scholarly journals The Application of Multiobjective Genetic Algorithm to the Parameter Optimization of Single-Well Potential Stochastic Resonance Algorithm Aimed at Simultaneous Determination of Multiple Weak Chromatographic Peaks

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Haishan Deng ◽  
Shaofei Xie ◽  
Bingren Xiang ◽  
Ying Zhan ◽  
Wei Li ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simultaneous Determination ◽  
Stochastic Resonance ◽  
Peak Shape ◽  
Sudan Dyes ◽  
Data Set ◽  
Multiobjective Genetic Algorithm ◽  
Single Well ◽  
Chromatographic Peaks

Simultaneous determination of multiple weak chromatographic peaks via stochastic resonance algorithm attracts much attention in recent years. However, the optimization of the parameters is complicated and time consuming, although the single-well potential stochastic resonance algorithm (SSRA) has already reduced the number of parameters to only one and simplified the process significantly. Even worse, it is often difficult to keep amplified peaks with beautiful peak shape. Therefore, multiobjective genetic algorithm was employed to optimize the parameter of SSRA for multiple optimization objectives (i.e.,S/Nand peak shape) and multiple chromatographic peaks. The applicability of the proposed method was evaluated with an experimental data set of Sudan dyes, and the results showed an excellent quantitative relationship between different concentrations and responses.

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.

scholarly journals Application of a catalytic oxidation method for the simultaneous determination of total organic carbon and total nitrogen in marine sediments and soils

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252308
Author(s):  
Pavlos Avramidis ◽  
Vlasoula Bekiari
Keyword(s):  
Organic Carbon ◽  
Total Organic Carbon ◽  
Simultaneous Determination ◽  
Catalytic Oxidation ◽  
Correlation Coefficient ◽  
Marine Sediments ◽  
Titration Method ◽  
Data Set ◽  
Oxidation Method

This study presents the application of a simultaneous method for the determination of total organic carbon (TOC) and total nitrogen (TN) in marine sediments and soils, using a data set of 206 samples collected from coastal lagoonal/marine sedimentary environments and certified reference materials (CRMs). TOC and TN were determined using the high temperature (720°C) catalytic (Pt/Al2O3) oxidation method and the detection of TOC and TN was performed using an infrared or a chemiluminescence detector, respectively. Results from the abovementioned TOC method were compared with the results from the widely used Wakley-Black titration method, while TN results with these from elemental analysis. Analytical quality control experiments were performed using CRM samples. Method characteristics such as range of measurement, calibration, method detection limit (MDL), limit of quantification (LOQ), repeatability and reproducibility, were calculated. The precision and the accuracy of the methods are also discussed. Comparison of the two TOC methods of 206 data set yields a regression line of correlation coefficient with R2 = 0.985. Additional different levels of TOC concentrations of low <1%, moderate 1–5% and high 5–40% level were examined indicating very good correlations. The lowest correlation coefficient was observed in low concentrations TOC<1% (R2 0.825), mainly as a result of the limitation of titration method. The evaluation of TN results indicated that the catalytic oxidation method and the elemental analysishave a significant good correlation with R2 = 0.977. The results of precision and accuracy, as well as the calculated MDL and LOQ show that this is a reliable method. Moreover, it requires a small amount of the analyzed sample and the total analysis time is 10 min. Therefore, it can be easily applied for the fast and precise simultaneous determination of TOC and TN in sediment and soil samples.

Fast converging elitist genetic algorithm for knot adjustment in B-spline curve approximation

2019 ◽  
Vol 13 (4) ◽  
pp. 317-328
Author(s):  
Johannes Bureick ◽  
Hamza Alkhatib ◽  
Ingo Neumann
Keyword(s):  
Genetic Algorithm ◽  
Real Data ◽  
Adjustment Problem ◽  
Data Set ◽  
New Approach ◽  
B Spline ◽  
Spline Curve ◽  
Curve Approximation ◽  
Data Gaps

Abstract B-spline curve approximation is a crucial task in many applications and disciplines. The most challenging part of B-spline curve approximation is the determination of a suitable knot vector. The finding of a solution for this multimodal and multivariate continuous nonlinear optimization problem, known as knot adjustment problem, gets even more complicated when data gaps occur. We present a new approach in this paper called an elitist genetic algorithm, which solves the knot adjustment problem in a faster and more precise manner than existing approaches. We demonstrate the performance of our elitist genetic algorithm by applying it to two challenging test functions and a real data set. We demonstrate that our algorithm is more efficient and robust against data gaps than existing approaches.

Determination of Optimal Clusters Using a Genetic Algorithm

2008 ◽  
pp. 98-117 ◽  
Author(s):  
Tushar ◽  
Tushar ◽  
Shibendu Shekhar Roy ◽  
Dilip Kumar Pratihar
Keyword(s):  
Genetic Algorithm ◽  
Threshold Value ◽  
Data Sets ◽  
Self Organizing Map ◽  
Data Set ◽  
Fcm Algorithm ◽  
Data Points ◽  
The Relationship

Clustering is a potential tool of data mining. A clustering method analyzes the pattern of a data set and groups the data into several clusters based on the similarity among themselves. Clusters may be either crisp or fuzzy in nature. The present chapter deals with clustering of some data sets using Fuzzy C-Means (FCM) algorithm and Entropy-based Fuzzy Clustering (EFC) algorithm. In FCM algorithm, the nature and quality of clusters depend on the pre-defined number of clusters, level of cluster fuzziness and a threshold value utilized for obtaining the number of outliers (if any). On the other hand, the quality of clusters obtained by the EFC algorithm is dependent on a constant used to establish the relationship between the distance and similarity of two data points, a threshold value of similarity and another threshold value used for determining the number of outliers. The clusters should ideally be distinct and at the same time compact in nature. Moreover, the number of outliers should be as minimum as possible. Thus, the above problem may be posed as an optimization problem, which will be solved using a Genetic Algorithm (GA). The best set of multi-dimensional clusters will be mapped into 2-D for visualization using a Self-Organizing Map (SOM).

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