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

A Bayesian Regularized Artificial Neural Network for Simultaneous Determination of Loratadine, Naproxen and Diclofenac in Wastewaters

Background::

Simultaneous determination of medication components in pharmaceutical samples using ordinary methods have some difficulties and therefore these determinations usually were made by expensive methods and instruments. Chemometric methods are an effective way to analyze several components simultaneously.

Objective::

In this paper, a novel approach based on Bayesian regularized artificial neural network is developed for the determination of Loratadine, Naproxen, and Diclofenac in water using UV-Vis spectroscopy.

Methods:

A dataset is collected by performing several chemical experiments and recording the UV-Vis spectra and actual constituent values. The effect of a different number of neurons in the hidden layer was analyzed based on final mean square error, and the optimum number was selected. Principle Component Analysis (PCA) was also applied to the data. Other back-propagation methods, such as Levenberg-Marquardt, scaled conjugate gradient, and resilient backpropagation, were tested.

Results::

In order to see the proposed network performance, it was performed on two crossvalidation methods, namely partitioning data into train and test parts, and leave-one-out technique. Mean square errors between expected results and predicted ones implied that the proposed method has a strong ability in predicting the expected values.

Conclusion::

he results showed that the Bayesian regularization algorithm has the best performance among other methods for simultaneous determination of Loratadine, Naproxen, and Diclofenac in water samples.

Wavelet Transform-Based UV Spectroscopy for Pharmaceutical Analysis

In research and development laboratories, chemical or pharmaceutical analysis has been carried out by evaluating sample signals obtained from instruments. However, the qualitative and quantitative determination based on raw signals may not be always possible due to sample complexity. In such cases, there is a need for powerful signal processing methodologies that can effectively process raw signals to get correct results. Wavelet transform is one of the most indispensable and popular signal processing methods currently used for noise removal, background correction, differentiation, data smoothing and filtering, data compression and separation of overlapping signals etc. This review article describes the theoretical aspects of wavelet transform (i.e., discrete, continuous and fractional) and its characteristic applications in UV spectroscopic analysis of pharmaceuticals.

Application of wavelet and Fuorier transforms as powerful alternatives for derivative spectrophotometry in analysis of binary mixtures: A comparative study

Two signal processing methods, namely, Continuous Wavelet Transform (CWT) and the second was Discrete Fourier Transform (DFT) were introduced as alternatives to the classical Derivative Spectrophotometry (DS) in analysis of binary mixtures. To show the advantages of these methods, a comparative study was performed on a binary mixture of Naltrexone (NTX) and Bupropion (BUP). The methods were compared by analyzing laboratory prepared mixtures of the two drugs. By comparing performance of the three methods, it was proved that CWT and DFT methods are more efficient and advantageous in analysis of mixtures with overlapped spectra than DS. The three signal processing methods were adopted for the quantification of NTX and BUP in pure and tablet forms. The adopted methods were validated according to the ICH guideline where accuracy, precision and specificity were found to be within appropriate limits.

Evaluation of the efficiency of continuous wavelet transform as processing and preprocessing algorithm for resolution of overlapped signals in univariate and multivariate regression analyses; an application to ternary and quaternary mixtures

Wavelets have been adapted for a vast number of signal-processing applications due to the amount of information that can be extracted from a signal. In this work, a comparative study on the efficiency of continuous wavelet transform (CWT) as a signal processing tool in univariate regression and a pre-processing tool in multivariate analysis using partial least square (CWT-PLS) was conducted. These were applied to complex spectral signals of ternary and quaternary mixtures. CWT-PLS method succeeded in the simultaneous determination of a quaternary mixture of drotaverine (DRO), caffeine (CAF), paracetamol (PAR) and p-aminophenol (PAP, the major impurity of paracetamol). While, the univariate CWT failed to simultaneously determine the quaternary mixture components and was able to determine only PAR and PAP, the ternary mixtures of DRO, CAF, and PAR and CAF, PAR, and PAP. During the calculations of CWT, different wavelet families were tested. The univariate CWT method was validated according to the ICH guidelines. While for the development of the CWT-PLS model a calibration set was prepared by means of an orthogonal experimental design and their absorption spectra were recorded and processed by CWT. The CWT-PLS model was constructed by regression between the wavelet coefficients and concentration matrices and validation was performed by both cross validation and external validation sets. Both methods were successfully applied for determination of the studied drugs in pharmaceutical formulations.

