Simultaneous determination of phenobarbital and phenytoin in tablet preparations by multivariate spectrophotometric calibration

The use of multivariate spectrophotometric calibration for the simultaneous determination of the active components of antiepileptic tablets is presented. The resolution of binary mixtures of phenobarbital and phenytoin has been accomplished by using partial least squares (PLS-1) regression analysis. Although the components show an important degree of spectral overlap, they have been simultaneously determined with high accuracy, with no interference from tablet excipients. A comparison is presented with the related multivariate method of classical least squares (CLS) analysis, which is shown to yield less reliable results due to the severe spectral overlap presented by the studied compounds. A statistical measure for the spectral overlap is proposed.     

Chemometric assisted simultaneous spectrophotometric determination of four-component nasal solutions with a reduced number of calibration samples

The use of multivariate spectrophotometric calibration for the simultaneous determination of three active components and one excipient in nasal solutions is presented. The resolution of four-component mixtures of phenylephrine, diphenhydramine, naphazoline and methylparaben in a matrix of excipients has been accomplished by using partial least-squares (PLS-1) and a variant of the so-called hybrid linear analysis (HLA) named net analyte preprocessing (NAP). Notwithstanding the presence of a large number of components and their high degree of spectral overlap, they have been rapidly and simultaneously determined with high accuracy and precision, with no interference, and without resorting to extraction procedures using non-aqueous solvents. A simple and fast method for wavelength selection in the calibration step is used, based on the minimisation of the predicted error sum of squares (PRESS) calculated as a function of a moving spectral window. The use of calibration designs of reduced size has been attempted. Satisfactory results were obtained when the number of calibration samples was reduced from 25 (full central composite) to 17 (fractional central composite) using the net analyte-based NAP method.     

Multi-element analysis of iron ore pellets by Laser-induced Breakdown Spectroscopy and Principal Components Regression

Laser-induced Breakdown Spectroscopy (LIBS) in combination with Principal Components Regression (PCR) has been applied to determine the elemental composition of a series of run-of-mine (ROM) iron ore samples. The samples were presented for measurement both as compressed pellets and as loose chipped material. The present paper details the results of the measurements of the compressed pellets. Results from ore chips will be reported separately. LIBS spectral data was recorded in three separate spectral regions to measure major, minor and trace components of the iron ore sample pellets. Background stripping, normalization and spectral cleaning were applied to minimize the relative standard deviations of the LIBS data. PCR analysis was then applied to produce calibration models for iron, aluminum, silicon, manganese, potassium and phosphorous. These calibration models were then validated using independent LIBS measurements. Robust calibration models were determined for iron, aluminum, silicon and potassium, whilst the results for manganese were encouraging. Phosphorous, present at low levels in the ores measured, remained the most difficult element to determine accurately. The combination of LIBS and PCR shows potential for in-situ on-line determination of ore composition.     

Spectral resolution in multivariate optical computing

An examination of spectral resolution effects on multivariate optical computing (MOC) and the design of multivariate optical elements (MOEs) is presented. A solution of napthalene and pyrene in CCl(4) is used as a test mixture, with spectra recorded in the nominal 2-2.5 microm spectral region at resolutions varying from 1 to 128 cm(-1). Spectra were treated in absorption mode and in transmission mode at sample pathlengths of 1, 0.5, and 0.2 cm. Principal components regression is used to provide comparison to MOE models. Conventional model prediction errors using all methods are presented as well as results of applying low-resolution models to high-resolution validation sets. This latter calculation is aimed at understanding the limits of calibration transfer when a model is based on spectra acquired with marginal spectral resolution. A theory is developed describing calibration transfer in the case of linear spectroscopy, which is shown to be consistent with the results observed in absorption mode and to represent the case of the short-pathlength limit in transmission mode. A definition of the necessary spectral resolution as a function of apodization function is given for linear spectroscopy, and a brief discussion of how non-linearity affects the results is provided.     

Orthogonal signal correction, wavelet analysis, and multivariate calibration of complicated process fluorescence data

In this paper, multivariate calibration of complicated process fluorescence data is presented. Two data sets related to the production of white sugar are investigated. The first data set comprises 106 observations and 571 spectral variables, and the second data set 268 observations and 3997 spectral variables. In both applications, a single response, ash content, is modelled and predicted as a function of the spectral variables. Both data sets contain certain features making multivariate calibration efforts non-trivial. The objective is to show how principal component analysis (PCA) and partial least squares (PLS) regression can be used to overview the data sets and to establish predictively sound regression models. It is shown how a recently developed technique for signal filtering, orthogonal signal correction (OSC), can be applied in multivariate calibration to enhance predictive power. In addition, signal compression is tested on the larger data set using wavelet analysis. It is demonstrated that a compression down to 4% of the original matrix size — in the variable direction — is possible without loss of predictive power. It is concluded that the combination of OSC for pre-processing and wavelet analysis for compression of spectral data is promising for future use.     

