Fractional-continuous wavelet transforms and ultra-performance liquid chromatography for the multicomponent analysis of a ternary mixture containing thiamine, pyridoxine, and lidocaine in ampules

New chemometric approaches based on the application of partial least squares (PLS) and principal component regression (PCR) algorithms with fractional wavelet transform (FWT) and continuous wavelet transform (CWT) are proposed for the spectrophotometric multicomponent determination of thiamine hydrochloride (B1), pyridoxine hydrochloride (B6), and lidocaine hydrochloride (LID) in ampules without any separation step. In this study PLS and PCR techniques were applied to the raw spectral data, FWT-coefficients, and FWT-CWT-coefficients. These calibration models were labeled as Raw-PLS and Raw-PCR, FWT-PLS and FWT-PCR, and FWT-CWT-PLS and FWT-CWT-PCR, respectively. A new ultra-performance liquid chromatographic (UPLC) method was developed for the comparison of the results obtained by applying the chemometric calibration methods. Chromatographic separation and determination of B1, B6, and LID in ampules were performed on an Acquity UPLC BEH C18 column (50x2.1 mm id, 1.7 pm particle size) using gradient elution with a mobile phase consisting of methanol and 0.01 M HCI at a constant flow rate of 0.6 mL/min. These combined chemometric calibrations and UPLC were validated by analyzing various ternary mixtures, B1, B6, and LID. The proposed chemometric approaches (signal processing-multivariate calibrations) and UPLC method were applied to the quantitative multicomponent analysis of marketed ampules containing the vitamins B1 and B6 with LID.     

A new fractional wavelet approach for the simultaneous determination of ampicillin sodium and sulbactam sodium in a binary mixture

A new application of the fractional wavelet transform (FWT) was proposed for the simultaneous determination of ampicillin (AP) and sulbactam (SB) in a pharmaceutical combination for injection. FWT approach is a new powerful tool for removing noise and irrelevant information from the absorption spectra. Cardinal information having higher peak amplitude, eliminated noise, sharp peaks with shrinking width of spectral range was obtained by the application of FWT procedure to the original absorption spectra. In this paper, FWT approach was subjected to the data vector of the UV-signals obtained from AP and SB in the wavelength range of 211.5-313.8 nm. Derivative transform was applied to the original absorption signal together with its FWT generalization. The calibration graphs for AP and SB were obtained by measuring the FWT and usual derivative amplitudes at zero-crossing points. The method validation was carried out by using the synthetic mixture analysis. Our proposed FWT approach was compared with the usual derivative spectrophotometry and chemometric methods (CLS, PCR and PLS) and a good agreement was reported.     

Mathematical algorithms applied to the multi-linear regression functions for the multicomponent determination of pharmaceutical dosage form containing three-component mixtures

In the presence of closely overlapping spectra, the quantitative multiresolution of ternary mixtures of three active compounds paracetamol (PAR), caffeine (CAF) and acetylsalycilic acid (ASP) in tablets, without using pretreatment such as separation step and graphical procedure of spectra was accomplished by the multivariate spectral calibration models, tri-linear regression calibration (TLRC), multi-linear regression calibration (MLRC) and Cramer's rule solution (CRS) of three linear equation functions in the matrix form. In the first two models, TLRC and MLRC are based on the use of the linear regression functions at selected wavelength sets in the spectral region of 210-300 nm. In the case of CRS model, A1(1) (1%, 1 cm) were used to obtain three linear equation functions and this linear equation system was resolved by the Cramer's rule for the prediction of PAR, CAF and ASP in samples. In the TLRC and CRS models, the selection of the appropriate wavelength set was performed by the Kaiser's technique. The algorithms of these mathematical calibration models were briefly described. The validation of TLRC, MLRC and CRS models was carried out by analyzing various synthetic ternary mixtures and by using the standard addition technique. These three calibration approaches were applied to the analysis of the real pharmaceutical tablets containing PAR, CAF and ASP. The obtained results were statistically compared with each other by using experimental and statistical tests. In the comparison of TLRC and MLRC models to the classical approach, CRS technique, the successful assay results were observed for the quantitative multiresolution of ternary mixture of the subject active compounds.     

