Comparative Spectrophotometric,Spectrofluorometric, and High‐performance Liquid Chromatographic Study for the Quantitative Determination of the Binary Mixture Felodipine and Ramipril in Pharmaceutical Formulations

Abstract

Two‐component mixtures of felodipine (FLD) and ramipril (RMP) were assayed by derivative UV spectrophotometry, spectrofluorometry, and high performance liquid chromatography (HPLC). The spectrophotometric methods included a zero‐crossing first‐ and second‐order derivative procedure and a derivative compensation technique for the determination of binary mixtures with overlapping spectra. The spectrofluorometric method was based on first‐ and second‐order derivatives of the emission spectra (zero‐crossing point). Results from these methods were compared with those obtained by an exclusively developed isocratic reversed phase HPLC method. A reversed‐phase Adsorbosil DS analytical column, with methanol‐acetonitrile‐water (50∶30∶20, v/v) mobile phase at a flow rate of 1.5 ml/min, was used with a UV detector. The temperature was set at 25±0.2°C. Results obtained by the spectrophotometric and spectrofluorometric methods were comparable to those obtained by the HPLC method, as far as analysis of variance (ANOVA) test results were concerned. It is concluded that the developed methods are equally accurate, sensitive, and precise; with direct and simple application to pharmaceutical formulations of felodipine and ramipril combination, without interference from common pharmaceutical adjuvants.     

A Simple and Highly Sensitive Method for Quantitative Detection of Methyl Paraben and Phenol in Cosmetics Using Derivative Spectrophotometry and Multivariate Chemometric Techniques

This paper presents a simple and sensitive method for the simultaneous determination of methyl paraben (MP ) and phenol (PO ) based on the application of successive projections algorithm (SPA ) to the first derivative spectra (200–350 nm). SPA is used for variables selection in order to obtain multiple linear regression (MLR ) models using a small subset of wavelengths. The starting vector and the number of variables are optimized and the best variables are selected according to the sequence of projection operations on the spectral data matrix of the calibration set. Principal component regression and partial least squares models are also developed for comparison. The best models are found to be SPA‐MLR using seven wavelengths from the first‐derivative spectra with a root‐mean‐square error of prediction (RMSEP) of 0.08 for MP and eight wavelengths with RMSEP of 0.31 for the determination of PO . The accuracy of the proposed method is confirmed by spiked recovery test on cosmetic samples with satisfactory results (86–110%). Analysis results of the cosmetic samples are also statistically compared with those obtained from the HPLC method, showing no significant difference regarding accuracy and precision. The results indicate the potential of SPA‐MLR and derivative spectrophotometry for rapid and sensitive analysis of cosmetic samples.     

Spectrophotometric Methods for the Determination of Acediasulfone in Mixture with Cinchocaine

Abstract

Derivative spectrophotometry techniques (ratio‐spectra first derivative and zero‐crossing first derivative) were described for simultaneous determination of acediasulfone and cinchocaine. Acediasulfone was also determined via the formation of a colored product as a result of its reaction with p‐dimethylaminobenzaldehyde. In the ratio‐spectra first derivative method, the measurements were taken at 310 and 233.9 nm for acediasulfone and cinchocaine, respectively. By the zero‐crossing first derivative method, lines of regression were taken at 318 and 233 nm for acediasulfone and cinchocaine, respectively. In the colorimetric method, absorbance measurements were obtained at 452 nm. Acediasulfone showed linearity over concentration ranges 2–14 µg/ml, 2–16 µg/ml, and 12–60 µg/ml for ratio‐spectra first derivative, zero‐crossing first derivative, and colorimetric methods, whereas cinchocaine showed linearity over concentration ranges 1–10 µg/ml and 2.28–16 µg/ml for ratio‐spectra first derivative and zero‐crossing first derivative techniques. The proposed methods proved to be specific and accurate for the analysis of the cited drugs in laboratory‐prepared mixtures and dosage form. The obtained results agree statistically with those obtained by reference methods.     

