purposes the HPLC method called the classic HPLC method was used to confirm the results obtained from combined HPLC-chemometric calibration techniques. A good chromatographic separation between two drugs with losartan potassium as an internal standard was achieved using a Waters Symmetry C18 Column 5 microm 4.6+/-250 mm and a mobile phase containing 0.2 M acetate buffer and acetonitrile (v/v, 40:60). The multiwavelength PDA detection was measured at five different wavelengths. The chromatograms were recorded as a training set in the mobile phase. Three HPLC-chemometric calibrations and the classic-HPLC method were used to test the synthetic mixtures of naproxen sodium and pseudoephedrine hydrochloride in the presence of the internal standard. The HPLC-chemometric approaches were applied to real samples containing drugs of interest. The experimental results obtained from HPLC-chemometric calibrations were compared with those obtained by a classic HPLC method.     

New liquid chromatographic-chemometric approach for the determination of sunset yellow and tartrazine in commercial preparation

A new liquid chromatographic (LC)-chemometric approach was developed for the determination of sunset yellow (SUN) and tartrazine (TAR) in commercial preparations. This approach uses LC and chemometric calibration methods, i.e., classical least-squares (CLS), principal component regression (PCR), and partial-least squares (PLS), simultaneously. The combined LC-chemometric approaches, denoted as LC-CLS, LC-PCR, and LC-PLS, are based on photodiode array (PDA) detection at multiple wavelengths. Optimum chromatographic separation of SUN and TAR with allura red as the internal standard (IS) was obtained by using a Waters Symmetry C18 column, 5 microm, 4.6 x 250 mm, and 0.2 M acetate buffer (pH 5)-acetonitrile-methano-bidistilled water (55 + 20 + 15 + 10, v/v) as the mobile phase at a flow rate of 1.9 mL/min. The LC data sets consisting of the ratios of analyte peak areas to the IS peak area were obtained by using PDA detection at 5 wavelengths (465, 470, 475, 480, and 485 nm). LC-chemometric calibrations for SUN and TAR were separately constructed by using the relationship between the peak-area ratio and the training sets for each colorant. LC-chemometric approaches were tested for different synthetic mixtures containing SUN and TAR in the presence of the IS. These LC-chemometric calibrations were applied to a commercial preparation of the 2 colorants. The experimental results of the LC-chemometric approaches were compared with those obtained by a developed classical LC method using single-wavelength detection.     

Simultaneous quantitative resolution of atorvastatin calcium and fenofibrate in pharmaceutical preparation by using derivative ratio spectrophotometry and chemometric calibrations.

In the present work, five different spectrophotometric techniques for simultaneous determination of formulations containing atorvastatin calcium (ATOR) and fenofibrate (FENO) in various combinations are described. In ratio spectra derivative spectrophotometry, analytical signals were measured at wavelengths corresponding to either maximums or minimums for both drugs in first derivative spectra of ratio spectra obtained by using either spectrum as divisor. For the remaining four methods using chemometric techniques, namely, classical least squares (CLS), inverse least squares (ILS), principal component regression (PCR) and partial least squares (PLS), the calibrations were constructed by using the absorption data matrix corresponding to the concentration data matrix, with measurements in the range of 231 - 310 nm (Deltalambda = 1 nm) in their zero-order spectra. The linearity range was found to be 4 - 22 and 2 - 20 microg/ml for ATOR and FENO, respectively. The validity of the proposed methods was successfully assessed for analyses of both drugs in laboratory-prepared mixtures and in commercial tablet formulations.     

