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.     

Resolution of Differential Pulse Voltammetric Peaks Using Genetic Algorithm Based Variable Selection‐Partial Least Squares and Principal Component‐Artificial Neural Networks

Differential Pulse Voltammetry has been used for the simultaneous determination of cysteine, tyrosine and trptophan on the unmodified glassy carbon electrode. In the analysis of these analytes in the same samples, the main difficulty is the high degree of overlapping of voltammograms. The relationships between the currents and the concentrations are complex and highly nonlinear. The predictive ability of principal component regression (PCR), partial least squares regression (PLS), genetic algorithm‐partial least squares regression (GA‐PLS) and principal component‐artificial neural networks (PC‐ANNs) were examined for simultaneous determination of three amino acids. For a regression model, everything that could not help in constructing the model may be considered as noise without further specification. PC‐ANN and GA‐PLS use significant data and show superiority over other applied multivariate methods. The proposed method was also applied satisfactorily to determination of analytes in some synthetic samples.     

Handling complex effects in slurry-sampling-electrothermal atomic absorption spectrometry by multivariate calibration

Analysis of solid samples by slurry-sampling-electrothermal atomic absorption spectrometry (SS-ETAAS) can imply spectral and chemical interferences caused by the large amount of concomitants introduced into the graphite furnace. Sometimes they cannot be solved using stabilized temperature platform furnace (STPF) conditions or typical approaches (previous sample ashing, use of chemical modifiers, etc.), which are time consuming and quite expensive. A new approach to handle interferences using multivariate calibrations (partial least squares, PLS, and artificial neural networks, ANN) is presented and exemplified with a real problem consisting on determining Sb in several solid matrices (soils, sediments and coal fly ash) as slurries by ETAAS.Experimental designs were implemented at different levels of Sb to develop the calibration matrix and assess which concomitants (seven ions were considered) modified the atomic signal mostly. They were Na⁺ and Ca²⁺ and they induced simultaneous displacement, depletion (enhancement) and broadening of the atomic peak. Here it is shown that these complex effects can be handled in a reliable, fast and cost-effective way to predict the concentration of Sb in slurry samples of several solid matrices. The method was validated predicting the concentrations of five certified reference materials (CRMs) and studying its robustness to current ETAAS problems. It is also shown that linear PLS can handle eventual non-linearities and that its results are comparable to more complex (non-linear) models, as those from ANNs.     

Development of a Novel Versatile Method for Determination of two Antihistamines in Association with Naphazoline Using Cathodically Pretreated Boron‐doped Diamond Electrode

Antihistamines such as pheniramine (PHN) or chlorpheniramine (CPH) are commonly associated with naphazoline (NPZ) in eye drops and nasal decongestants. In this work, a batch‐injection analysis system with multiple pulse amperometric (BIA‐MPA) detection has been applied for the first time for fast simultaneous determination of naphazoline (NPZ) and pheniramine (PHN) or NPZ and chlorpheniramine (CPH). PHN or CPH was selectively detected at +1.1 V and both PHN and NPZ or CPH and NPZ were detected at +1.3 V using boron doped diamond (BDD) as working electrode and Britton‐Robinson (BR) buffer (pH=10.0) as supporting electrolyte. The current of NPZ can then be obtained by subtraction of the currents detected at both potential pulses and applying a correction factor (CF). The proposed method presented good intra‐day repeatability (RSD between 0.7 and 3.2 % for PHN; 0.7 and 2.1 % for CPH; 1.5 and 4.0 % for NPZ; n=20), high analytical frequency (>80 injections h⁻¹), and limits of detection of 0.64, 0.47 and 0.11 μmol L⁻¹ for PHN, CPH and NPZ, respectively. The results obtained with the proposed method are in agreement with those obtained by HPLC (95 % confidence level).     