Spectrophotometric and spectrodensitometric determination of paracetamol and drotaverine HCl in combination

Thin-layer chromatography, first derivative, ratio spectra derivative spectrophotometry and Vierordt's method have been developed for the simultaneous determination of paracetamol and drotaverine HCl. TLC densitometric method depends on the difference in Rf values using ethyl acetate:methanol:ammonia (100:1:5 v/v/v) as a mobile phase. The spots of the two drugs were scanned at 249 and 308 nm over concentration ranges of 60-1200 microg/ml and 20-400 microg/ml with mean percentage recovery 100.11%+/-1.91 and 100.15%+/-1.87, respectively. The first derivative spectrophotometric method deals with the measurements at zero-crossing points 259 and 325 nm with mean percentage recovery 99.25%+/-1.08 and 99.45%+/-1.14, respectively. The ratio spectra first derivative technique was used at 246 and 305 nm with mean percentage recovery 99.75%+/-1.93 and 99.08%+/-1.22, respectively. Beer's law for first derivative and ratio spectra derivative methods was obeyed in the concentration range 0.8-12.8 and 0.4-6.4 microg/ml of paracetamol and drotaverine HCl, respectively. Vierordt's method was applied to over come the overlapping of paracetamol and drotaverine HCl in zero-order spectra in concentration range 2-26 and 2-40 microg/ml respectively. The suggested methods were successfully applied for the analysis of the two drugs in laboratory prepared mixtures and their pharmaceutical formulation. The validity of the methods was assessed by applying the standard addition technique. The obtained results were statistically agreed with those obtained by the reported method.     

Application of derivative, derivative ratio, and multivariate spectral analysis and thin-layer chomatography-densitometry for determination of a ternary mixture containing drotaverine hydrochloride, caffeine, and paracetamol

New selective, precise, and accurate methods are described for the determination of a ternary mixture containing drotaverine hydrochloride (I), caffeine (II), and paracetamol (III). The first method uses the first (D1) and third (D3) derivative spectrophotometry at 331 and 315 nm for the determination of (I) and (III), respectively, without interference from (II). The second method depends on the simultaneous use of the first derivative of the ratio spectra (DD1) with measurement at 312.4 nm for determination of (I) using the spectrum of 40 microg/mL (III) as a divisor or measurement at 286.4 and 304 nm after using the spectrum of 4 microg/mL (I) as a divisor for the determination of (II) and (III), respectively. In the third method, the predictive abilities of the classical least-squares, principal component regression, and partial least-squares were examined for the simultaneous determination of the ternary mixture. The last method depends on thin-layer chromatography-densitometry after separation of the mixture on silica gel plates using ethyl acetate-chloroform-methanol (16 + 3 + 1, v/v/v) as the mobile phase. The spots were scanned at 281, 272, and 248 nm for the determination of (I), (II), and (III), respectively. Regression analysis showed good correlation in the selected ranges with excellent percentage recoveries. The chemical variables affecting the analytical performance of the methodology were studied and optimized. The methods showed no significant interferences from excipients. Intraday and interday assay precision and accuracy values were within regulatory limits. The suggested procedures were checked using laboratory-prepared mixtures and were successfully applied for the analysis of their pharmaceutical preparations. The validity of the proposed methods was further assessed by applying a standard addition technique. The results obtained by applying the proposed methods were statistically analyzed and compared with those obtained by the manufacturer's method.     

A comparative study on various spectrometries with thin layer chromatography for simultaneous analysis of drotaverine and nifuroxazide in capsules

Three spectrophotometric methods including Vierordt's method, derivative, ratio spectra derivative, and thin layer chromatography (TLC)-UV densitometric method were developed for simultaneous determination of drotaverine HCl (DRT) and nifuroxazide (NIF) in presence of its impurity, 4-hydroxybenzohydrazide (4-HBH). In Vierordt's method, (E(1 cm)(1%)) values were calculated at 227 and 368 nm in the zero-order spectra of DRT and NIF. By derivative spectrophotometry, the zero-crossing method, drotaverine HCl was determined using the second derivative at 245 nm and the third derivative at 238 nm, while nifuroxazide was determined using the first derivative at 399 nm and the second derivative at 411 nm. The ratio spectra derivative spectrophotometry is basedon the measure of the amplitude at 459 nm for DRT and at 416 nm for NIF in the first derivative of the ratio spectra. Calibration graphs of the three spectrophotometric methods were plotted in the range 1-10 mug/ml of DRT and 2-20 mug/ml of NIF. TLC-UV densitometric method was achieved on silica gel plates using ethyl acetate : methanol : ammonia 33% (10 : 1 : 0.1 v/v/v) as the mobile phase. The Rf values were 0.74, 0.50, 0.30+/-0.01 for DRT, NIF and 4-HBH, respectively. On the fluorescent plates, the spots were located by fluorescence quenching and the densitometrical area were measured at 308 and 287 nm with linear range 0.2-4 mug/spot and 0.6-12 mug/spot for DRT and NIF, respectively. The proposed methods have been successfully applied to the commercial pharmaceutical formulation without any interference of excipients. Mean recoveries, relative standard deviations and the results of the proposed methods were compared with those obtained by applying the alternate methods.     

