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Pregled bibliografske jedinice broj: 691471

Development of Gradient Retention Model in Ion Chromatography—Part I: Conventional QSRR Approach


Ukić, Šime; Novak, Mirjana; Žuvela, Petar; Avdalović, Nebojša; Liu, Yan; Buszewski, Bogusław; Bolanča, Tomislav
Development of Gradient Retention Model in Ion Chromatography—Part I: Conventional QSRR Approach // Chromatographia, 77 (2014), 15; 985-996 doi:10.1007/s10337-014-2653-5 (međunarodna recenzija, članak, znanstveni)


Naslov
Development of Gradient Retention Model in Ion Chromatography—Part I: Conventional QSRR Approach

Autori
Ukić, Šime ; Novak, Mirjana ; Žuvela, Petar ; Avdalović, Nebojša ; Liu, Yan ; Buszewski, Bogusław ; Bolanča, Tomislav

Izvornik
Chromatographia (0009-5893) 77 (2014), 15; 985-996

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

Ključne riječi
Ion chromatography; QSRR; gradient retention model; stepwise MLR; PLS; UVE–PLS

Sažetak
New retention methodology that integrates conventional quantitative structure-retention relationship QSRR) approach and gradient retention modeling based on isocratic retention data is developed and presented in the paper. Such integrated approach removes the general QSRR limitation of highly predefined application conditions (i.e., QSRR are generally applicable only under the conditions used during model development) and allows the prediction of retentions over wide range of different elution conditions (practically for any isocratic or gradient elution profile). At the same time, it retains the ability to predict retention of components unknown to model, i.e., the components that have not been used in modeling. Ion-exchange chromatography (IC) analysis of carbohydrates was selected as modeling environment. Three regression techniques were applied and compared during QSRR modeling, namely: stepwise multiple linear regression, partial least squares (PLS), and uninformative variable elimination–PLS regression. The obtained prediction results of the best QSRR model (root-mean-square error of prediction = 22.69 %) were similar to those found in the literature. The upgrade from QSRR to the integrated model did not diminish the predictive ability of the model, indicating an excellent potential of the developed methodology not only in IC but also in chromatography in general.

Izvorni jezik
Engleski

Znanstvena područja
Kemija, Kemijsko inženjerstvo



POVEZANOST RADA


Projekt / tema
110005
125-1253092-3004 - Procesi ionske izmjene u sustavu kvalitete industrijskih voda (Tomislav Bolanča, )

Ustanove
Fakultet kemijskog inženjerstva i tehnologije, Zagreb

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


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