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.     

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.     

Simultaneous spectrophotometric-multivariate calibration determination of several components of ophthalmic solutions: phenylephrine, chloramphenicol, antipyrine, methylparaben and thimerosal

The use of multivariate spectrophotometric calibration for the simultaneous determination of several active components and excipients in ophthalmic solutions is presented. The resolution of five-component mixtures of phenylephrine, chloramphenicol, antipyrine, methylparaben and thimerosal has been accomplished by using partial least-squares (PLS-1) and a variant of the so-called hybrid linear analysis (HLA). Notwithstanding the presence of a large number of components and their high degree of spectral overlap, they have been determined simultaneously with high accuracy and precision, with no interference, rapidly and without resorting to extraction procedures using non aqueous solvents. A simple and fast method for wavelength selection in the calibration step is presented, based on the minimisation of the predicted error sum of squares (PRESS) calculated as a function of a moving spectral window.     

New strategy to identify radicals in a time evolving EPR data set by multivariate curve resolution-alternating least squares

Electron paramagnetic resonance (EPR) spectroscopy is a powerful technique that is able to characterize radicals formed in kinetic reactions. However, spectral characterization of individual chemical species is often limited or even unmanageable due to the severe kinetic and spectral overlap among species in kinetic processes. Therefore, we applied, for the first time, multivariate curve resolution-alternating least squares (MCR-ALS) method to EPR time evolving data sets to model and characterize the different constituents in a kinetic reaction. Here we demonstrate the advantage of multivariate analysis in the investigation of radicals formed along the kinetic process of hydroxycoumarin in alkaline medium. Multiset analysis of several EPR-monitored kinetic experiments performed in different conditions revealed the individual paramagnetic centres as well as their kinetic profiles. The results obtained by MCR-ALS method demonstrate its prominent potential in analysis of EPR time evolved spectra.     

Simultaneous Spectrophotometric Determination of Imipramine Hydrochloride with Chlordiazepoxide and Nortriptyline Hydrochloride with Fluphenazine Hydrochloride

Spectrophotometry was used with multivariate calibration to simultaneously determine compounds in mixtures. Two antidepressant mixtures were investigated: imipramine hydrochloride and chlordiazepoxide and nortriptyline hydrochloride and fluphenazine hydrochloride. Considerable spectral overlap and large differences in component concentrations were challenges. Since this type of analysis is often performed using complex algorithms, a simple strategy was used here for the simultaneous determination of both mixture components by classical least squares, principal component regression, and partial least squares. Experimental design was used to select the optimum parameters including the wavelength range, sampling interval, software, and derivative order. Accuracy was enhanced by proper wavelength selection. In addition, derivatives of the raw spectra improved the selectivity. The standard deviation, deviation of mean recovery from 100%, and prediction ability of the models were used as the responses. In respect to these terms, first-order derivatization of the spectra and a sampling interval of 1?nm provided the best results. In particular, the low concentration compounds in the mixtures (chlordiazepoxide and fluphenazine) were determined more accurately with precision lower than 3%. The strategy was used for the quality control of pharmaceuticals containing the mixtures without chemical pretreatment.     

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)].     

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.     

Testing the performance of pure spectrum resolution from Raman hyperspectral images of differently manufactured pharmaceutical tablets

Chemical imaging is a rapidly emerging analytical method in pharmaceutical technology. Due to the numerous chemometric solutions available, characterization of pharmaceutical samples with unknown components present has also become possible. This study compares the performance of current state-of-the-art curve resolution methods (multivariate curve resolution-alternating least squares, positive matrix factorization, simplex identification via split augmented Lagrangian and self-modelling mixture analysis) in the estimation of pure component spectra from Raman maps of differently manufactured pharmaceutical tablets. The batches of different technologies differ in the homogeneity level of the active ingredient, thus, the curve resolution methods are tested under different conditions. An empirical approach is shown to determine the number of components present in a sample. The chemometric algorithms are compared regarding the number of detected components, the quality of the resolved spectra and the accuracy of scores (spectral concentrations) compared to those calculated with classical least squares, using the true pure component (reference) spectra. It is demonstrated that using appropriate multivariate methods, Raman chemical imaging can be a useful tool in the non-invasive characterization of unknown (e.g. illegal or counterfeit) pharmaceutical products.     

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.     