Polarographic chemometric determination of zinc and nickel in aqueous samples

Polarographic chemometric methods were applied to the determination of zinc and nickel in aqueous solutions previously acidified with 0.1 M acetate buffer (pH 4.2). The studied methods are multivariate methods including classical least squares (CLS), principal component regression (PCR) and partial least squares (PLS); derivative ratio methods (first, ¹D and second, ²D derivative ratio). A comparative study was considered. The studied chemometric methods do not need the presence of any reduction potential shift reagent in spite of the great overlap between the two metals polarograms. A training set consisting of 10 binary mixture solutions in the possible combinations containing 0.13–9.30 μg/ml Zn(II) and 0.20–12.25 μg/ml Ni(II) was used to develop the chemometric calibrations (CLS, PCR and PLS). A validation set containing the synthetic mixtures in the range of 0.29–9.00 μg/ml for Zn(II) and 0.30–11.60 μg/ml for Ni(II) was used to validate the multivariate calibrations. Same mixtures were used to develop the derivative ratio methods. The polarograms were recorded and their current values were measured within the potential range −920 to −1052 mV at 2 mV intervals. The mean percentage recoveries obtained using CLS, PCR and PLS were found to be 99.5 ± 1.5%, 100.0 ± 1.1% and 100.0 ± 1.0% for Zn(II) and 99.4 ± 1.3%, 99.7 ± 1.2% and 99.9 ± 1.0% for Ni(II), respectively. The mean percentage recoveries obtained using ¹D at −950 mV, ¹D at −1010 mV, ¹D at −950 mV–¹D at −1010 mV and ²D at −986 mV for Zn(II) were found to be 99.7 ± 1.2%, 99.2 ± 1.6%, 99.4 ± 1.4% and 99.4 ± 1.4%; and using ¹D at −1030 mV and ²D at −1010 mV for Ni(II) were found to be 100.5 ± 1.3% and 100.4 ± 1.3%, respectively. Interferences due to the presence of Cd, Co, Pb, Fe, Mn, Ca, Mg, Cu and Al were studied. The applicability of the proposed methods was assessed through the determination of both metals in tap drinking-water. Samples were subjected if required up to a 20-fold preconcentration step by microwaving in pyrex vessels. The results were compared with those obtained using the zincon and the heptoxime colorimetric reference methods for the determination of zinc and nickel, respectively.     

Multicomponent kinetic spectrophotometric determination of pefloxacin and norfloxacin in pharmaceutical preparations and human plasma samples with the aid of chemometrics

A spectrophotometric method for the simultaneous determination of the important pharmaceuticals, pefloxacin and its structurally similar metabolite, norfloxacin, is described for the first time. The analysis is based on the monitoring of a kinetic spectrophotometric reaction of the two analytes with potassium permanganate as the oxidant. The measurement of the reaction process followed the absorbance decrease of potassium permanganate at 526nm, and the accompanying increase of the product, potassium manganate, at 608nm. It was essential to use multivariate calibrations to overcome severe spectral overlaps and similarities in reaction kinetics. Calibration curves for the individual analytes showed linear relationships over the concentration ranges of 1.0-11.5mgL(-1) at 526 and 608nm for pefloxacin, and 0.15-1.8mgL(-1) at 526 and 608nm for norfloxacin. Various multivariate calibration models were applied, at the two analytical wavelengths, for the simultaneous prediction of the two analytes including classical least squares (CLS), principal component regression (PCR), partial least squares (PLS), radial basis function-artificial neural network (RBF-ANN) and principal component-radial basis function-artificial neural network (PC-RBF-ANN). PLS and PC-RBF-ANN calibrations with the data collected at 526nm, were the preferred methods-%RPE(T) approximately 5, and LODs for pefloxacin and norfloxacin of 0.36 and 0.06mgL(-1), respectively. Then, the proposed method was applied successfully for the simultaneous determination of pefloxacin and norfloxacin present in pharmaceutical and human plasma samples. The results compared well with those from the alternative analysis by HPLC.     

Comparison of three multivariate calibration methods as an approach to arsenic speciation by HG-AAS

This paper compares three multivariate calibration methods, namely classical least squares (CLS), inverse least squares (ILS) and Kaiman filter, applied to continuous-flow hydride generation with sodium tetrahydroborate(III) reducing agent and AAS detection for the purposes of speciation of As(III), As(V), monomethylarsonic acid (MMA), and dimethylarsinic acid (DMA). The precision of the three multivariate methods was compared in the calibration and prediction steps by standard error of prediction (SEP) and relative error of prediction (REP), respectively, for each analyte and no significant differences have been found for As(III), As(V), MMA, and DMA when the F-test was applied to compare the three multivariate calibration methods in pairs at the 95% confidence level. Determination of the As species was carried out in spiked drinking and sea water in the range 7–35 g 1^–1. Recoveries were in all cases around 100% and the detection limit for the least sensitive species was close to 5 g 1^–1.     