An adaptive strategy for selecting representative calibration samples in the continuous wavelet domain for near-infrared spectral analysis

Sample selection is often used to improve the cost-effectiveness of near-infrared (NIR) spectral analysis. When raw NIR spectra are used, however, it is not easy to select appropriate samples, because of background interference and noise. In this paper, a novel adaptive strategy based on selection of representative NIR spectra in the continuous wavelet transform (CWT) domain is described. After pretreatment with the CWT, an extension of the Kennard–Stone (EKS) algorithm was used to adaptively select the most representative NIR spectra, which were then submitted to expensive chemical measurement and multivariate calibration. With the samples selected, a PLS model was finally built for prediction. It is of great interest to find that selection of representative samples in the CWT domain, rather than raw spectra, not only effectively eliminates background interference and noise but also further reduces the number of samples required for a good calibration, resulting in a high-quality regression model that is similar to the model obtained by use of all the samples. The results indicate that the proposed method can effectively enhance the cost-effectiveness of NIR spectral analysis. The strategy proposed here can also be applied to different analytical data for multivariate calibration.     

Application of the wavelet method for the simultaneous quantitative determination of benazepril and hydrochlorothiazide in their mixtures

The discrete and continuous wavelet transforms were applied to the overlapping signal analysis of the ratio data signal for simultaneous quantitative determination of the title subject compounds in samples. The ratio spectra data of the binary mixtures containing benazepril (BE) and hydrochlorothiazide (HCT) were transferred as data vectors into the wavelet domain. Signal compression, followed by a 1-dimension continuous wavelet transform (CWT), was used to obtain coincident transformed signals for pure BE and HCT and their mixtures. The coincident transformed amplitudes corresponding to both maximum and minimum points allowed construction of calibration graphs for each compound in the binary mixture. The validity of CWT calibrations was tested by analyzing synthetic mixtures of the investigated compounds, and successful results were obtained. All calculations were performed within EXCEL, C++, and MATLAB6.5 softwares. The obtained results indicated that our approach was flexible and applicable for the binary mixture analysis.     

Simultaneous spectrophotometric determination of metformin hydrochloride and glibenclamide in binary mixtures using combined discrete and continuous wavelet transforms

In this work, a combined discrete and continuous wavelet transform analysis was developed for simultaneous spectrophotometric determinations of metformin hydrochloride and glibenclamide, two antidiabetic drugs, in binary mixtures without any chemical pretreatment. Absorption spectra were subjected to the 4-level db4 discrete wavelet transform (DWT) for signal de-noising. Selected continuous wavelet transform (CWT) families (rbio3.1 with scaling factor, a = 80, and gaus2, a = 60) were applied on these de-noised signals. Finally, a zero-crossing technique was used for the construction of calibration curves for both drugs. The proposed method was validated by analyzing synthetic mixtures of the investigated drugs with various concentrations. The amount of metformin hydrochloride and glibenclamide were determined by using CWT amplitudes in zero-crossing points. The mean recovery values of metformin hydrochloride and glibenclamide were found between 98.6-102.0 and 97.9-102.4% for rbio3 and 98.3-101.2 and 97.1-101.4% for gaus2 families, respectively. The obtained results showed that the developed method is a simple, rapid and precise procedure for the simultaneous determination of metformin hydrochloride and glibenclamide in binary mixtures.     

A new application of continuous wavelet transform to overlapping chromatograms for the quantitative analysis of amiloride hydrochloride and hydrochlorothiazide in tablets by ultra-performance liquid chromatography

A new application of continuous wavelet transform (CWT) to overlapping peaks in a chromatogram was developed for the quantitative analysis of amiloride hydrochloride (AML) and hydrochlorothiazide (HCT) in tablets. Chromatographic analysis was done by using an ACQUITY ultra-performance LC (UPLC) BEH C18 column (50 x 2.1 mm id, 1.7 pm particle size) and a mobile phase consisting of methanol-0.1 M acetic acid (21 + 79, v/v) at a constant flow rate of 0.3 mL/min with diode array detection at 274 nm. The overlapping chromatographic peaks of the calibration set consisting of AML and HCT mixtures were recorded rapidly by using an ACQUITY UPLC H-Class system. The overlapping UPLC data vectors of AML and HCT drugs and their samples were processed by CWT signal processing methods. The calibration graphs for AML and HCT were computed from the relationship between concentration and areas of chromatographic CWT peaks. The applicability and validity of the improved UPLC-CWT approaches were confirmed by recovery studies and the standard addition technique. The proposed UPLC-CWT methods were applied to the determination of AML and HCT in tablets. The experimental results indicated that the suggested UPLC-CWT signal processing provides accurate and precise results for industrial QC and quantitative evaluation of AML-HCT tablets.     