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 Artificial Neural Networks with Partial Least Squares Regression for Simultaneous Determinations by ICP-AES

Simultaneous determination of several elements （U, Ta, Mn, Zr and W） with inductively coupled plasma atomic emission spectrometry （ICP-AES） in the presence of spectral interference was performed using chemometrics methods. True comparison between artificial neural network （ANN） and partial least squares regression （PLS） for simultaneous determination in different degrees of overlap was investigated. The emission spectra were recorded at uranium analytical line （263.553 nm） with a 0.06 nm spectral window by ICP-AES. Principal component analysis was applied to data and scores on 5 dominant principal components were subjected to ANN. A 5-5-5 （input, hidden and output neurons） network was used with linear transfer function after both hidden and output layers. The PI,S model was trained with five latent variables and 20 samples in calibration set. The relative errors of predictions （REP） in test set were 3.75% and 3.56% for ANN and PLS respectively.     

混合线性分析-分光光度法同时测定微量锌、镉、汞

提出了以2－（5－溴－2－吡啶偶氮）－5－二乙氨基酚为显色剂分光光度法同时测定锌、镉、汞的新方法。由于在可见区3种络合物的吸收光谱具有相似的特征和严重重叠,本文采用混合线性分析进行光谱分辨,各组分的纯光谱则用最小二乘法从校正集中求出。讨论了显色条件,波长选择,纯光谱的确定和吸光度加合性等因素对测定的影响。方法具有简单,快速,准确等优点。已成功地应用于混合试样中锌、镉、汞的同时测定,并与同条件下偏最小二乘法的计算结果进行了对比。     

Quantitative analysis of tea using ytterbium‐based internal standard near‐infrared spectroscopy coupled with boosting least‐squares support vector regression

The present study demonstrated the possibility of utilizing the ytterbium (Yb)‐based internal standard near‐infrared (NIR) spectroscopic measurement technique coupled with multivariate calibration for quantitative analysis of tea, including total free amino acids and total polyphenols in tea. Yb is a rare earth element aimed to compensate for the spectral variation induced by the alteration of sample quantity during the spectral measurement of the powdered samples. Boosting was invoked to be combined with least‐squares support vector regression (LS‐SVR), forming boosting least‐squares support vector regression (BLS‐SVR) for the multivariate calibration task. The results showed that the tea quality could be accurately and rapidly determined via the Yb‐based internal standard NIR spectroscopy combined with BLS‐SVR method. Moreover, the introduction of boosting drastically enhanced the performance of individual LS‐SVR, and BLS‐SVR compared favorably with partial least‐squares regression. Copyright © 2013 John Wiley & Sons, Ltd.     

Supervised principal components: a new method for multivariate spectral analysis

The supervised principal components (SPC) method was proposed by Bair and Tibshirani for statistics regression problems where the number of variables greatly exceeds the number of samples. This case is extremely common in multivariate spectral analysis. The objective of this research is to apply SPC to near‐infrared and Raman spectral calibration. SPC is similar to traditional principal components analysis except that it selects the most significant part of wavelength from the high‐dimensional spectral data, which can reduce the risk of overfitting and the effect of collinearity in modeling according to a semi‐supervised strategy. In this study, four conventional regression methods, including principal component regression, partial least squares regression, ridge regression, and support vector regression, were compared with SPC. Three evaluation criteria, coefficient of determination (R²), external correlation coefficient (Q²), and root mean square error of prediction, were calculated to evaluate the performance of each algorithm on both near‐infrared and Raman datasets. The comparison results illustrated that the SPC model had a desirable ability of regression and prediction. We believe that this method might be an alternative method for multivariate spectral analysis. Copyright © 2013 John Wiley & Sons, Ltd.     

Elimination of spectral interferences in the reaction of 2-thiobarbituric acid with malonaldehyde, 2-furfuraldehyde and 5-hydroxymethyl-2-furfuraldehyde by partial least squares multivariate calibration (PLS)

Summary The spectral resolution of ternary mixtures of malonaldehyde (MLD), 2-furfuraldehyde (FUR) and 5-hydroxymethyl-2-furfuraldehyde (HMF) in the presence of glyoxal and biliverdine is achieved by partial least squares multivariate calibration (PLS). The spectrophotometric method is based on the reaction of these substances with 2-thiobarbituric acid (TBA). A calibration set of standard samples has been statistically designed in the presence of adequate amounts of both interferent compounds in several degrees of concentration. The possibility of a spectrophotometric determination of mixtures of MLD, FUR and HMF in the presence of glyoxal and biliverdine is demonstrated. A comparative study of the results found by application of PLS-1 and PLS-2 methods is presented.     