Solving matrix effect,spectral overlapping and nonlinearity by generalized standard addition method coupled with radial basis functions–partial least squares: simultaneous determination of atorvastatin and amlodipine in urine

For simultaneous determination in conditions with spectral overlap and variation of matrix effects, coupling of the generalized standard addition method (GSAM) with the multivariate nonlinear method of radial basis function–partial least squares (RBF–PLS) was proposed. The nonlinearity caused by the GSAM used to correct matrix effects was studied, and principal component analysis was proposed for identifying it. In the method introduced, the whole sensor range can be used without the collinearity problem encountered in the application of GSAM with classical least squares (CLS), and calibration can be made for each analyte, separately. The introduced method was applied to determine amlodipine and atorvastatin in urine samples. The mean of the percent recoveries was between 95 and 101.12. The percent relative standard deviation values of the method were in most cases below 5%. The results of GSAM–RBF–PLS were compared with those obtained by GSAM–CLS and GSAM–PLS. Copyright © 2013 John Wiley & Sons, Ltd.     

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     

Simultaneous determination of nifuroxazide and drotaverine hydrochloride in pharmaceutical preparations by bivariate and multivariate spectral analysis

The quantitative predictive abilities of the new and simple bivariate spectrophotometric method are compared with the results obtained by the use of multivariate calibration methods [the classical least squares (CLS), principle component regression (PCR) and partial least squares (PLS)], using the information contained in the absorption spectra of the appropriate solutions. Mixtures of the two drugs Nifuroxazide (NIF) and Drotaverine hydrochloride (DRO) were resolved by application of the bivariate method. The different chemometric approaches were applied also with previous optimization of the calibration matrix, as they are useful in simultaneous inclusion of many spectral wavelengths. The results found by application of the bivariate, CLS, PCR and PLS methods for the simultaneous determinations of mixtures of both components containing 2-12microgml(-1) of NIF and 2-8microgml(-1) of DRO are reported. Both approaches were satisfactorily applied to the simultaneous determination of NIF and DRO in pure form and in pharmaceutical formulation. The results were in accordance with those given by the EVA Pharma reference spectrophotometric method.     

Simultaneous Polarographic Determination of Lead (II) and TIN (II) by Multivariate Calibration

ABSTRACT

The polarographic waves of lead (II) and tin (II) overlap due to their similar reductive potentials and it is difficult to determine these two components simultaneously without a pre-separation. In this paper, differential pulse polarography (DPP) combined with multivariate calibration approaches, such as classical least squares (CLS), principal component regression (PCR) and partial least squares (PLS), were successfully applied to the resolution of overlapping polarographic waves of these two components in the concentration range of 0.05-3.50 mg 1^?1. Satisfactory quantitative results were obtained.     

Simultaneous Anodic Stripping Voltammetric Determination of Pb and Cd,Using a Vibrating Gold Microwire Electrode,Assisted by Chemometric Techniques

Simultaneous anodic stripping voltammetric determination of Pb and Cd is restricted on gold electrodes as a result of the overlapping of these two peaks. This work describes the quantitative determination of a binary mixture system of Pb and Cd, at low concentration levels (up to 15.0 and 10.0 µg L^?1 for Pb and Cd, respectively) by differential pulse anodic stripping voltammetry (DPASV; deposition time of 30 s), using a green electrode (vibrating gold microwire electrode) without purging in a chloride medium (0.5 M NaCl) under moderate acidic conditions (HCl 1.0 mM), assisted by chemometric tools. The application of multivariate curve resolution alternating least squares (MCR‐ALS) for the resolution and quantification of both metals is shown. The optimized MCR‐ALS models showed good prediction ability with concentration prediction errors of 12.4 and 11.4 % for Pb and Cd, respectively. The quantitative results obtained by MCR‐ALS were compared to those obtained with partial least squares (PLS) and classical least squares (CLS) regression methods. For both metals, PLS and MCR‐ALS results are comparable and superior to CLS. For Cd, as a result of the peak shift problem, the application of CLS was unsuitable. MCR‐ALS provides additional advantage compared to PLS since it estimates the pure response of the analytes signal. Finally, the built up multivariate calibration models, based either in MCR‐ALS or PLS regression, allowed to quantify concentrations of Pb and Cd in surface river water samples, with satisfactory results.     

