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Pregled bibliografske jedinice broj: 205343

Application of artificial neural networks for gradient elution retention modelling in ion chromatography


Bolanča, Tomislav; Cerjan-Stefanović, Štefica; Regelja, Melita; Regelja, Hrvoje; Lončarić, Sven
Application of artificial neural networks for gradient elution retention modelling in ion chromatography // Journal of separation science, 28 (2005), 1427-1433 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 205343 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Application of artificial neural networks for gradient elution retention modelling in ion chromatography

Autori
Bolanča, Tomislav ; Cerjan-Stefanović, Štefica ; Regelja, Melita ; Regelja, Hrvoje ; Lončarić, Sven

Izvornik
Journal of separation science (1615-9306) 28 (2005); 1427-1433

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
gradient elution ; artificial neural networks ; retention modelling ; inorganic anions ; ion chromatography

Sažetak
Gradient elution in ion chromatography offers several advantages: total analysis time can be significantly reduced, overall resolution of a mixture can be increased, peak shape can be improved (less tailing) and effective sensitivity can be increased (because there is little variation in peak shape). More importantly, it provides the maximum resolution per unit time. The aim of this work is the development of a suitable artificial neural network gradient elution retention model that can be used in a variety of applications for method development and retention modeling of inorganic anions in ion chromatography. Multi-layer perceptron artificial neural networks were used to model the retention behavior of fluoride, chloride, nitrite, sulphate, bromide, nitrate and phosphate in relation to starting time of gradient elution and slope of linear gradient elution curve. The advantage of the developed model is the application of an optimized two-phase training algorithm that enables the researcher to make use of the advantages of first- and second-order training algorithms in one training procedure. This results in better predictive ability, with less time required for the calculations. The number of hidden layer neurons and experimental data points used for the training set were optimized in terms of obtaining a precise and accurate retention model with respect to minimization of unnecessary experimentation and time needed for the calculation procedures. This study shows that developed artificial neural networks are the method of first choice for retention modelling of inorganic anions in ion chromatography.

Izvorni jezik
Engleski

Znanstvena područja
Kemija



POVEZANOST RADA


Projekti:
0125016

Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb,
Fakultet kemijskog inženjerstva i tehnologije, Zagreb


Citiraj ovu publikaciju:

Bolanča, Tomislav; Cerjan-Stefanović, Štefica; Regelja, Melita; Regelja, Hrvoje; Lončarić, Sven
Application of artificial neural networks for gradient elution retention modelling in ion chromatography // Journal of separation science, 28 (2005), 1427-1433 (međunarodna recenzija, članak, znanstveni)
Bolanča, T., Cerjan-Stefanović, Š., Regelja, M., Regelja, H. & Lončarić, S. (2005) Application of artificial neural networks for gradient elution retention modelling in ion chromatography. Journal of separation science, 28, 1427-1433.
@article{article, author = {Bolan\v{c}a, Tomislav and Cerjan-Stefanovi\'{c}, \v{S}tefica and Regelja, Melita and Regelja, Hrvoje and Lon\v{c}ari\'{c}, Sven}, year = {2005}, pages = {1427-1433}, keywords = {gradient elution, artificial neural networks, retention modelling, inorganic anions, ion chromatography}, journal = {Journal of separation science}, volume = {28}, issn = {1615-9306}, title = {Application of artificial neural networks for gradient elution retention modelling in ion chromatography}, keyword = {gradient elution, artificial neural networks, retention modelling, inorganic anions, ion chromatography} }
@article{article, author = {Bolan\v{c}a, Tomislav and Cerjan-Stefanovi\'{c}, \v{S}tefica and Regelja, Melita and Regelja, Hrvoje and Lon\v{c}ari\'{c}, Sven}, year = {2005}, pages = {1427-1433}, keywords = {gradient elution, artificial neural networks, retention modelling, inorganic anions, ion chromatography}, journal = {Journal of separation science}, volume = {28}, issn = {1615-9306}, title = {Application of artificial neural networks for gradient elution retention modelling in ion chromatography}, keyword = {gradient elution, artificial neural networks, retention modelling, inorganic anions, ion chromatography} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus
  • MEDLINE





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