Complementary use of partial least-squares and artificial neural networks for the non-linear spectrophotometric analysis of pharmaceutical samples

The complementary use of partial least-squares (PLS) multivariate calibration and artificial neural networks (ANNs) for the simultaneous spectrophotometric determination of three active components in a pharmaceutical formulation has been explored. The presence of non-linearities caused by chemical interactions was confirmed by a recently discussed methodology based on Mallows augmented partial residual plots. Ternary mixtures of chlorpheniramine, naphazoline and dexamethasone in a matrix of excipients have been resolved by using PLS for the two major analytes (chlorpheniramine and naphazoline) and ANNs for the minor one (dexamethasone). Notwithstanding the large number of constituents, their high degree of spectral overlap and the occurrence of non-linearities, rapid and simultaneous analysis has been achieved, with reasonably good accuracy and precision. No extraction procedures using non-aqueous solvents are required.     

Simultaneous Determination of Timolol Maleate and Pilocarpine Hydrochloride in Ophthalmic Solutions by First Derivative UV Spectrophotometry and PLS-1 Multivariate Calibration

ABSTRACT

The use of UV spectrophotometry (first-derivative/zero-crossing and zero-order spectra/multivariate calibration) is reported for the analysis of two miotic agents in ophthalmic solutions. The resolution of these mixtures has been accomplished without prior separation or derivatisation by using: 1) first-derivative measurements at two appropriate zero-crossing points: λ₁ = 222 nm, where the absorption corresponding to excipients is negligible, and λ₂ = 307 nm, where the contribution of pilocarpine and excipients to the overall absorption is negligible, and 2) partial least squares (PLS-1) regression analysis of zero-order spectral data. Although the components show an important degree of spectral overlap, they have been simultaneously determined with high accuracy, and with no interference from ophthalmic solution excipients.     

Resolution of ternary mixtures of salicylic, salicyluric and gentisic acids by partial least squares and principal component regression: Optimization of the scanning path in the excitation-emission matrices

The resolution of ternary mixtures of salicylic, salicyluric and gentisic acids has been accomplished by partial least squares (PLS) and principal component regression (PCR) multivariate calibration. The total luminescence information of the compounds has been used to optimize the spectral data set to perform the calibration. A comparison between the predictive ability of the three multivariate calibration methods, PLS-1, PLS-2 and PCR, on three spectral data sets, excitation, emission and synchronous spectra, has been performed. The excitation spectrum has been the best scanning path for salicylic and salicyluric acid determinations, while the emission spectrum has been the best for the gentisic acid determination. The convenience of analysing the total luminescence spectrum information when using multivariate calibration methods on fluorescence data is demonstrated.     

Comparison of near-infrared and mid-infrared spectroscopy for the determination of distillation property of kerosene

Near-infrared (NIR) and mid-infrared (MIR) spectroscopy have been compared and evaluated for the determination of the distillation property of kerosene with the use of partial least squares (PLS) regression. Since kerosene is a complex mixture of similar hydrocarbons, both spectroscopic methods will be best evaluated with this complex sample matrix. PLS calibration models for each percent recovery temperature have been developed by using both NIR and MIR spectra without spectral pretreatment. Both methods have shown good correlation with the corresponding reference method, however NIR provided better calibration performance over MIR. To rationalize the improved calibration performance of NIR, spectra of the same kerosene sample were continuously collected and the corresponding spectral reproducibility was evaluated. The greater spectral reproducibility including signal-to-noise ratio of NIR led to the improved calibration performance, even though MIR spectroscopy provided more qualitative spectral information. The reproducibility of measurement, signal-to-noise ratio, and richness of qualitative information should be simultaneously considered for proper selection of a spectroscopic method for quantitative analysis.     

Sustained prediction ability of net analyte preprocessing methods using reduced calibration sets. Theoretical and experimental study involving the spectrophotometric analysis of multicomponent mixtures

A newly developed multivariate method involving net analyte preprocessing (NAP) was tested using central composite calibration designs of progressively decreasing size regarding the multivariate simultaneous spectrophotometric determination of three active components (phenylephrine, diphenhydramine and naphazoline) and one excipient (methylparaben) in nasal solutions. Its performance was evaluated and compared with that of partial least-squares (PLS-1). Minimisation of the calibration predicted error sum of squares (PRESS) as a function of a moving spectral window helped to select appropriate working spectral ranges for both methods. The comparison of NAP and PLS results was carried out using two tests: (1) the elliptical joint confidence region for the slope and intercept of a predicted versus actual concentrations plot for a large validation set of samples and (2) the D-optimality criterion concerning the information content of the calibration data matrix. Extensive simulations and experimental validation showed that, unlike PLS, the NAP method is able to furnish highly satisfactory results when the calibration set is reduced from a full four-component central composite to a fractional central composite, as expected from the modelling requirements of net analyte based methods.     

