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A fast chemometrics approach to quantitative analysis of metformin hydrochloride, enalapril maleate, and captopril in tablets based on HPLC-PAD spectra

Muhire Jules, Li Bao Qiong, Zhai Hong Lin, Wang Xue, Xu Min Li

Acta Chromatographica

2019

Vol. 31, no. 3

228--234

Diabetes mellitus and concurrent hypertension disorder are dreadful all over the world and are often managed by some drugs, such as metformin hydrochloride (MFH), enalapril maleate (ENM), and captopril (CAP). In this work, a reliable and fast quantitative analysis of these three components in tablets was carried out by Tchebichef image moment method and multivariate curve resolution with alternating least squares on three-dimensional (3D) spectra obtained by high-performance liquid chromatography coupled with photodiode array detection (HPLC-PAD). 3D spectra were obtained within only 2 min, and linear quantitative models were established by stepwise regression based on the calculated image moments. Among these two methods, Tchebichef image moment method showed outcome distinction. The correlation coefficients of cross-validation (RLoo-cv) are more than 0.988, while their recoveries are 100.1 ± 1.7% (MFH), 95.4 ± 5.4% (ENM), and 105.3 ± 5.7% (CAP), respectively. The intra- and inter-day precisions (RSD) are less than 5.42%. The proposed methods were also applied to the analysis of real tablets. This study reveals the effectiveness and convenience of the proposed image-moment method that may be a potential technology for the quality control and investigation of drugs in routine analysis.

Efficient compression of bio-signals by using Tchebichef moments and Artificial Bee Colony

Hosny K. M., Khalid A. M., Mohamed E. R.

Biocybernetics and Biomedical Engineering

2018

Vol. 38, no. 2

385--398

In this paper, an algorithm is proposed for efficient compression of bio-signals based on discrete Tchebichef moments and Artificial Bee Colony (ABC). The Tchebichef moments are used to extract features of the bio-signals, then, the ABC algorithm is used to select of the optimum features which achieve the best bio-signal quality for a specific compression ratio (CR). The proposed algorithm has been tested by using different datasets of Electrocardio-gram (ECG), Electroencephalogram (EEG), and Electromyogram (EMG). The optimum feature selection using ABC significantly improve the quality of the reconstructed bio-signals. Different numerical experiments are performed to compress different records of ECG, EEG and EMG bio-signals by using the proposed algorithm and the most recent existing methods. The performance of the proposed algorithm and the other existing methods are evaluated using different metrics such as CR, PRD, and peak signal to noise ratio (PSNR). The comparison has shown that, at the same CR, the proposed compression algorithm yields the best quality of the reconstructed signals over the other existing methods.