A review of multivariate calibration methods applied to biomedical analysis

The determination of the contents of therapeutic drugs, metabolites and other important biomedical analytes in biological samples is usually performed by using high-performance liquid chromatography (HPLC). Modern multivariate calibration methods constitute an attractive alternative, even when they are applied to intrinsically unselective spectroscopic or electrochemical signals. First-order (i.e., vectorized) data are conveniently analyzed with classical chemometric tools such as partial least-squares (PLS). Certain analytical problems require more sophisticated models, such as artificial neural networks (ANNs), which are especially able to cope with non-linearities in the data structure. Finally, models based on the acquisition and processing of second- or higher-order data (i.e., matrices or higher dimensional data arrays) present the phenomenon known as “second-order advantage”, which permits quantitation of calibrated analytes in the presence of interferents. The latter models show immense potentialities in the field of biomedical analysis. Pertinent literature examples are reviewed.     

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.     

Polarography and artificial neural network for the simultaneous determination of nalidixic acid and its main metabolite (7-hydroxymethylnalidixic acid)

Nalidixic acid (NA) and its main metabolite, 7-hydroxymethylnalidixic acid (OH-NA), are simultaneously determined by applying artificial neural networks (ANNs), to their square wave voltammetric signals. The scores of a PCR model, built with the voltammetric data of a set of standard samples, recorded between −0.70 and −1.0 V, are used as training set for the net for each compound. The trained nets (ANNs) are used for the simultaneous determination of NA and OH-NA in urine. The recovery values are comprised between 91 and 109% for NA and between 82 and 112% for OH-NA, being these results better than the results obtained by application of partial least squares (PLS) multivariate calibration.     

Net analyte signal-based simultaneous determination of antazoline and naphazoline using wavelength region selection by experimental design-neural networks

Net analyte signal (NAS)-based multivariate calibration methods were employed for simultaneous determination of anthazoline and naphazoline. The NAS vectors calculated from the absorbance data of the drugs mixture were used as input for classical least squares (CLS), principal component and partial least squares regression PCR and PLS methods. A wavelength selection strategy was used to find the best wavelength region for each drug separately. As a new procedure, we proposed an experimental design-neural network strategy for wavelength region optimization. By use of a full factorial design method, some different wavelength regions were selected by taking into account different spectral parameters including the starting wavelength, the ending wavelength and the wavelength interval. The performance of all the multivariate calibration methods, in all selected wavelength regions for both drugs, was evaluated by calculating a fitness function based on the root mean square error of calibration and validation. A three-layered feed-forward artificial neural network (ANN) model with back-propagation learning algorithm was employed to model the nonlinear relationship between the spectral parameters and fitness of each regression method. From the resulted ANN models, the spectral regions in which lowest fitness could be obtained were chosen. Comparison of the results revealed that the net NAS-PLS resulted in lower prediction error than the other models. The proposed NAS-based calibration method was successfully applied to the simultaneous analyses of anthazoline and naphazoline in a commercial eye drop sample.     

Determination of phenylephrine hydrochloride and chlorpheniramine maleate in binary mixture using chemometric-assisted spectrophotometric and high-performance liquid chromatographic-UV methods

Four methods have been developed for the simultaneous determination of phenylephrine hydrochloride and chlorpheniramine maleate without previous separation. In the first method both drugs are determined using first derivative UV spectrophotometry, with zero-crossing measurement. The second method depends on first derivative of the ratios spectra. The third method describes the use of multivariate spectrophotometric calibration for the simultaneous determination of the analyzed binary mixture where the resolution is accomplished by using partial least squares (PLS) regression analysis. In the fourth method (HPLC), a reversed-phase column and a mobile phase of methanol:water:acetonitrile (80:12:8 v/v/v/) at 0.9 ml/min flow rate have been used to separate both drugs with a UV detection at 270 nm. All the proposed methods are extensively validated. They have the advantage to be economic and time saving. All the described methods can be readily utilized for analysis of pharmaceutical formulations. The results obtained using the proposed methods are statistically analyzed and compared with some reported methods.     

