Betamethasone-based chiral electrochemical sensor coupled to chemometric methods for determination of mandelic acid enantiomers

2017 ◽  
Vol 30 (12) ◽  
pp. e2653 ◽  
Author(s):  
Marjan Borazjani ◽  
Ali Mehdinia ◽  
Ali Jabbari
Keyword(s):  
Electrochemical Sensor ◽  
Mandelic Acid ◽  
Chemometric Methods ◽  
Mandelic Acid Enantiomers

Graphene Modified Molecular Imprinting Electrochemical Sensor for Determining the Content of Dopamine

2019 ◽  
Vol 15 (6) ◽  
pp. 628-634
Author(s):  
Rong Liu ◽  
Jie Li ◽  
Tongsheng Zhong ◽  
Liping Long
Keyword(s):  
Electrochemical Sensor ◽  
Molecular Imprinting ◽  
Neurological Disorders ◽  
Room Temperature ◽  
Low Cost ◽  
Differential Pulse ◽  
Current Response ◽  
Specific Selectivity ◽  
Pulse Voltammetry

Background: The unnatural levels of dopamine (DA) result in serious neurological disorders such as Parkinson’s disease. Electrochemical methods which have the obvious advantages of simple operation and low-cost instrumentation were widely used for determination of DA. In order to improve the measurement performance of the electrochemical sensor, molecular imprinting technique and graphene have always been employed to increase the selectivity and sensitivity. Methods: An electrochemical sensor which has specific selectivity to (DA) was proposed based on the combination of a molecular imprinting polymer (MIP) with a graphene (GR) modified gold electrode. The performance and effect of MIP film were investigated by differential pulse voltammetry (DPV) and cyclic voltammetry (CV) in the solution of 5.0 ×10-3 mol/L K3[Fe(CN)6] and K4[Fe(CN)6] with 0.2 mol/L KCl at room temperature. Results: This fabricated sensor has well repeatability and stability, and was used to determine the dopamine of urine. Under the optimized experiment conditions, the current response of the imprinted sensor was linear to the concentration of dopamine in the range of 1.0×10-7 ~ 1.0×10-5 mol/L, the linear equation was I (µA) = 7.9824+2.7210lgc (mol/L) with the detection limit of 3.3×10-8 mol/L. Conclusion: In this work, a highly efficient sensor for determination of DA was prepared with good sensitivity by GR and great selectivity of high special recognization ability by molecular imprinting membrane. This proposed sensor was used to determine the dopamine in human urine successfully.

scholarly journals Full spectrum and genetic algorithm-selected spectrum-based chemometric methods for simultaneous determination of azilsartan medoxomil, chlorthalidone, and azilsartan: Development, validation, and application on commercial dosage form

2021 ◽  
Vol 19 (1) ◽  
pp. 205-213
Author(s):  
Hany W. Darwish ◽  
Abdulrahman A. Al Majed ◽  
Ibrahim A. Al-Suwaidan ◽  
Ibrahim A. Darwish ◽  
Ahmed H. Bakheit ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simultaneous Determination ◽  
Predictive Power ◽  
Principal Component Regression ◽  
Selection Procedure ◽  
Principal Component ◽  
Chemometric Methods ◽  
Full Spectrum ◽  
Azilsartan Medoxomil

Abstract Five various chemometric methods were established for the simultaneous determination of azilsartan medoxomil (AZM) and chlorthalidone in the presence of azilsartan which is the core impurity of AZM. The full spectrum-based chemometric techniques, namely partial least squares (PLS), principal component regression, and artificial neural networks (ANN), were among the applied methods. Besides, the ANN and PLS were the other two methods that were extended by genetic algorithm procedure (GA-PLS and GA-ANN) as a wavelength selection procedure. The models were developed by applying a multilevel multifactor experimental design. The predictive power of the suggested models was evaluated through a validation set containing nine mixtures with different ratios of the three analytes. For the analysis of Edarbyclor® tablets, all the proposed procedures were applied and the best results were achieved in the case of ANN, GA-ANN, and GA-PLS methods. The findings of the three methods were revealed as the quantitative tool for the analysis of the three components without any intrusion from the co-formulated excipient and without prior separation procedures. Moreover, the GA impact on strengthening the predictive power of ANN- and PLS-based models was also highlighted.

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