M. Eid

Simultaneous determination of methocarbamol and aspirin by RP-HPLC using fluorescence detection with time programming: its application to pharmaceutical dosage form

A new simple, rapid and sensitive reversed-phase liquid chromatographic method was developed and validated for the simultaneous determination of methocarbamol (MET) and aspirin (ASP) in their combined dosage form. The separation of these compounds was achieved within 6.0 min on a CLC Shim-pack C8 column (250 × 4.6 mm, 5 µm particle size) using isocratic mobile phase consisting of acetonitrile and 0.02 M dihydrogenphosphate buffer (30:70, v/v) at pH = 5.0. The analysis was performed at a flow rate of 1.0 mL/min with fluorescence detection at 277/313 nm for MET and 298/410 nm for ASP using real-time programming. The selectivity, linearity of calibration, accuracy, inter- and intra-day precision and recovery were examined as parts of the method validation. The concentration-response relationship was linear over concentration ranges of 0.02-0.20 and 0.02-0.40 µg/mL for MET and ASP, respectively, with a limit of detection of 6 and 32 ng/mL for MET and ASP, respectively. The proposed method was successfully applied for the analysis of both MET and ASP in prepared tablets with average recoveries of 99.88 ± 0.65% for MET and 100.44 ± 0.78% for ASP. The results were favourably compared to those obtained by a reference method.

Development and Validation of RP- HPLC Method for Simultaneous Determination of Ascorbic Acid and Salicylamide in their Binary Mixtures: Application to Combined Tablets

A new simple, rapid and sensitive reversed-phase liquid chromatographic method was developed and validated for the simultaneous determination of ascorbic acid (ASC) and salicylamide (SAL) in their combined dosage form. The analysis was carried out on CLC Shim-pack C8 column (250 x 4.6 mm, 5 μm particle size) using a mobile phase consisting of methanol: 0.03 M phosphate buffer mixture (55: 45, v/v) of pH 4.0. The mobile phase was pumped at a flow rate of 1 mL/min with ultraviolet detection at 255 nm. The selectivity, linearity of calibration, accuracy, intra and inter day precision and recovery were examined as parts of the method validation. The concentration– response relationship was linear over a concentration range of 0.50-10.00 and 5.00-50.00 μg/mL for ASC and SAL, respectively with limits of detection of 0.048 and 0.676 μg/mL. The proposed method was applied for the simultaneous determination of the two studied drugs in their combined tablets with average recoveries of 100.04 ± 0.75% and 100.11 ± 1.04% for ASC and SAL, respectively. The results were favorably compared to those obtained by the comparison methods.

Simultaneous determination of methocarbamol and aspirin binary mixture in their combined tablets by derivative and ratio derivative spectrophotometry

Two simple and rapid spectrophotometric methods were developed for the simultaneous determination of methocarbamol (MET) and aspirin (ASP) in their combined dosage form. The first method, zero-crossing first derivative spectrophotometry, is based on the measurement of the first derivative amplitude values at 279.2 nm and 246.6 nm for the simultaneous determination of MET and ASP, respectively. The second method, ratio first derivative spectrophotometry, is based on the measurement of the peak amplitudes of the first derivative of the ratio spectra at 284 nm for MET and at 238.2 and 246 nm for ASP determination. The calibration graphs were linear over the range of 2.0–30.0 μg mL−1 for MET and ASP by the two proposed methods. The detection limits of the first derivative method were found to be 0.2998 and 0.3227 μg mL−1 and the quantitation limits were 0.9084 and 0.9780 μg mL−1 for MET and ASP, respectively. For the ratio first derivative method, the limits of detection and quantitation were 0.2795 and 0.8470 μg mL−1 for MET, respectively, while for ASP the limits of detection and quantitation were 0.2485 and 0.7530 μg mL−1 at 238.2 nm and 0.4173 and 1.2647 μg mL−1 at 246 nm. The proposed methods were applied successfully in the assay of the studied drugs in their combined tablets. The results compared favorably to those obtained by the reference methods.