Simultaneous determination of chlorpheniramine maleate and dexamethasone in a tablet dosage form by liquid chromatography

An accurate, simple, reproducible, and sensible liquid chromatographic method was developed and validated for the determination of chlorpheniramine maleate and dexamethasone in a tablet formulation. The analysis was performed at room temperature on a reversed-phase C18 column with UV detection at 254 nm. The mobile phase consisted of 7.5 mM monobasic potassium phosphate in methanol-water (62.5 + 37.5) at a constant flow rate of 1 mL/min. The method was validated in terms of linearity, precision, accuracy, and specificity by forced decomposition of chlorpheniramine maleate and dexamethasone initiated by using acid, base, water, hydrogen peroxide, heat, and light. The response was linear in the ranges of 0.04-0.12 and 0.006-0.016 mg/mL for chlorpheniramine maleate (r2 = 0.9999) and dexamethasone (r2 = 0.9994), respectively. The relative standard deviation values for intra- and interday precision studies were 2.39 and 2.02, respectively, for chlorpheniramine maleate and 2.39 and 1.25, respectively, for dexamethasone. Recoveries ranged from 95.07 to 101.95% for chlorpheniramine maleate and from 97.75 to 102.10% for dexamethasone.     

Simultaneous spectrophotometric determination of p-benzoquinone and chloranil after microcrystalline naphthalene extraction by using genetic algorithm-based wavelength selection-partial least squares regression.

Microcrystalline naphthalene extraction has been used for the preconcentration of p-benzoquinone and tetrachloro-p-benzoquinone (chloranil), after their reaction by aniline, and later simultaneous spectrophotometric analysis by genetic algorithm-partial least squares (GA-PLS) calibration. The chemical variables affecting the analytical performance of the methodology were studied and optimized. Under the optimum conditions i.e., [aniline] = 0.05 M and [naphthalene] = 2.2% (w/v), preconcentration of 25 ml of sample solution permitted the detection of 0.32 and 0.23 microg ml(-1) for p-benzoquinone and chloranil, respectively. The predictive abilities of partial least squares regression (PLS) and genetic algorithm-partial least squares regression (GA-PLS) were examined for simultaneous determination of two quinones. The GA-PLS shows superiority over other PLS methods due to the wavelength selection in PLS calibration using a genetic algorithm without loss of prediction capacity, provides useful information about the chemical system.     

An expeditious method for determining particle size distribution by near infrared spectroscopy: Comparison of PLS2 and ANN models

The particle size distribution of a solid product can be crucial parameter considering its application to different kinds of processes. The influence of particle size on near infrared (NIR) spectra has been used to develop effective alternative methods to traditional ones in order to determine this parameter. In this work, we used the chemometrical techniques partial least squares 2 (PLS2) and artificial neural networks (ANNs) to simultaneously predict several variables to the rapid construction of particle size distribution curves. The PLS2 algorithm relies on linear relations between variables, while the ANN technique can model non-linear systems.Samples were passed through sieves of different sieve opening in order to separate several size fractions that were used to construct two types of particle size distribution curves. The samples were recorded by NIR and their spectra were used with PLS2 and ANN to develop two calibration models for each. The correlation coefficients and relative standard errors of prediction (RSEP) have been used to assess the goodness of fit and accuracy of the results.The four calibration models studied provided statistically identical results based on RSEP values. Therefore, the combined use of NIR spectroscopy and PLS2 or ANN calibration models allows determining the particle size distributions accurately. The results obtained by ANN or PLS2 are statistically similar.     

