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dc.contributor.authorSamanipour, Saer
dc.contributor.authorBaz-Lomba, Jose A
dc.contributor.authorAlygizakis, Nikiforos A
dc.contributor.authorReid, Malcolm J
dc.contributor.authorThomaidis, Nikolaos S.
dc.contributor.authorThomas, Kevin V
dc.date.accessioned2018-09-04T13:27:13Z
dc.date.available2018-09-04T13:27:13Z
dc.date.created2017-09-28T13:31:37Z
dc.date.issued2017
dc.identifier.citationJournal of Chromatography A. 2017, 1501 68-78.nb_NO
dc.identifier.issn0021-9673
dc.identifier.urihttp://hdl.handle.net/11250/2560769
dc.descriptionEmbargo until 20 April 2019.nb_NO
dc.description.abstractLC-HR-QTOF-MS recently has become a commonly used approach for the analysis of complex samples. However, identification of small organic molecules in complex samples with the highest level of confidence is a challenging task. Here we report on the implementation of a two stage algorithm for LC-HR-QTOF-MS datasets. We compared the performances of the two stage algorithm, implemented via NIVA MZ AnalyzerTM, with two commonly used approaches (i.e. feature detection and XIC peak picking, implemented via UNIFI by Waters and TASQ by Bruker, respectively) for the suspect analysis of four influent wastewater samples. We first evaluated the cross platform compatibility of LC-HR-QTOF-MS datasets generated via instruments from two di erent manufacturers (i.e. Waters and Bruker). Our data showed that with an appropriate spectral weighting function the spectra recorded by the two tested instruments are comparable for our analytes. As a consequence, we were able to perform full spectral comparison between the data generated via the two studied instruments. Four extracts of wastewater influent were analyzed for 89 analytes, thus 356 detection cases. The analytes were divided into 158 detection cases of artificial suspect analytes (i.e. verified by target analysis) and 198 true suspects. The two stage algorithm resulted in a zero rate of false positive detection, based on the artificial suspect analytes while producing a rate of false negative detection of 0.12. For the conventional approaches, the rates of false positive detection varied between 0.06 for UNIFI and 0.15 for TASQ. The rates of false negative detection for these methods ranged between 0.07 for TASQ and 0.09 for UNIFI. The effect of background signal complexity on the two stage algorithm was evaluated through the generation of a synthetic signal. We further discuss the boundaries of applicability of the two stage algorithm. The importance of background knowledge and experience in evaluating the reliability of results during the suspect screening was evaluated.nb_NO
dc.language.isoengnb_NO
dc.publisherElseviernb_NO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleTwo stage algorithm vs commonly used approaches for the suspect screening of complex environmental samples analyzed via liquid chromatography high resolution time of flight mass spectroscopy: A test studynb_NO
dc.title.alternativeTwo stage algorithm vs commonly used approaches for the suspect screening of complex environmental samples analyzed via liquid chromatography high resolution time of flight mass spectroscopy: A test studynb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber68-78nb_NO
dc.source.volume1501nb_NO
dc.source.journalJournal of Chromatography Anb_NO
dc.identifier.doi10.1016/j.chroma.2017.04.040
dc.identifier.cristin1499719
dc.relation.projectNorges forskningsråd: 243720nb_NO
cristin.unitcode7464,30,21,0
cristin.unitcode7464,20,13,0
cristin.unitnameMiljøkjemi
cristin.unitnameØkotoksikologi
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode2


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
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