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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Boti, V. I. | en |
| dc.contributor.author | Sakkas, V. A. | en |
| dc.contributor.author | Albanis, T. A. | en |
| dc.date.accessioned | 2015-11-24T16:48:17Z | - |
| dc.date.available | 2015-11-24T16:48:17Z | - |
| dc.identifier.issn | 0021-9673 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/9299 | - |
| dc.rights | Default Licence | - |
| dc.subject | experimental design | en |
| dc.subject | artificial neural networks | en |
| dc.subject | matrix solid-phase dispersion | en |
| dc.subject | edcs | en |
| dc.subject | performance liquid-chromatography | en |
| dc.subject | gas-chromatography | en |
| dc.subject | mass-spectrometry | en |
| dc.subject | polychlorinated-biphenyls | en |
| dc.subject | pesticide-residues | en |
| dc.subject | sample preparation | en |
| dc.subject | optimization | en |
| dc.subject | extraction | en |
| dc.subject | contaminants | en |
| dc.subject | herbicides | en |
| dc.title | An experimental design approach employing artificial neural networks for the determination of potential endocrine disruptors in food using matrix solid-phase dispersion | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | DOI 10.1016/j.chroma.2008.12.070 | - |
| heal.identifier.secondary | <Go to ISI>://000263610500004 | - |
| heal.identifier.secondary | http://ac.els-cdn.com/S002196730802270X/1-s2.0-S002196730802270X-main.pdf?_tid=129e5a05d935db394f7dbbbc62e10d5a&acdnat=1333022724_640a66d26aa6d54e203aff331c31a07f | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Χημείας | el |
| heal.publicationDate | 2009 | - |
| heal.abstract | Matrix 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.publisher | Elsevier | en |
| heal.journalName | Journal of Chromatography A | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά). ΧΗΜ | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Boti-2009-An experimental desi.pdf | 663.81 kB | Adobe PDF | View/Open Request a copy |
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