Quality assessment of gasoline using comprehensive two‐dimensional gas chromatography combined with unfolded partial least squares: A reliable approach for the detection of gasoline adulteration

Comprehensive two‐dimensional gas chromatography and flame ionization detection combined with unfolded‐partial least squares is proposed as a simple, fast and reliable method to assess the quality of gasoline and to detect its potential adulterants. The data for the calibration set are first baseline corrected using a two‐dimensional asymmetric least squares algorithm. The number of significant partial least squares components to build the model is determined using the minimum value of root‐mean square error of leave‐one out cross validation, which was 4. In this regard, blends of gasoline with kerosene, white spirit and paint thinner as frequently used adulterants are used to make calibration samples. Appropriate statistical parameters of regression coefficient of 0.996–0.998, root‐mean square error of prediction of 0.005–0.010 and relative error of prediction of 1.54–3.82% for the calibration set show the reliability of the developed method. In addition, the developed method is externally validated with three samples in validation set (with a relative error of prediction below 10.0%). Finally, to test the applicability of the proposed strategy for the analysis of real samples, five real gasoline samples collected from gas stations are used for this purpose and the gasoline proportions were in range of 70–85%. Also, the relative standard deviations were below 8.5% for different samples in the prediction set.     

取代基物化参数及其在药物定量构效关系中的应用

把取代基电性、立体及疏水性物化参数组合建立一种新取代基描述方法, 对环尿素类和N,N-二甲基-2-溴苯乙胺类衍生物进行结构表征. 对训练样本集通过逐步回归筛选变量, 所建多元线性回归方程R2分别为0.853和0.960, 留一法交互检验Rcv2分别为0.723和0.901；用预测集样本作外部预测, 所得Qext2分别为0.7617和0.7653. 结果显示：环尿素类化合物结构中苯环邻位立体、间位疏水、对位疏水及立体因素对该类药物抗HIV活性产生阻抑作用； N,N-二甲基-2-溴苯乙胺类苯环上取代基立体因素及对位给电子效应有利于提高肾上腺素能阻断活性.     

Computational performance and cross‐validation error precision of five PLS algorithms using designed and real data sets

An evaluation of computational performance and precision regarding the cross‐validation error of five partial least squares (PLS) algorithms (NIPALS, modified NIPALS, Kernel, SIMPLS and bidiagonal PLS), available and widely used in the literature, is presented. When dealing with large data sets, computational time is an important issue, mainly in cross‐validation and variable selection. In the present paper, the PLS algorithms are compared in terms of the run time and the relative error in the precision obtained when performing leave‐one‐out cross‐validation using simulated and real data sets. The simulated data sets were investigated through factorial and Latin square experimental designs. The evaluations were based on the number of rows, the number of columns and the number of latent variables. With respect to their performance, the results for both simulated and real data sets have shown that the differences in run time are statistically different. PLS bidiagonal is the fastest algorithm, followed by Kernel and SIMPLS. Regarding cross‐validation error, all algorithms showed similar results. However, in some situations as, for example, when many latent variables were in question, discrepancies were observed, especially with respect to SIMPLS. Copyright © 2010 John Wiley & Sons, Ltd.     

