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dc.creatorAndrić, Filip
dc.creatorŠegan, Sandra
dc.creatorDramićanin, Aleksandra M.
dc.creatorMajstorović, Helena
dc.creatorMilojković-Opsenica, Dušanka
dc.date.accessioned2019-10-22T08:38:40Z
dc.date.available2018-06-18
dc.date.available2019-10-22T08:38:40Z
dc.date.issued2016
dc.identifier.issn0021-9673
dc.identifier.urihttp://cer.ihtm.bg.ac.rs/handle/123456789/3156
dc.description.abstractSoil-water partition coefficient normalized to the organic carbon.content (K-OC) is one of the crucial properties influencing the fate of organic compounds in the environment. Chromatographic methods are well established alternative for direct sorption techniques used for K-OC determination. The present work proposes reversed-phase thin-layer chromatography (RP-TLC) as a simpler, yet equally accurate method as officially recommended HPLC technique. Several TLC systems were studied including octadecyl-(RP18) and cyano-(CN) modified silica layers in combination with methanol-water and acetonitrile-water mixtures as mobile phases. In total 50 compounds of different molecular shape, size, and various ability to establish specific interactions were selected (phenols, beznodiazepines, triazine herbicides, and polyaromatic hydrocarbons). Calibration set of 29 compounds with known logK(OC) values determined by sorption experiments was used to build simple univariate calibrations, Principal Component Regression (PCR) and Partial Least Squares (PLS) models between logK(OC) and TLC retention parameters. Models exhibit good statistical performance, indicating that CN-layers contribute better to logK(OC) modeling than RP18-silica. The most promising TLC methods, officially recommended HPLC method, and four in silico estimation approaches have been compared by non-parametric Sum of Ranking Differences approach (SRD). The best estimations of logK(OC) values were achieved by simple univariate calibration of TLC retention data involving CN-silica layers and moderate content of methanol (40-50% v/v). They were ranked far well compared to the officially recommended HPLC method which was ranked in the middle. The worst estimates have been obtained from in silico computations based on octanol-water partition coefficient. Linear Solvation Energy Relationship study revealed that increased polarity of CN-layers over RP18 in combination with methanol-water mixtures is the key to better modeling of logK(OC) through significant diminishing of dipolar and proton accepting influence of the mobile phase as well as enhancing molar refractivity in excess of the chromatographic systems. (C) 2016 Elsevier B.V. All rights reserved.en
dc.publisherElsevier Science Bv, Amsterdam
dc.relationinfo:eu-repo/grantAgreement/MESTD/Basic Research (BR or ON)/172017/RS//
dc.rightsembargoedAccess
dc.sourceJournal of Chromatography A
dc.subjectSoil-water partition coefficienten
dc.subjectReversed-phase thin-layer chromatography (RP-TLC)en
dc.subjectBenzodiazepinesen
dc.subjectPolycyclic aromatic hydrocarbons (PAHs)en
dc.subjectMultivariate regression methodsen
dc.subjectSum of Ranking Differencen
dc.titleLinear modeling of the soil-water partition coefficient normalized to organic carbon content by reversed-phase thin-layer chromatographyen
dc.typearticle
dc.rights.licenseBY-NC-ND
dcterms.abstractAндрић, Филип; Милојковић-Опсеница, Душанка; Драмићанин, Aлександра М.; Мајсторовић, Хелена; Шеган, Сандра;
dc.citation.volume1458
dc.citation.spage136
dc.citation.epage144
dc.citation.other1458: 136-144
dc.citation.rankM21
dc.description.otherThis is peer-reviewed version of the following artcle: Andrić, F.; Šegan, S.; Dramićanin, A.; Majstorović, H.; Milojković-Opsenica, D. Linear Modeling of the Soil-Water Partition Coefficient Normalized to Organic Carbon Content by Reversed-Phase Thin-Layer Chromatography. Journal of Chromatography A 2016, 1458, 136–144. [https://doi.org/10.1016/j.chroma.2016.06.063]
dc.description.other[http://cer.ihtm.bg.ac.rs/handle/123456789/1849]
dc.identifier.pmid27378251
dc.identifier.doi10.1016/j.chroma.2016.06.063
dc.identifier.rcubKon_3104
dc.identifier.fulltexthttp://cer.ihtm.bg.ac.rs/bitstream/id/14296/Linear_modeling_of_acc_2016.pdf
dc.identifier.scopus2-s2.0-84993983941
dc.identifier.wos000380972800016
dc.type.versionacceptedVersionen


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