Improving the performance of hollow waveguide-based infrared gas sensors via tailored chemometrics

The use of chemometrics in order to improve the molecular selectivity of infrared (IR) spectra has been evaluated using classic least squares (CLS), partial least squares (PLS), science-based calibration (SBC), and multivariate curve resolution-alternate least squares (MCR-ALS) techniques for improving the discriminatory and quantitative performance of infrared hollow waveguide gas sensors. Spectra of mixtures of isobutylene, methane, carbon dioxide, butane, and cyclopropane were recorded, analyzed, and validated for optimizing the prediction of associated concentrations. PLS, CLS, and SBC provided equivalent results in the absence of interferences. After addition of the spectral characteristics of water by humidifying the sample mixtures, CLS and SBC results were similar to those obtained by PLS only if the water spectrum was included in the calibration model. In the presence of an unknown interferant, CLS revealed errors up to six times higher than those obtained by PLS. However, SBC provided similar results compared to PLS by adding a measured noise matrix to the model. Using MCR-ALS provided an excellent estimation of the spectra of the unknown interference. Furthermore, this method also provided a qualitative and quantitative estimation of the components of an unknown set of samples. In summary, using the most suitable chemometrics approach could improve the selectivity and quality of the calibration model derived for a sensor system, and may avoid the need to analyze expensive calibration data sets. The results obtained in the present study demonstrated that (1) if all sample components of the system are known, CLS provides a sufficiently accurate solution; (2) the selection between PLS and SBC methods depends on whether it is easier to measure a calibration data set or a noise matrix; and (3) MCR-ALS appears to be the most suitable method for detecting interferences within a sample. However, the latter approach requires the most extensive calculations and may thus result in limited temporal resolution, if the concentration of a component should be continuously monitored.     

Nondestructive determination of compound amoxicillin powder by NIR spectroscopy with the aid of chemometrics

Near-infrared (NIR) spectroscopy, in combination with chemometrics, enables nondestructive analysis of solid samples without time-consuming sample preparation methods. A new method for the nondestructive determination of compound amoxicillin powder drug via NIR spectroscopy combined with an improved neural network model based on principal component analysis (PCA) and radial basis function (RBF) neural networks is investigated. The PCA technique is applied to extraction relevant features from lots of spectra data in order to reduce the input variables of the RBF neural networks. Various optimum principal component analysis-radial basis function (PCA-RBF) network models based on conventional spectra and preprocessing spectra (standard normal variate (SNV) and multiplicative scatter correction (MSC)) have been established and compared. Principal component regression (PCR) and partial least squares (PLS) multivariate calibrations are also used, which are compared with PCA-RBF neural networks. Experiment results show that the proposed PCA-RBF method is more efficient than PCR and PLS multivariate calibrations. And the PCA-RBF approach with SNV preprocessing spectra is found to provide the best performance.     

Application of multivariate methods to the simultaneous Fourier Transform Infrared (FT-IR) spectrometric determination of stereo-isomers; quinine and quinidine

The two stereo-isomers; quinine and quinidine have been determined in their mixtures in the IR region using chemometric multivariate methods, principal component regression (PCR) and partial least squares (PLS). A training set of thirty synthetic binary mixture solutions in the possible combinations containing 0.0 - 4.0 and 4.0 - 0.0% w/v quinine and quinidine, respectively in chloroform was used to develop the multivariate calibrations. A validation set containing thirty synthetic binary mixtures of variable ratios in the range of 0.2 - 4.0 and 4.0 - 0.2% w/v for quinine and quinidine, respectively in chloroform was used to validate the developed calibrations. The results of analysis of the validation synthetic mixtures were found to be 100.5+/-0.44% (R.S.D.%=0.44) and 100.5+/-0.38% (R.S.D.%=0.38) for quinine and 100.1+/-0.67% (R.S.D.%=0.67) and 100.1+/-0.68% (R.S.D.%=0.68) for quinidine using PCR and PLS models, respectively.     

