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Comparison of Retention Modeling in ion Chromatography by Using Multiple Linear Regression and Artificial Neural Networks
Separation Science and Technology, 40 (2005), 6; 1333-1352. https://doi.org/10.1081/SS-200052816
Bolanča, Tomislav; Cerjan-Stefanović, Štefica; Srečnik, Goran; Debeljak, Željko; Novič, Milko

Institucijski repozitorij: Repozitorij KBC Osijek

Citirajte ovaj rad

Bolanča, T., Cerjan Stefanović, Š., Srečnik, G., Debeljak, Ž. i Novič, M. (2005). Comparison of Retention Modeling in ion Chromatography by Using Multiple Linear Regression and Artificial Neural Networks. Separation Science and Technology, 40. (6), 1333-1352. doi: 10.1081/SS-200052816

Bolanča, Tomislav, et al. "Comparison of Retention Modeling in ion Chromatography by Using Multiple Linear Regression and Artificial Neural Networks." Separation Science and Technology, vol. 40, br. 6, 2005, str. 1333-1352. https://doi.org/10.1081/SS-200052816

Bolanča, Tomislav, Štefica Cerjan Stefanović, Goran Srečnik, Željko Debeljak i Milko Novič. "Comparison of Retention Modeling in ion Chromatography by Using Multiple Linear Regression and Artificial Neural Networks." Separation Science and Technology 40, br. 6 (2005): 1333-1352. https://doi.org/10.1081/SS-200052816

Bolanča, T., et al. (2005) 'Comparison of Retention Modeling in ion Chromatography by Using Multiple Linear Regression and Artificial Neural Networks', Separation Science and Technology, 40(6), str. 1333-1352. doi: 10.1081/SS-200052816

Bolanča T, Cerjan Stefanović Š, Srečnik G, Debeljak Ž, Novič M. Comparison of Retention Modeling in ion Chromatography by Using Multiple Linear Regression and Artificial Neural Networks. Separation Science and Technology [Internet]. 2005. [pristupljeno 22.11.2024.];40(6):1333-1352. doi: 10.1081/SS-200052816

T. Bolanča, Š. Cerjan Stefanović, G. Srečnik, Ž. Debeljak i M. Novič, "Comparison of Retention Modeling in ion Chromatography by Using Multiple Linear Regression and Artificial Neural Networks", Separation Science and Technology, vol. 40, br. 6, str. 1333-1352, 2005. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:239:911779. [Citirano: 22.11.2024.]

Za citiranje koristite ovu mrežnu adresu: https://urn.nsk.hr/urn:nbn:hr:239:911779
Podaci o radu
Naslov (engleski)Comparison of Retention Modeling in ion Chromatography by Using Multiple Linear Regression and Artificial Neural Networks
AutorTomislav Bolanča
MBZ: 233894
AutorŠtefica Cerjan-Stefanović
MBZ: 6631
AutorGoran Srečnik
AutorŽeljko Debeljak
MBZ: 296571
AutorMilko Novič
Autorova ustanova(Klinički bolnički centar Osijek)
Autorova ustanovaSveučilište u Zagrebu
Fakultet kemijskog inženjerstva i tehnologije
Znanstveno / umjetničko
područje, polje i grana		BIOMEDICINA I ZDRAVSTVO
Kliničke medicinske znanosti
Neurologija
Znanstveno / umjetničko
područje, polje i grana		BIOMEDICINA I ZDRAVSTVO
Farmacija
Farmacija
Znanstveno / umjetničko
područje, polje i grana		BIOMEDICINA I ZDRAVSTVO
Farmacija
Medicinska biokemija
Sažetak (engleski)
Abstract: The aim of this work is comparison of the prediction power of multiple linear regression and artificial neural networks retention models for inorganic anions (fluoride, chloride, nitrite, sulfate, bromide, nitrate, and phosphate) in suppressed ion chromatography with isocratic elution. Relations between ion chromatographic parameters (eluent flow rate and concentration of OH2 in eluent) and retention time of particular anion are described with unique mathematical function obtained by multiple linear regression and with a three-layers feed-forward artificial neural network. The artificial neural network was trained with a Levenberg-Marquardt batch error back propagation algorithm. It is shown that the multiple linear regression retention model has lower, but still very satisfactory, predictive ability. Due to its complexity, the artificial neural network must still be regarded as a more complicated technique. That indicates multiple linear regression as a method of choice for retention modeling in the case of ion chromatographic analysis with isocratic elution.
Ključne riječi (engleski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran - međunarodna recenzija
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaSeparation Science and Technology
Brojčani podacivol. 40, br. 6, str. 1333-1352
p-ISSN0149-6395
e-ISSN1520-5754
DOIhttps://doi.org/10.1081/SS-200052816
URN:NBNurn:nbn:hr:239:911779
Datum objave publikacije2005
URL dokumentahttps://www.tandfonline.com/doi/full/10.1081/SS-200052816
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Imenovanje 3.0 (CC BY 3.0)
Datum i vrijeme pohrane2020-05-11 10:37:47