A PLS-based extractive spectrophotometric method for simultaneous determination of carbamazepine and carbamazepine-10,11-epoxide in plasma and comparison with HPLC

Carbamazepine (CBZ) undergoes enzyme biotransformation through epoxidation with the formation of its metabolite, carbamazepine-10,11-epoxide (CBZE). A simple chemometrics-assisted spectrophotometric method has been proposed for simultaneous determination of CBZ and CBZE in plasma. A liquid extraction procedure was operated to separate the analytes from plasma, and the UV absorbance spectra of the resultant solutions were subjected to partial least squares (PLS) regression. The optimum number of PLS latent variables was selected according to the PRESS values of leave-one-out cross-validation. A HPLC method was also employed for comparison. The respective mean recoveries for analysis of CBZ and CBZE in synthetic mixtures were 102.57 (+/-0.25)% and 103.00 (+/-0.09)% for PLS and 99.40 (+/-0.15)% and 102.20 (+/-0.02)%. The concentrations of CBZ and CBZE were also determined in five patients using the PLS and HPLC methods. The results showed that the data obtained by PLS were comparable with those obtained by HPLC method.     

Simultaneous spectrophotometric determination of carbamazepine and phenytoin in serum by PLS regression and comparison with HPLC

Carbamazepine (CBZ) and phenytoin (PHT) are two antiepileptic drugs which are used simultaneously. In this paper a partial least-squares (PLS) calibration method is described for the simultaneous spectrophotometric determination of CBZ and PHT in plasma. Standard binary mixtures of CBZ and PHT have been resolved by application of PLS-1 to their UV spectra. Then, the binary standard solutions, spiked to plasma, were prepared and after the extraction of the drugs, their corresponding UV spectrum were analyzed by PLS regression to calculate the concentration of drugs in unknown plasma. A leave one out cross-validation procedure was employed to find the optimum numbers of latent variables using PRESS. A HPLC method was also applied for simultaneous determination of two drugs in the plasma and in methanol. The mean recoveries obtained by PLS were 98.4 and 98.2 for CBZ and PHT and those obtained by HPLC were 100.1 and 101.7, respectively. Although, the HPLC method showed better performance than PLS, it was found that the results obtained by PLS were comparable with those obtained by HPLC method.     

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 Determination of Mepyramine Maleate,Lidocaine Hydrochloride,and Dexpanthenol in Pharmaceutical Preparations by Partial Least‐Squares Multivariate Calibration

Abstract

A partial least‐squares calibration (PLS) method has been developed for simultaneous quantitative determination of mepyramine maleate (MAM), lidocaine hydrochloride (LIH), and dexpanthenol (DPA) in pharmaceutical preparations. The resolution of these mixtures has been accomplished by using partial least‐squares (PLS‐2) regression analysis of electronic absorption spectral data without prior separation or derivatization. The experimental calibration matrix was constructed with 27 samples. The concentration ranges considered were 2, 3, 4 µg mL^?1 for MAM, 2, 3, 4 µg mL^?1 for LIH, and 8, 10, 12 µg mL^?1 for DPA. The absorbances were recorded between 190 and 340 nm every 5 nm. The results show that PLS‐2 is a simple, rapid, and accurate method applied to the determination of these compounds in pharmaceuticals.     

非线性偏最小二乘法在速效伤风胶囊紫外分光光度分析中的应用

本文介绍了非线性偏最小二乘法的基本原理及算法。以速效伤风胶囊的紫外分光光度分析为例,扑热息痛、咖啡因、扑尔敏、人工牛黄四组分的测定结果满意,且明显好于线性偏最小二乘法。本法为多组分混合体系的分光光度分析提供了更为理想的新途径。     

Monitoring substrate and products in a bioprocess with FTIR spectroscopy coupled to artificial neural networks enhanced with a genetic-algorithm-based method for wavelength selection

An experiment was developed as a simple alternative to existing analytical methods for the simultaneous quantitation of glucose (substrate) and glucuronic acid (main product) in the bioprocesses Kombucha by using FTIR spectroscopy coupled to multivariate calibration (partial least-squares, PLS-1 and artificial neural networks, ANNs). Wavelength selection through a novel ranked regions genetic algorithm (RRGA) was used to enhance the predictive ability of the chemometric models. Acceptable results were obtained by using the ANNs models considering the complexity of the sample and the speediness and simplicity of the method. The accuracy on the glucuronic acid determination was calculated by analysing spiked real fermentation samples (recoveries ca. 115%).