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Application of neural networks for response surface modeling in HPLC optimization
dc.creator | Agatonović-Kuštrin, Snežana | |
dc.creator | Zečević, Mira | |
dc.creator | Živanović, L | |
dc.creator | Tucker, I.G | |
dc.date.accessioned | 2019-09-02T10:50:34Z | |
dc.date.available | 2019-09-02T10:50:34Z | |
dc.date.issued | 1998 | |
dc.identifier.issn | 0003-2670 | |
dc.identifier.uri | https://farfar.pharmacy.bg.ac.rs/handle/123456789/183 | |
dc.description.abstract | The usefulness of artificial neural networks for response surface modeling in HPLC optimization is compared with multiple regression methods. The results show that neural networks offer promising possibilities in HPLC method development. The predicted capacity factors of analytes were better to those obtained with multiple regression method. | en |
dc.publisher | Elsevier Science BV, Amsterdam | |
dc.rights | restrictedAccess | |
dc.source | Analytica Chimica Acta | |
dc.title | Application of neural networks for response surface modeling in HPLC optimization | en |
dc.type | article | |
dc.rights.license | ARR | |
dcterms.abstract | Живановић, Л; Aгатоновић-Куштрин, Снежана; Зечевић, Мира; Туцкер, И.Г; | |
dc.citation.volume | 364 | |
dc.citation.issue | 1-3 | |
dc.citation.spage | 265 | |
dc.citation.epage | 273 | |
dc.citation.other | 364(1-3): 265-273 | |
dc.citation.rank | M21 | |
dc.identifier.wos | 000074040300029 | |
dc.identifier.doi | 10.1016/S0003-2670(98)00121-4 | |
dc.identifier.scopus | 2-s2.0-0032565631 | |
dc.type.version | publishedVersion |