Chemometrics in fuel science: demonstration of the feasibility of chemometrics analyses applied to physicochemical parameters to screen solvent tracers in Brazilian commercial gasoline

Samples of commercial gasoline, from the National Program of Fuel Quality Monitoring of the National Petroleum Agency, were collected from gas stations located in the Midwestern state of Sao Paulo, Brazil, and analyzed by several physicochemical standard methods established by ANP Resolution no. 309. Also, important information related to tampering was analyzed with the marker solvent. Statistical analysis and exploratory chemometric were employed to discriminate the presence of markers of solvents in commercial gasoline. The results showed that statistical and chemometric parameters such as atmospheric distillation temperatures T10 and T90, RON, benzene and saturated and aromatic hydrocarbons satisfactorily describe the presence of marker solvent, usually with a probability exceeding 70%. Furthermore, after optimizing the SIMCA algorithm, sensitivity in the training set with cross‐validation leave‐one‐out (83.8%) and the set of prediction (77.1%) were revealed. The proposed method will become indispensable and recommended for discriminating samples of fuels for commercial applications in routine monitoring programs and quality control. Copyright © 2011 John Wiley & Sons, Ltd.     

A new strategy to improve the predictive ability of the local lazy regression and its application to the QSAR study of melanin‐concentrating hormone receptor 1 antagonists

In the quantitative structure‐activity relationship (QSAR) study, local lazy regression (LLR) can predict the activity of a query molecule by using the information of its local neighborhood without need to produce QSAR models a priori. When a prediction is required for a query compound, a set of local models including different number of nearest neighbors are identified. The leave‐one‐out cross‐validation (LOO‐CV) procedure is usually used to assess the prediction ability of each model, and the model giving the lowest LOO‐CV error or highest LOO‐CV correlation coefficient is chosen as the best model. However, it has been proved that the good statistical value from LOO cross‐validation appears to be the necessary, but not the sufficient condition for the model to have a high predictive power. In this work, a new strategy is proposed to improve the predictive ability of LLR models and to access the accuracy of a query prediction. The bandwidth of k neighbor value for LLR is optimized by considering the predictive ability of local models using an external validation set. This approach was applied to the QSAR study of a series of thienopyrimidinone antagonists of melanin‐concentrating hormone receptor 1. The obtained results from the new strategy shows evident improvement compared with the commonly used LOO‐CV LLR methods and the traditional global linear model. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010     

Wood identification using pressure DSC data

This work addresses the problem of supervised classification of industrial wood species (seven different types in the present study) through their thermo‐oxidative stability. This is evaluated by pressure differential scanning calorimetry (PDSC) using the ASTM E2009. The maximization of the ratio of correct classification and the reduction of the costs of this activity are intended. This supervised classification problem was carried out using two different proposals: applying novel nonparametric functional data analysis techniques, based on kernel estimation, to the original PDSC curves, and using machine learning classification approaches applied to different multivariate data sets. The multivariate data sets were obtained, on the one hand, by estimating the fractal (Hausdorff) dimension of the PDSC curves by several methods, jointly with selecting the parameters from fitting a nonlinear model to the PDSC curves and, on the other hand, applying principal component analysis or partial linear squares to the thermograms. The results obtained show that the PDSC curves can be used to discriminate wood samples when these innovative and traditional statistical techniques are applied. In the best of the cases, a probability of correct classification that equals to 0.92 was obtained. PDSC represents a new alternative to the use of images, spectra, and other thermal signals as thermogravimetric analysis for classification purposes.Copyright © 2013 John Wiley & Sons, Ltd.     

Rapid and Nondestructive Analysis of Bacillus Calmette–Guerin Polysaccharide Nucleic Acid Injection by near-Infrared Spectroscopy with Chemometrics

A novel quantitative analytical method using near-infrared (NIR) spectroscopy combined with chemometrics has been developed to determine the polysaccharides and nucleic acids for routine quality analysis of Bacillus Calmette–Guerin polysaccharide and nucleic acid injections. A Monte-Carlo method was used to detect and discard outliers and to improve the predictive ability of the model. Various other spectral preprocessing methods such as smoothing, derivative, multiplicative scattering correction, standard normal variables, and orthogonal signal correction methods were used to remove noise and other irrelevant information from the spectra. Sample-set partitioning based on joint x–y distance method was utilized to divide the sample measurements into calibration and validation datasets. The optimal wavelength variables were determined by competitive adaptive weighted sampling. The model was established and cross-validated using partial least square regression. The root mean square errors of cross-validation for polysaccharides and nucleic acids were determined to be 0.0382 and 5.218, and the root mean square errors of prediction were 0.0229 and 6.282. The overall results show that NIR spectroscopy combined with chemometry is effective for the quantitative analysis of Bacillus Calmette–Guerin polysaccharide and nucleic acid injections.     

