An experimental design approach employing artificial neural networks for the determination of potential endocrine disruptors in food using matrix solid-phase dispersion

dc.contributor.authorBoti, V. I.en
dc.contributor.authorSakkas, V. A.en
dc.contributor.authorAlbanis, T. A.en
dc.date.accessioned2015-11-24T16:48:17Z
dc.date.available2015-11-24T16:48:17Z
dc.identifier.issn0021-9673-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/9299
dc.rightsDefault Licence-
dc.subjectexperimental designen
dc.subjectartificial neural networksen
dc.subjectmatrix solid-phase dispersionen
dc.subjectedcsen
dc.subjectperformance liquid-chromatographyen
dc.subjectgas-chromatographyen
dc.subjectmass-spectrometryen
dc.subjectpolychlorinated-biphenylsen
dc.subjectpesticide-residuesen
dc.subjectsample preparationen
dc.subjectoptimizationen
dc.subjectextractionen
dc.subjectcontaminantsen
dc.subjectherbicidesen
dc.titleAn experimental design approach employing artificial neural networks for the determination of potential endocrine disruptors in food using matrix solid-phase dispersionen
heal.abstractMatrix solid-phase dispersion (MSPD) as a sample preparation method for the determination of two potential endocrine disruptors, linuron and diuron and their common metabolites, 1-(3,4-dichlorophenyl)-3-methylurea (DCPMU), 1-(3,4-dichlorophenyl) urea (DCPU) and 3,4-dichloroaniline (3,4-DCA) in food commodities has been developed. The influence of the main factors on the extraction process yield was thoroughly evaluated. For that purpose, a 3((4-1)) fractional factorial design in further combination with artificial neural networks (ANNs) was employed. The optimal networks found were afterwards used to identify the optimum region corresponding to the highest average recovery displaying at the same time the lowest standard deviation for all analytes. Under final optimal conditions, potato samples (0.5 g) were mixed and dispersed on the same amount of Florisil. The blend was transferred on a polypropylene cartridge and analytes were eluted using 10 ml of methanol. The extract was concentrated to 50 mu l of acetonitrile/water (50:50) and injected in a high performance liquid chromatography coupled to UV-diode array detector system (HPLC/UV-DAD). Recoveries ranging from 55 to 96% and quantification limits between 5.3 and 15.2 ng/g were achieved. The method was also applied to other selected food commodities such as apple, carrot, cereals/wheat flour and orange juice demonstrating very good overall performance. (C) 2008 Elsevier B.V. All rights reserved.en
heal.accesscampus-
heal.fullTextAvailabilityTRUE-
heal.identifier.primaryDOI 10.1016/j.chroma.2008.12.070-
heal.identifier.secondary<Go to ISI>://000263610500004-
heal.identifier.secondaryhttp://ac.els-cdn.com/S002196730802270X/1-s2.0-S002196730802270X-main.pdf?_tid=129e5a05d935db394f7dbbbc62e10d5a&acdnat=1333022724_640a66d26aa6d54e203aff331c31a07f-
heal.journalNameJournal of Chromatography Aen
heal.journalTypepeer reviewed-
heal.languageen-
heal.publicationDate2009-
heal.publisherElsevieren
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Χημείαςel
heal.typejournalArticle-
heal.type.elΆρθρο Περιοδικούel
heal.type.enJournal articleen

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