Factors affecting the performance of polyvinyl chloride (PVC) based ISE were recently the scope of study by several researchers; however, no enough data has related to the thickness of the PVC membrane to performance characteristics and sensitivity of sensors. The current work introduces an ecofriendly experimental approach to evaluate the effect of membrane composition including ion exchanger and plasticizer along with thickness on the performance of a PVC sensor for determination of a tocolytic selective ß 2 -agonist; ritodrine HCl (RTH), in presence of its active impurity, tyramine. Thirteen different PVC membranes were prepared by varying the content of ion exchanger types and amounts constructing different thickness to address their effect on their performance characteristics. A comparative study was held among the fabricated sensors for determination of RTH in pharmaceutical dosage form, and biological fluids including human plasma and urine. It was found that the thickness of liquid membrane electrodes had a remarkable effect on the sensitivity of PVC based ion-selective electrode. The proposed sensors proved acceptable selectivity for RTH against tyramine and common inorganic species. The proposed technique presents a green alternative to reported ones by saving cost, time and effort, and yielding of the minimum amount of wastes.
Following the sudden widespread of the novel coronavirus (COVID-19) which first appeared in Wuhan city. Remdesivir (REM) was the first medicine licensed by the US Food and Drug Administration (FDA) for COVID-19 infected hospitalized patients.Hence, there was an urgent demand for the optimization of efficient selective and sensitive methods to be developed for the determination of REM in pharmaceuticals as well as biological samples. A sensitive and simple green spectrofluorimetric
Quantitative multicomponent analysis is considered an analytical goal to save time and cost in analysis. Hence, this work aimed to provide sensitive and selective UV-spectrophotometric, chemometric manipulation, and ultra-performance LC (UPLC) methods for the determination of well-known coformulated antiemetics used in pregnancy, namely pyridoxine HCl (PYR), meclozine HCl, and cyclizine. The developed UV-spectrophotometric methods are dual wavelength in ratio spectra and first derivative of the ratio spectra with which PYR was determined selectively at 290.8 nm, whereas the other drugs in a ternary mixture were determined from their ratio spectra using a spectrum of PYR as a divisor in 0.1 M HCl. An ecofriendly partial least-squares regression chemometric method was applied to raw UV absorbance data for the determination of the ternary mixture in a 218-355 nm range using a three-factor, three-level design with water as the green solvent. A gradient UPLC method was developed and successfully resolved the ternary mixture within 5 min. Different ratios of water (adjusted to pH 3 with phosphoric acid) and methanol were delivered at 0.5 mL/min as the mobile phase into a Hypersil Gold C18 column (50 × 2.1 mm, 1.9 µm). The developed methods were successfully applied to different pharmaceutical formulations containing the aforementioned drugs and validated according to the International Conference on Harmonization guidelines. The results obtained were reproducible and reliable and can be applied for routine analysis and QC in laboratories.
Background
Noising is an undesirable phenomenon accompanying with development of the widely-used chemometric models such as partial least square regression (PLSR) and support vector regression (SVR).
Objective
Optimizations of these chemometric models by applying Orthogonal projection to latent structures (OPLS) as a preprocessing step which characterized by cancelling noise is the purpose of the presented research study. Additionally a comprehensive comparative study between the developed methods was achieved highlighting pros and cons.
Methods
OPLS conducted with PLSR and SVR for quantitative determination of Pyridoxine HCl (PYR), Cyclizine HCl (CYC), and Meclizine HCl (MEC) in presence of their related impurities. Training set was formed from twenty-five mixtures, as there are five mixtures for each compound at each concentration level. Additionally, to check the validity and predictive ability of the developed chemometric models, the independent test set mixtures were prepared by repeating the preparation of four mixtures of the training set plus preparation of another four independent mixtures.
Results
Upon application of OPLS processing method, upswing of predictive abilities of PLSR and SVR was found. The root mean square error of prediction (RMSEP) of the test set was the basic benchmark for comparison.
Conclusion
The major finding that was concluded from the conducted research is that processing with OPLS reinforces the ability of models to anticipate the future samples.
Highlights
Novel optimizations of the widely-used chemometric models; application of comparative study between the suggested methods; application of OPLS pre-processing methods; quantitative determination of pyridoxine HCl, cyclizine HCl and meclizine HCl; checking the predictive power of developed chemometric models; analysis of the active ingredients in their pharmaceutical dosage forms.
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