OWAVEC: a combination of wavelet analysis and an orthogonalization algorithm as a pre-processing step in multivariate calibration

In an spectroscopic context, when a calibration model based on partial least squares is developed to predict a response, it is often the case that a high percentage of variation in the data explained by the first latent variable is not accompanied by an equally high percentage of variation in the studied response. The addition of more components can slowly improve the calibration model, but with negative effects on the robustness and interpretability of the final model. To solve this problem, several pre-processing methods have been proposed to remove only a portion unrelated to the studied response from the spectral matrix.Moreover, the need for efficient compression methods is increasingly important due to the large size of the data currently collected. In this sense, discrete wavelet transform has proven that it can achieve good compression without losing relevant information when used on individual signals.This paper introduces a new pre-processing method, orthogonal wavelet correction (OWAVEC) that tries to lump together two important needs in multivariate calibration: signal correction and compression. The new method has been tested on a set of diesel fuels using viscosity as variable response, and its results have been compared not only with those obtained from original data but also with those provided by other correction methods. The first practical results are encouraging, as the method generates considerably better calibration models compared to the model developed from raw data and provides results as least so good as other orthogonal correction methods.     

A new democratic phase coherent data-scatter technique for calibration, measurement, fingerprinting and rapid archival identification of ultraviolet-visible multi-component food spectra

The new theory of democratic phase coherent data-scatter (DPCD-S) is introduced. Basics of UV-visible spectrometry theory and error propagation have been presented. The qualitative spectral analysis provided is point-by-point over the complete data set and not just limited to Lambda-maxima. Equal weightings of the ‘voting’ data scattering algorithm are employed in the analysis of both the calibration and food colour data and this is consequently the democratic algorithm. The paper shows how the technique can be used with UV-visible standards to analyse the wavelength and photometric calibration of a spectrophotometer. The main results relate to the analysis of a series of spectra taken on complex mixtures of three important food dyes and their quantitative analysis using the phase coherent data-scatter technique. This method is shown to offer new possibilities for identifying and archiving UV-visible spectra from a single point in a transform space. Complex spectra can therefore be represented by a single point in this transform space, which is weighted by the ‘votes’ of all the data points in the complex data set. The software allows the user to interrogate the scatter results and locate the scatter point to the specific spectral positions. A new mathematical operator has been introduced to resolve any possible coincidence of two spectral projection points. Analysis of two close spectra from very-different admixtures of food colours shows powerfully the utility of this operator. Error propagation severely limits the accuracy of the usual UV-method of simultaneous equation secondary mixtures analysis.     

Estimation of the composition of heparin mixtures from various origins using proton nuclear magnetic resonance and multivariate calibration methods

A multivariate calibration method for the characterization of heparin samples based on the analysis of (1)H nuclear magnetic resonance (NMR) spectral data is proposed. Heparin samples under study consisted of two-component or four-component mixtures of heparins from porcine, ovine and bovine mucosae and bovine lung. Although the (1)H NMR spectra of all heparin types were highly overlapping, each origin showed some particular features that could be advantageously used for the quantification of the components. These features mainly concerned the anomeric H, which appeared in the range 5.0-5.7 ppm and the peaks of acetamidomethyl protons at 2.0-2.1 ppm. The determination of the percentage of each heparin class depended on these differences and was carried out using partial least squares regression (PLS) as a calibration method. Prior to the PLS analysis, the spectral data were standardized using the internal standard peak (sodium 4,4-dimethyl-4-silapentanoate- 2,2,3,3- d (4), TSP) as the reference. The quantification of each heparin type in the samples using PLS models built with 4 or 5 components was satisfactory, with an overall prediction error ranging from 3% to 10%.     

Improving prediction selectivity for on-line near-infrared monitoring of components in etchant solution by spectral range optimization

The components (H₃PO₄, HNO₃, CH₃COOH and water) in an etchant solution have been accurately measured in an on-line manner using near-infrared (NIR) spectroscopy by directly illuminating NIR radiation through a Teflon line. In particular, the spectral features according to the change of H₃PO₄ or HNO₃ concentrations were not mainly from NIR absorption themselves, but from the perturbation (or displacement) of water bands; therefore, the resulting spectral variations were quite similar to each other. Consequently partial least squares (PLS) prediction selectivity among the components should be the most critical issue for continuous on-line compositional monitoring by NIR spectroscopy. To improve selectivity of the calibration model, we have optimized the calibration models by finding selective spectral ranges with the use of moving window PLS. Using the optimized PLS models for each component, the resulting prediction accuracies were substantially improved. Furthermore, on-line prediction selectivity was evaluated by spiking individual pure components step by step and examining the resulting prediction trends. When optimized PLS models were used, each concentration was selectively and sensitively varied at each spike; meanwhile, when whole or non-optimized ranges were used for PLS, the prediction selectivity was greatly degraded. This study verifies that the selection of an optimal spectral range for PLS is the most important factor to make Teflon-based NIR measurements successful for on-line and real-time monitoring of etching solutions.     