A comparison of multivariate analysis techniques and variable selection strategies in a laser-induced breakdown spectroscopy bacterial classification

Laser-induced breakdown spectroscopy has been used to obtain spectral fingerprints from live bacterial specimens from thirteen distinct taxonomic bacterial classes representative of five bacterial genera. By taking sums, ratios, and complex ratios of measured atomic emission line intensities three unique sets of independent variables (models) were constructed to determine which choice of independent variables provided optimal genus-level classification of unknown specimens utilizing a discriminant function analysis. A model composed of 80 independent variables constructed from simple and complex ratios of the measured emission line intensities was found to provide the greatest sensitivity and specificity. This model was then used in a partial least squares discriminant analysis to compare the performance of this multivariate technique with a discriminant function analysis. The partial least squares discriminant analysis possessed a higher true positive rate, possessed a higher false positive rate, and was more effective at distinguishing between highly similar spectra from closely related bacterial genera. This suggests it may be the preferred multivariate technique in future species-level or strain-level classifications.     

Determination of Nitrogen in Rapeseed by Fourier Transform Infrared Photoacoustic Spectroscopy and Independent Component Analysis

Fourier-transform mid-infrared photoacoustic spectroscopy was utilized for rapid and nondestructive determination of nitrogen in rapeseeds. Rapeseed spectra were characterized by independent component analysis for quantitative calibration. A calibration model was built by using independent components as the input for partial least squares. Compared to full-spectrum partial least squares, the combined model achieved higher prediction accuracy with a residual predictive deviation of 2.06. Moreover, a genetic algorithm coupled with partial least squares was adopted to optimize the independent components for partial least square modeling and provide a further refined model with the highest residual predictive deviation of 2.12. A t-test verified a high congruence between results obtained by calibration models and the reference Kjeldahl method. This study demonstrated the promise of Fourier-transform mid-infrared photoacoustic spectroscopy for the determination of nitrogen in rapeseeds and the applicability of independent components for multivariate calibration.     

Determination of amoxicillin in pharmaceuticals using sequential injection analysis (SIA): Evaluation of the presence of interferents using multivariate curve resolution

In this study, we report a method for determining amoxicillin in pharmaceuticals using sequential injection analysis (SIA) with a diode-array spectrophotometric detector. Before determining the amoxicillin, we use multivariate curve resolution with alternating least squares (MCR-ALS) to investigate whether any interferents are present. With a suitable analytical sequence, we can use SIA to generate a pH gradient and, for each sample, obtain a matrix of data that have been analysed by several chemometric techniques based on multivariate analysis: principal components analysis (PCA), simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) and MCR-ALS. In this way we obtain the concentration profiles and spectra of the species in the sample.We studied six pharmaceuticals containing amoxicillin. Two of these pharmaceuticals contained no interferents, one contained an interferent but amoxicillin had a selective spectral area with respect to it, and the other three contained interferents. For the first three samples, we set up a system of univariate calibration, which determines amoxicillin quickly (it can analyse 20 samples/h) using inexpensive instrumentation and reactives.     

Development and validation of chemometrics-assisted spectrophotometry and liquid chromatography methods for the simultaneous determination of the active ingredients in two multicomponent mixtures containing chlorpheniramine maleate and phenylpropanolamine hydrochloride

Multivariate spectrophotometric calibration and liquid chromatography (LC) methods were used for the simultaneous determination of the active ingredients in 2 multicomponent mixtures containing chlorpheniramine maleate and phenylpropanolamine hydrochloride with ibuprofen and caffeine (mixture 1) or with propyphenazone (mixture 2). For the multivariate spectrophotometric calibration methods, principal component regression (PCR) and partial least squares (PLS-1), a calibration set of the mixtures consisting of the components of each mixture was prepared in distilled water. A leave-1-out cross-validation procedure was used to find the optimum numbers of latent variables. Analytical parameters such as sensitivity, selectivity, analytical sensitivity, limit of quantitation, and limit of detection were determined for both PLS-1 and PCR. The LC method depends on the use of a cyanopropyl column with the mobile phase acetonitrile-12 mM ammonium acetate, pH 5.0 (25 + 75, v/v), for mixture 1 or acetonitrile-10 mM potassium dihydrogen phosphate, pH 4.7 (45 + 55, v/v), for mixture 2; the UV detector was set at 212 nm. In spite of the presence of a high degree of spectral overlap of these components, they were rapidly and simultaneously determined with high accuracy and precision, with no interference from the matrix excipients. The proposed methods were successfully applied to the analysis of pharmaceutical formulations and laboratory-prepared mixtures containing the 2 multicomponent combinations.     

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.