From Isocratic Data to a Gradient Elution Retention Model in IC: An Artificial Neural Network Approach
| Autor: | Tomislav Bolanča, Melita Luša, Šime Ukić, Marko Rogošić, Štefica Cerjan Stefanović |
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| Rok vydání: | 2009 |
| Předmět: |
Chromatography
Artificial neural network Elution Organic Chemistry Clinical Biochemistry Chlorate Ion chromatography Analytical chemistry Biochemistry Chloride Analytical Chemistry Ion Chemometrics chemistry.chemical_compound chemistry ion chromatography retention modeling artificial neural network medicine Sulfate medicine.drug |
| Zdroj: | Chromatographia. 70:15-20 |
| ISSN: | 1612-1112 0009-5893 |
| DOI: | 10.1365/s10337-009-1126-8 |
| Popis: | Gradient elution is used in ion chromatography to achieve rapid analysis with reasonable separation. Optimization and prediction of the gradient is clearly a multidimensional problem, however. One approach to prediction of gradient retention behavior is based on isocratic experimentation. In this work a gradient model for simultaneous prediction of the retention behavior of fluoride, chlorite, chloride, chlorate, nitrate, and sulfate ions, on the basis of isocratic experimental data, is proposed. An artificial neural network was used to predict isocratic results ; the network was optimized with regard to the number of data in the training set (25) and number of neurons in the hidden layer (6). A slight systematic error was observed in the isocratic prediction, but this did not effect gradient prediction. Good predictions were achieved for all the anions investigated (average error 1.79%). Deviations were somewhat higher for prediction of sulfate retention than for the other anions, probably because of the higher charge and larger size of sulfate in comparison with the other ions examined. |
| Databáze: | OpenAIRE |
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