Feasibility of monitoring acetic acid process using near-infrared spectroscopy

Near-infrared (NIR) spectroscopy has been utilized to demonstrate its feasibility for the measurement of major components in the acetic acid process. In order to simulate the acetic acid process, synthetic mixtures were prepared from five different components: acetic acid, methyl acetate, methyl iodide, water, and potassium iodide. Partial least squares (PLS) regression was utilized to differentiate the spectral characteristics as well as to quantify each component for the mixtures. The spectral features of acetic acid, methyl acetate, methyl iodide, and water are noticeably different with each other over the entire NIR region. The quantity of iodide ion, which does not absorb NIR radiation, was determined using the wavelength shift and intensity change of water absorption band caused by the change of iodide ion concentration. The PLS calibration results of the five components show good correlation with reference data. They also demonstrate the technical feasibility of NIR spectroscopy for monitoring important components in the acetic acid process.     

Simultaneous spectrophotometric determination of paracetamol, ibuprofen and caffeine in pharmaceuticals by chemometric methods

In this study, the simultaneous determination of paracetamol, ibuprofen and caffeine in pharmaceuticals by chemometric approaches using UV spectrophotometry has been reported as a simple alternative to using separate models for each component. Spectra of paracetamol, ibuprofen and caffeine were recorded at several concentrations within their linear ranges and were used to compute the calibration mixture between wavelengths 200 and 400 nm at an interval of 1 nm in methanol:0.1 HCl (3:1). Partial least squares regression (PLS), genetic algorithm coupled with PLS (GA-PLS), and principal component-artificial neural network (PC-ANN) were used for chemometric analysis of data and the parameters of the chemometric procedures were optimized. The analytical performances of these chemometric methods were characterized by relative prediction errors and recoveries (%) and were compared with each other. The GA-PLS shows superiority over other applied multivariate methods due to the wavelength selection in PLS calibration using a genetic algorithm without loss of prediction capacity. Although the components show an important degree of spectral overlap, they have been determined simultaneously and rapidly requiring no separation step. These three methods were successfully applied to pharmaceutical formulation, capsule, with no interference from excipients as indicated by the recovery study results. The proposed methods are simple and rapid and can be easily used in the quality control of drugs as alternative analysis tools.     

Determination of antihypertensive mixtures by use of a chemometrics-assisted spectrophotometric method

A simple multivariate calibration method for analysis of two types of hypotensive mixture is described. The mixtures are composed of chlorthalidone with atenolol or chlorthalidone with both amiloride hydrochloride and atenolol. The components of the mixtures result in substantial spectral overlap—between 87.5 and 91.0%. Resolution of the mixtures under investigation has been accomplished mainly by using CLS and PCR methods. The components in each mixture have been simultaneously determined in three commercial dosage forms with high accuracy and without interference from commonly encountered excipients and additives. Good recoveries were obtained with both synthetic mixtures and commercial tablets. The results obtained were compared with those from pharmacopeial methods and found to be in good agreement. The results obtained from CLS and PCR were also compared with those obtained from a ¹D spectrophotometric method.     

A new formulation to estimate the variance of model prediction. Application to near infrared spectroscopy calibration

Evaluation of uncertainty affecting predictions is a major trend in analytical chemistry and chemometrics. Several approximate expressions and resampling methods have been proposed for the estimation of prediction uncertainty when using multivariate calibration. This article proposes a new expression for the variance of prediction, adapted to near infrared spectroscopy specificities and particularly to the spectral error structure, induced by the high colinearity of the variables. The proposed analytical expression enables a detailed evaluation of the different contributions and components of uncertainty affecting the model. An application to real data of feedstuff near infrared spectra related to protein content has shown its advantages.     

校正集样品特性对鼠药中毒鼠强近红外光谱分析的影响

根据市售鼠药样品成分各异且相对复杂,建立6种不同成分体系和9个不同样本容量的校正集,运用小波变换压缩鼠药的近红外透射光谱数据,结合BP反向神经网络算法对压缩的数据进行建模,考察校正集样品特性对模型预测能力的影响。试验结果表明:采用BP神经网络算法建立定量模型时,只要校正集样品中包含了与预测样品性质相似的样本,就能准确地对复杂样品进行近红外定量分析。当校正集容量分别为72和84时,模型预测结果趋于平稳。当校正集数量为96时,模型的最大相关系数为0.959 8,预测最小标准差和平均相对误差分别为1.893%和1.92%。     

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.