Comparative spectral analysis of veterinary powder product by continuous wavelet and derivative transforms

Comparative simultaneous determination of chlortetracycline and benzocaine in the commercial veterinary powder product was carried out by continuous wavelet transform (CWT) and classical derivative transform (or classical derivative spectrophotometry). In this quantitative spectral analysis, two proposed analytical methods do not require any chemical separation process. In the first step, several wavelet families were tested to find an optimal CWT for the overlapping signal processing of the analyzed compounds. Subsequently, we observed that the coiflets (COIF-CWT) method with dilation parameter, a=400, gives suitable results for this analytical application. For a comparison, the classical derivative spectrophotometry (CDS) approach was also applied to the simultaneous quantitative resolution of the same analytical problem. Calibration functions were obtained by measuring the transform amplitudes corresponding to zero-crossing points for both CWT and CDS methods. The utility of these two analytical approaches were verified by analyzing various synthetic mixtures consisting of chlortetracycline and benzocaine and they were applied to the real samples consisting of veterinary powder formulation. The experimental results obtained from the COIF-CWT approach were statistically compared with those obtained by classical derivative spectrophotometry and successful results were reported.     

A novel method to calculate the approximate derivative photoacoustic spectrum using continuous wavelet transform

A novel method based on continuous wavelet transform (CWT) using Haar wavelet function for approximate derivative calculation of analytical signals is proposed and successfully used in processing the photoacoustic signal. An approximate nth derivative of an analytical signal can be obtained by applying n times of the wavelet transform to the signal. The results obtained from four other different methods--the conventional numerical differentiation, the Fourier transform method, the Savitzky-Golay method, and the discrete wavelet transform (DWT) method--were compared with the proposed CWT method; it was demonstrated that all the results are almost the same for signals without noise, but the proposed CWT method is superior to the former four methods for noisy signals. The approximate first and second derivative of the photoacoustic spectrum of Pr(Gly)3Cl3.3H2O and PrCl3.6H2O were obtained using the proposed CWT method; the results are satisfactory.     

Quantification of components in overlapping peaks from capillary electrophoresis by using continues wavelet transform method

The application of continues wavelet transform (CWT) for resolving overlapping peaks from capillary electrophoresis (CE) is described. Overlapping peaks can be resolved easily by transforming experimental signals into their wavelet coefficients. The proposed method was applied for the determination of benzoic acid and salicylic acid in overlapping peaks from CE. The composition of the two acids in Zuguangsan, a traditional Chinese patent medicine, was determined. The quantification results are satisfactory. CWT has been shown to be a practicable approach for resolving overlapping peaks and for quantitative determining coeluted compounds in overlapping peaks from CE. The quantification results obtained from CWT were compared with those obtained from numerical differentiation method. CWT has been shown prior to numerical differentiation method for processing experimental signals which contain noise.     

A novel method to calculate the approximate derivative photoacoustic spectrum using continuous wavelet transform

A novel method based on continuous wavelet transform (CWT) using Haar wavelet function for approximate derivative calculation of analytical signals is proposed and successfully used in processing the photoacoustic signal. An approximate nth derivative of an analytical signal can be obtained by applying n times of the wavelet transform to the signal. The results obtained from four other different methods – the conventional numerical differentiation, the Fourier transform method, the Savitzky-Golay method, and the discrete wavelet transform (DWT) method – were compared with the proposed CWT method; it was demonstrated that all the results are almost the same for signals without noise, but the proposed CWT method is superior to the former four methods for noisy signals. The approximate first and second derivative of the photoacoustic spectrum of Pr(Gly)₃Cl₃· 3 H₂O and PrCl₃· 6 H₂O were obtained using the proposed CWT method; the results are satisfactory. Received: 21 December 1999 / Revised: 28 February 2000 / Accepted: 7 March 2000     

Variable selection by modified IPW (iterative predictor weighting)-PLS (partial least squares) in continuous wavelet regression models

Variable selection is often used to produce more robust and parsimonious regression models. But when they are applied directly to the raw near-infrared spectra, it is not easy to select appropriate variables because background and noise will often overshadow or overlap the absorption bands of analyte. In this work, a new hybrid algorithm based on the selection of the most informative variables in the continuous wavelet transform (CWT) domain is described. The strategy is a combination of CWT and a procedure of modified iterative predictor weighting-partial least square (mIPW-PLS). After elimination of the background and noise in NIR spectra by CWT, the mIPW-PLS approach is used to select the most informative CWT coefficients. With the selected CWT coefficients, a PLS model is built finally for prediction. It is indicated that the extraction of most important variables in the CWT domain can effectively avoid the interference of background and noise, and result in a high quality of regression model with a very small number of variables and fewer PLS components.     