The application of continuous wavelet transform and least squares support vector machine for the simultaneous quantitative spectrophotometric determination of Myricetin, Kaempferol and Quercetin as flavonoids in pharmaceutical plants

Flavonoids are γ-benzopyrone derivatives, which are highly regarded in these researchers for their antioxidant property. In this study, two new signals processing methods been coupled with UV spectroscopy for spectral resolution and simultaneous quantitative determination of Myricetin, Kaempferol and Quercetin as flavonoids in Laurel, St. John's Wort and Green Tea without the need for any previous separation procedure. The developed methods are continuous wavelet transform (CWT) and least squares support vector machine (LS-SVM) methods integrated with UV spectroscopy individually. Different wavelet families were tested by CWT method and finally the Daubechies wavelet family (Db4) for Myricetin and the Gaussian wavelet families for Kaempferol (Gaus3) and Quercetin (Gaus7) were selected and applied for simultaneous analysis under the optimal conditions. The LS-SVM was applied to build the flavonoids prediction model based on absorption spectra. The root mean square errors for prediction (RMSEP) of Myricetin, Kaempferol and Quercetin were 0.0552, 0.0275 and 0.0374, respectively. The developed methods were validated by the analysis of the various synthetic mixtures associated with a well- known flavonoid contents. Mean recovery values of Myricetin, Kaempferol and Quercetin, in CWT method were 100.123, 100.253, 100.439 and in LS-SVM method were 99.94, 99.81 and 99.682, respectively. The results achieved by analyzing the real samples from the CWT and LS-SVM methods were compared to the HPLC reference method and the results were very close to the reference method. Meanwhile, the obtained results of the one-way ANOVA (analysis of variance) test revealed that there was no significant difference between the suggested methods.

Continuous Wavelet Transform, a powerful alternative to Derivative Spectrophotometry in analysis of binary and ternary mixtures: A comparative study

A comparative study was established between two signal processing techniques showing the theoretical algorithm for each method and making a comparison between them to indicate the advantages and limitations. The methods under study are Numerical Differentiation (ND) and Continuous Wavelet Transform (CWT). These methods were studied as spectrophotometric resolution tools for simultaneous analysis of binary and ternary mixtures. To present the comparison, the two methods were applied for the resolution of Bisoprolol (BIS) and Hydrochlorothiazide (HCT) in their binary mixture and for the analysis of Amlodipine (AML), Aliskiren (ALI) and Hydrochlorothiazide (HCT) as an example for ternary mixtures. By comparing the results in laboratory prepared mixtures, it was proven that CWT technique is more efficient and advantageous in analysis of mixtures with severe overlapped spectra than ND. The CWT was applied for quantitative determination of the drugs in their pharmaceutical formulations and validated according to the ICH guidelines where accuracy, precision, repeatability and robustness were found to be within the acceptable limit.

Different signal processing techniques of ratio spectra for spectrophotometric resolution of binary mixture of bisoprolol and hydrochlorothiazide; a comparative study

Five signal processing techniques were applied to ratio spectra for quantitative determination of bisoprolol (BIS) and hydrochlorothiazide (HCT) in their binary mixture. The proposed techniques are Numerical Differentiation of Ratio Spectra (ND-RS), Savitsky-Golay of Ratio Spectra (SG-RS), Continuous Wavelet Transform of Ratio Spectra (CWT-RS), Mean Centering of Ratio Spectra (MC-RS) and Discrete Fourier Transform of Ratio Spectra (DFT-RS). The linearity of the proposed methods was investigated in the range of 2-40 and 1-22 μg/mL for BIS and HCT, respectively. The proposed methods were applied successfully for the determination of the drugs in laboratory prepared mixtures and in commercial pharmaceutical preparations and standard deviation was less than 1.5. The five signal processing techniques were compared to each other and validated according to the ICH guidelines and accuracy, precision, repeatability and robustness were found to be within the acceptable limit.

Deviations from bilinearity in multivariate voltammetric calibration models

This work considers the problem of lack of bilinearity in multivariate calibration. In voltammetry this issue especially relies on the analysis of overlapping signals, which change the shape, sensitivity or shift along the potential axis, causing a significant loss of linearity. It limits the quality of many chemometric models designed for linear data. Improvement of the predictive ability of multivariate calibration models is achieved by pre-processing of the raw data. In this work we proposed the application of a technique called orthogonal signal correction (OSC). We demonstrated that orthogonal correction enables the removal of almost all non-linear effects, disturbing voltammetric signals that impede the building of effective PLS models. The methodology was presented using simulated signals, and also in determination of the nanomolar concentration of scandium in the presence of a high and changing excess of nickel.

UV spectrophotometric simultaneous determination of cefoperazone and sulbactam in pharmaceutical formulations by derivative, Fourier and wavelet transforms

Signal processing methods based on the use of derivative, Fourier and wavelet transforms were proposed for the spectrophotometric simultaneous determination of cefoperazone and sulbactam in powders for injection. These transforms were successfully applied to UV spectra and ratio spectra to find suitable working wavelengths. Wavelet signal processing was proved to have distinct advantages (i.e. higher peak intensity obtained, additional smooth function and scaling factor process eliminated) over derivative and Fourier transforms. Especially, a better resolution of spectral overlapping bands was obtained by the use of double signal transform in the sequences such as (i) spectra pre-processed by Fractional Wavelet Transform and subsequently subjected to Continuous Wavelet Transform or Discrete Wavelet Transform, and (ii) derivative - wavelet transforms combined. Calibration graphs for cefoperazone and sulbactam were recorded for the range 10-35 mg/L. Good accuracy and precision were reported for all proposed methods by analyzing synthetic mixtures of cefoperazone and sulbactam. Furthermore, these methods were statistically comparable to RP-HPLC.