Comparative molecular field analysis and comparative molecular similarity index analysis studies on 1H NMR chemical shift of NH group of diaryl triazene derivatives

Comparative molecular field analysis (CoMFA), comparative molecular field analysis region focusing (CoMFA‐RF) for optimizing the region for the final partial least square analysis, and comparative molecular similarity indices analysis (CoMSIA) methods were employed to develop three‐dimensional quantitative structure–activity relationship (3D‐QSAR) models of ¹H NMR chemical shift of NH proton of diaryl triazene derivatives. The best orientation was searched by all‐orientation search (AOS) strategy to minimize the effect of the initial orientation of the structures. The predictive abilities of CoMFA‐RF and CoMSIA models were determined using a test set of ten compounds affording predictive correlation coefficients of 0.721 and 0.754, respectively, indicating good predictive power. For further model validation, cross validation (leave one out), progressive scrambling, and bootstrapping were also applied. The accuracy and speed of obtained 3D‐QSAR models for the prediction of ¹H NMR chemical shifts of NH group of diaryl triazene derivatives were greater compared to some computational well‐known procedures. Copyright © 2013 John Wiley & Sons, Ltd.     

Rational experimental design for bioanalytical methods validation illustration using an assay method for total captopril in plasma

Generally, bioanalytical chromatographic methods are validated according to a predefined programme and distinguish a pre-validation phase, a main validation phase and a follow-up validation phase. In this paper, a rational, total performance evaluation programme for chromatographic methods is presented. The design was developed in particular for the pre-validation and main validation phases. The entire experimental design can be performed within six analytical runs. The first run (pre-validation phase) is used to assess the validity of the expected concentration-response relationship (lack of fit, goodness of fit), to assess the specificity of the method and to assess the stability of processed samples in the autosampler for 30 h (benchtop stability). The latter experiment is performed to justify overnight analyses. Following approval of the method after the pre-validation phase, the next five runs (main validation phase) are performed to evaluate method precision and accuracy, recovery, freezing and thawing stability and over-curve control /dilution. The design is nested, i.e., many experimental results are used for the evaluation of several performance characteristics. Analysis of variance (ANOVA) is used for the evaluation of lack of fit and goodness of fit, precision and accuracy, freezing and thawing stability and over-curve control/ dilution. Regression analysis is used to evaluate benchtop stability. For over-curve control/ dilution, additional to ANOVA, also a paired comparison is applied. As a consequence, the recommended design combines the performance of as few independent validation experiments as possible with modern statistical methods, resulting in optimum use of information. A demonstration of the entire validation programme is given for an HPLC method for the determination of total captopril in human plasma.     

Data exploration on standard asphalt mix analyses

The purpose of our study was the evaluation of the most important factors that affect the volumetric and conventional mechanical properties of produced asphalt mix and the volumetric properties of built‐in asphalt layer. Asphalt mix design follows the standard procedure (Marshall procedure). We were interested not only in the quantity of bitumen specified by the Marshall procedure, but also in the quantity of stone aggregate fractions, temperatures of production and properties of bitumen that is used. The influence of these factors was investigated with several models. For the building of models we used 444 asphalt samples, analysed by one laboratory. To select the most important factors, several multiple linear regression (MLR) models, partial least squares (PLS) regression models and counterpropagation neural network models were made. Obtained models were tested with leave‐one‐out (LOO) and leave‐10%‐out cross‐validation procedures. The results of MLR and PLS models show that the independent variables are closely related. Among 21 variables there is only one found as less important. MLR and PLS models show better predictive ability than counterpropagation neural network models. The best MLR models will be employed for the preparation of the asphalt mix design (recipe) with some unknown material. Copyright © 2009 John Wiley & Sons, Ltd.