Multidetermination of thiamine HCl and pyridoxine HCl in their mixture using continuous daubechies and biorthogonal wavelet analysis

A new graphical method based on the one-dimensional wavelet transform (WT) was proposed and tested on mixture of thiamine hydrochloride (THI) and pyridoxine hydrochloride (PYR) in the presence of strongly overlapping signals. We selected from the data of the UV-VIS absorption spectra a signal consisting of 1150 points corresponding to the concentration range 8-32 mg ml⁻¹ for each vitamin and we subjected it to Daubechies8 (DAUB8) and Biorthogonal6.8 (BIOR6.8) wavelet transforms. Since the peaks of the transformed signals were bigger than original ones a zero crossing method was applied to obtain the calibration graphs. In addition, the validity of Beer-Lambert law was assumed for the transformed signals. An appropriate scale setting was choosing to obtain an alternative calibration for each method. Matlab 6.5 software was used for one-dimensional wavelet analysis and the basic concepts about wavelet method were given. The obtained results were successfully compared among each other as well as with those obtained by other literature methods. The method developed in this paper is rapid, easy to apply, not expensive and it is suitable for analyzing of the overlapping signals of compounds in their mixtures without any chemical pre-treatment.     

Continuous wavelet transform applied to removing the fluctuating background in near-infrared spectra

A novel method based on continuous wavelet transform (CWT) was proposed as a preprocessing tool for the near-infrared (NIR) spectra. Due to the property of the vanishing moments of the wavelet, the fluctuating background of the NIR spectra can be successfully removed through convolution of the spectra with an appropriate wavelet function. The vanishing moments of a wavelet and the scale parameter are two key factors that govern the result of the background elimination. The result of its application to both the simulated spectra and the NIR spectra of tobacco samples demonstrates that CWT is a competitive tool for removing fluctuating background in spectra.     

A new hybrid strategy for constructing a robust calibration model for near-infrared spectral analysis

A new hybrid algorithm is proposed for construction of a high-quality calibration model for near-infrared (NIR) spectra that is robust against both spectral interference (including background and noise) and multiple outliers. The algorithm is a combination of continuous wavelet transform (CWT) and a modified iterative reweighted PLS (mIRPLS) procedure. In the proposed algorithm the spectral interference is filtered by CWT at the first stage then mIRPLS is proposed to detect the multiple outliers in the CWT domain. Compared with the original IRPLS method, mIRPLS does not need to adjust variable parameters to achieve optimum calibration results, which makes it very convenient to perform in practice. The final PLS model is constructed robustly because both the spectral interference and multiple outliers are eliminated. In order to validate the effectiveness and universality of the algorithm, it was applied to two different sets of NIR spectra. The results indicate that the proposed strategy can greatly enhance the robustness and predictive ability of NIR spectral analysis.     

Resolution of overlapping signals in spectrometry using a wavelet packet transform and an Elman recurrent neural network

A novel method named a wavelet packet transform based Elman recurrent neural network (WPTERNN) was proposed for the simultaneous UV–visible spectrometric determination of Cu(II), Cd(II) and Zn(II). This method combined wavelet packet denoising with an Elman recurrent neural network. A wavelet packet transform was applied to perform data compression, to extract relevant information, and to eliminate noise and collinearity. An Elman recurrent network was applied for nonlinear multivariate calibration. In this case, using trials, the kind of wavelet function, the decomposition level, and the number of hidden nodes for the WPTERNN method were selected as Daubechies 14, 3, and 8, respectively. A program (PWPTERNN) was designed that could perform the simultaneous determination of Cu(II), Cd(II) and Zn(II). The relative standard errors of prediction (RSEP) obtained for all components using WPTERNN, a Elman recurrent neural network (ERNN), partial least squares (PLS), principal component regression (PCR), Fourier transform based PCR (FTPCR), and multivariate linear regression (MLR) were compared. Experimental results demonstrated that the WPTERRN method was successful even where there was severe overlap of spectra. The results obtained from an additional test case also demonstrated that the WPTERNN method performed very well. Figure The part of WP coefficients obtained by wavelet packet transforms     