SIMULTANEOUS DETERMINATION OF METHOCARBAMOL AND IBUPROFEN IN THEIR BINARY MIXTURES USING HPLC METHOD WITH FLUORESCENCE DETECTION: APPLICATION TO COMBINED TABLETS

A new simple, rapid, and sensitive reversed-phase liquid chromatographic method was developed and validated for the simultaneous determination of methocarbamol (MET) and ibuprofen (IBU) in their combined dosage form. The separation of these compounds was achieved within 7.0 min on a CLC Shim-pack C8 column (250 × 4.6 mm, 5-µm particle size) using isocratic mobile phase containing a mixture of methanol and 0.05 M dihydrogen phosphate buffer (75:25, v/v) at pH 6.5. The analysis was performed at a flow rate of 1.0 mL/min with fluorescence-detection at 295 nm after excitation at 224 nm. The selectivity, linearity of calibration, accuracy, intra- and inter-day precision and recovery were examined as parts of the method validation. The concentration–response relationship was linear over a concentration range of 0.05–0.50 µg/mL for MET and 0.03–0.50 µg/mL for IBU with a limit of detection 3.8 ng/mL and 1.6 ng/mL for MET and IBU, respectively. The proposed method was applied for the simultaneous determination of the two studied drugs in their combined tablets with average recoveries of 100.75 ± 1.01% and 99.25 ± 1.16% for MET and IBU, respectively. The results were favorably compared to those obtained by the USP reference methods.

An efficient algorithm in extracting human iris Morphological features

The interface of computer technologies and biology is having a huge impact on society. Human recognition research projects promises new life to many security-consulting firms and personal identification system manufacturers. Iris recognition is considered to be the most reliable biometric authentication system. Very few iris recognition algorithms were commercialized. The method proposed in this paper differed from the existing work in the iris segmentation and feature extraction phase. Digitized grayscale images of Chinese Academy of Sciences—Institute of Automation (CASIA) database were used for determining the performance of the proposed system. The circular iris and pupil of the eye image were segmented using Morphological operators and Hough transform. The localized iris region was then normalized in to a rectangular block to account for imaging inconsistencies. This method provides accurate features as well as simple and fast iris analysis methods.

Automated algorithm for iris detection and code generation

The interface of computer technologies and biology is having a huge impact on society. Human recognition research projects promises new life to many security-consulting firms and personal identification system manufacturers. Iris recognition is considered to be the most reliable biometric authentication system. Very few iris recognition algorithms were commercialized. The method proposed in this paper differed from the existing work in the iris segmentation and feature extraction phase. Digitized grayscale images of Chinese Academy of Sciences-Institute of Automation (CASIA) database were used for determining the performance of the proposed system. The circular iris and pupil of the eye image were segmented using morphological operators as well as circular Hough transform. The localized iris region was then normalized into a rectangular block to account for imaging inconsistencies. Finally, the corresponding iris code was generated using the Euclidean distance. This method provides accurate signature as well as accurate code of the human iris in a simple and fast way.

STABILITY-INDICATING HPLC METHOD WITH FLUORESCENCE DETECTION FOR DETERMINATION OF METHOCARBAMOL IN TABLETS. APPLICATION TO THERAPEUTIC DRUG MONITORING

A simple and highly sensitive stability-indicating HPLC method was developed and validated for the determination of the muscle relaxant, methocarbamol (MTC), in presence of its degradation product, guaifenesin (GFS). The analyses were carried out on a Nucleosil 100-5 phenyl column (4.6 × 250 mm, 5 µm particle size), using a mobile phase consisting of 0.02 M orthophosphoric acid/ acetonitrile mixture (80:20, v/v) of pH 5.0 at a flow rate of 1 mL/min with fluorescence detection at 277/313 nm. The method showed good linearity for MTC over the concentration range of 0.02–0.50 µg/mL with lower detection limit of 4.5 ng/mL and lower quantification limit of 14.0 ng/mL. The proposed method allowed the detection of GFS in MTC drug substance, as a potential impurity and degradation product, within the pharmacopeial limit specified by the USP. The proposed method was successfully applied for the determination of MTC in different pharmaceutical formulations without interference from coformulated drugs. Statistical comparison of the results obtained with those of the official method revealed no significance differences in the performance of the two methods regarding accuracy and precision. Moreover, the proposed method was applied for the therapeutic drug monitoring of MTC in human plasma.

Automated face recogntion system: Multi-input databases

There has been significant progress in improving the performance of computer-based face recognition algorithms over the last decade. Although algorithms have been tested and compared extensively with each other, there has been remarkably little work comparing the accuracy of computer-based human face recognition systems. We compared eight state-of-the-art face recognition algorithms with three different databases: (i) faces 94; (ii) Olivetti research lab (ORL), and (iii) Indian face database (IFD). The face detection phase had been performed using the morphological features. The recognition results had showed that in linear appearance based classifier; LDA performs better than ICA and PCA in terms of the accuracy of recognition. The computational overhead of LDA and the PCA are almost similar while ICA has a very long execution time. In addition, neural network based on DWT features perform better than classifiers based on other features with 99% recognition rate on the average.