Classification and determination of total protein in milk powder using near infrared reflectance spectrometry and the successive projections algorithm for variable selection

This paper proposes a methodology for the classification and determination of total protein in milk powder using near infrared reflectance spectrometry (NIRS) and variable selection. Two brands of milk powder were acquired from three Brazilian cities (Natal-RN, Salvador-BA and Rio de Janeiro-RJ). The protein content of 38 samples was determined by the Kjeldahl method and NIRS analysis. Principal component regression (PCR) and partial least squares (PLS) multivariate calibrations were used to predict the total protein. Soft independent modeling of class analogy (SIMCA) was also used for full-spectrum classification, resulting in almost 100% classification accuracy, regardless of the significance level adopted for the F-test. Using this strategy, it was feasible to classify powder milk rapidly and nondestructively without the need for various analytical determinations. Concerning the multivariate calibration models, the results show that PCR, PLS and MLR-SPA models are good for predicting total protein in powder milk; the respective root mean square errors of prediction (RMSEP) were 0.28 (PCR), 0.25 (PLS), 0.11 wt% (MLR-SPA) with an average sample protein content of 8.1 wt%. The results obtained in this investigation suggest that the proposed methodology is a promising alternative for the determination of total protein in milk powder.     

Simulation of high- and low-resolution mass spectra for assessment of calibration methods

Calibrating mixtures of residual gases in quadrupole mass spectrometry (QMS) can be difficult since low m/z ratios of molecular ions and their fragments result in overlap of signals especially in the lower mass regions. This causes problems in univariate calibration methods and encourages use of full spectral multivariate methods. Experimental assessment of regression methods has limitations since experimental sources of error can only be minimised and not entirely eliminated. A method of simulating full spectra at low and high resolution to accurate masses is described and these are then used for a calibration study of some popular linear regression methods [classical least squares regression (CLS), partial least squares (PLS), principal component regression (PCR)].     

On-Line Multicomponent Determination of the Flux of Mixtures of Phenols Through a Liquid Membrane in Real Time

 The analysis of mixtures of four phenolic compounds in an on-line system using UV-visible measurements with a fibre optic probe is discussed in this work. The aim of this system is to provide accurate real time concentration profiles in order to monitor the transport of phenols across a solid supported liquid membrane in both the feed and stripping phases. Different calibration models are taking into account the pH of the solution, using experimental designs and the first derivative in combination with different multivariate approaches like multiple linear regression (MLR), inverted least squares (ILS) and partial least squares regression (PLS). The comparison of all these combinations is carried out by means of the predictive residual error sum of squares (PRESS) evaluated from an independent set of spectra. From this comparison it is concluded that a PLS model using first derivative spectra offers the most accurate and robust prediction in the permeation experiments. Additionally, the stability of the model and the figures of merit obtained are also discussed. Received July 22, 1999. Revision October 23, 2000.     

HPLC and chemometrics-assisted UV-spectroscopy methods for the simultaneous determination of ambroxol and doxycycline in capsule

High-performance liquid chromatography (HPLC) and multivariate spectrophotometric methods are described for the simultaneous determination of ambroxol hydrochloride (AM) and doxycycline (DX) in combined pharmaceutical capsules. The chromatographic separation was achieved on reversed-phase C(18) analytical column with a mobile phase consisting of a mixture of 20mM potassium dihydrogen phosphate, pH 6-acetonitrile in ratio of (1:1, v/v) and UV detection at 245 nm. Also, the resolution has been accomplished by using numerical spectrophotometric methods as classical least squares (CLS), principal component regression (PCR) and partial least squares (PLS-1) applied to the UV spectra of the mixture and graphical spectrophotometric method as first derivative of the ratio spectra ((1)DD) method. Analytical figures of merit (FOM), such as sensitivity, selectivity, analytical sensitivity, limit of quantitation and limit of detection were determined for CLS, PLS-1 and PCR methods. The proposed methods were validated and successfully applied for the analysis of pharmaceutical formulation and laboratory-prepared mixtures containing the two component combination.     

Standardization of SPE signals in multicomponent analysis of three benzimidazolic pesticides by spectrofluorimetry

Piecewise direct standardization (PDS) is applied to multivariate standardization of fluorescence signals using partial least squares (PLS) and principal component regression (PCR) as the calibration models. The multivariate standardization was used to transfer spectra obtained after a step of solid phase extraction (SPE) to spectra registered in pure solvent in the determination of carbendazim, fuberidazole and thiabendazole in water samples. The influential parameters, such as tolerance, window size and the number of samples of the standardization subset were optimized by means of the root mean squared error of prediction (RMSEP). Similar RMSEP values were obtained by PLS and PCR using the optimized influential parameters in the standardization. However, better predictions of the compounds were obtained in test set by the PLS model.