Multicomponent analysis of electrochemical signals in the wavelet domain

Successful applications of multivariate calibration in the field of electrochemistry have been recently reported, using various approaches such as multilinear regression (MLR), continuum regression, partial least squares regression (PLS) and artificial neural networks (ANN). Despite the good performance of these methods, it is nowadays accepted that they can benefit from data transformations aiming at removing baseline effects, reducing noise and compressing the data. In this context the wavelet transform seems a very promising tool. Here, we propose a methodology, based on the fast wavelet transform, for feature selection prior to calibration. As a benchmark, a data set consisting of lead and thallium mixtures measured by differential pulse anodic stripping voltammetry and giving seriously overlapped responses has been used. Three regression techniques are compared: MLR, PLS and ANN. Good predictive and effective models are obtained. Through inspection of the reconstructed signals, identification and interpretation of significant regions in the voltammograms are possible.     

Wavelet unfolded partial least squares for near-infrared spectral quantitative analysis of blood and tobacco powder samples

Continuous wavelet transform (CWT) has been shown to be a high-performance signal processing technique in multivariate calibration. However, the signal processed by CWT with a specific wavelet may account for only a part of the information. To effectively utilize more abundant information contained in analytical signals, a method, named as wavelet unfolded partial least squares (WUPLS), was proposed. In the approach, the measured dataset is firstly extended by CWT with different wavelets, and then partial least squares (PLS) is employed to develop the quantitative model between the extended dataset and the target values. In order to select the representative wavelets, principal component analysis (PCA) is used to investigate the distribution of the signals obtained by CWT with different wavelets. The performance of the method was tested with blood and tobacco powder samples. Compared with the results obtained by PLS methods, the WUPLS method combined with signal processing techniques is proven to be a promising tool for improving the near-infrared (NIR) spectral analysis of complex samples.     

Elimination of interference information by a new hybrid algorithm for quantitative calibration of near infrared spectra

A new hybrid algorithm is proposed to eliminate the interference information for multivariate calibration of near-infrared (NIR) spectra that includes noise, background and systemic spectral variation irrelevant to concentration. The method consists of two parts: approximate derivative based on continuous wavelet transform (CWT) and orthogonal signal correction (OSC). After the approximate derivative calculated by CWT, OSC was performed. It was successfully applied to real complex NIR spectral data to eliminate the interference information. Correction for the interference of NIR spectra resulted in a substantial improvement in the predicted precision, and a more concise calibration model was obtained. The proposed procedure also compared favourably with several pretreatment methods, and the new method appears to provide a high-performance pretreatment tool for multivariate calibration of NIR spectra. In addition, the strategy proposed here can be applied to various other spectral data for quantitative purposes as well.     

Determination of nebivolol hydrochloride and hydrochlorothiazide in tablets by first-order derivative spectrophotometry and liquid chromatography

Two simple and accurate methods for analysis of nebivolol hydrochloride (NEB) and hydrochlorothiazide (HCTZ) in their combined dosage forms were developed using first-order derivative spectrophotometry and reversed-phase liquid chromatography (LC). NEB and HCTZ in their combined dosage forms (tablets) were quantified using first-derivative responses at 294.6 and 334.6 nm in the spectra of their solutions in methanol. The calibration curves were linear in the concentration range of 8-40 microg/mL for NEB and 10-60 microg/mL for HCTZ. LC analysis was performed on a Phenomenex Gemini C18 column (250 x 4.6 mm id, 5 microm particle size) in the isocratic mode with 0.05 M potassium dihydrogen phosphate-acetonitrile-methanol (30 + 20 + 50, v/v/v; pH 4) mobile phase at a flow rate of 1 mL/min. Detection was made at 220 nm. Both of the drugs and the internal standard (ezetimibe) were well resolved with retention times of 5.1 min for NEB, 2.9 min for HCTZ, and 8.2 min for ezetimibe. The calibration curves were linear in the concentration range of 1-14 microg/mL for NEB and 0.3-28 microg/mL for HCTZ. Both methods were validated and found to be accurate, precise, and specific, and results were compared statistically. Developed methods were successfully applied for the estimation of NEB and HCTZ in their combined dosage forms.