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• W4200264695 abstract "This work presents an optimized gas chromatography-electron ionization-high-resolution mass spectrometry (GC-EI-HRMS) screening method. Different method parameters affecting data processing with the Agilent Unknowns Analysis SureMass deconvolution software were optimized in order to achieve the best compromise between false positives and false negatives. To this end, an accurate-mass library of 26 model compounds was created. Then, five replicates of mussel extracts were spiked with a mixture of these 26 compounds at two concentration levels (10 and 100 ng/g dry weight in mussel, 50 and 500 ng/mL in extract) and injected in the GC-EI-HRMS system. The results of these experiments showed that accurate mass tolerance and pure weight factor (combination of reverse-forward library search) are the most critical factors. The validation of the developed method afforded screening detection limits in the 2.5-5 ng range for passive sampler extracts and 1-2 ng/g for mussel sample extracts, and limits of quantification in the 0.6-3.2 ng and 0.1-1.8 ng/g range, for the same type of samples, respectively, for 17 model analytes. Once the method was optimized, an accurate-mass HRMS library, containing retention indexes, with ca. 355 spectra of derivatized and non-derivatized compounds was generated. This library (freely available at https://doi.org/10.5281/zenodo.5647960 ), together with a modified Agilent Pesticides Library of over 800 compounds, was applied to the screening of passive samplers, both of polydimethylsiloxane and polar chemical integrative samplers (POCIS), and mussel samples collected in Galicia (NW Spain), where a total of 75 chemicals could be identified." @default.
• W4200264695 created "2021-12-31" @default.
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• W4200264695 date "2021-12-04" @default.
• W4200264695 modified "2023-10-15" @default.
• W4200264695 title "Development and application of an in-house library and workflow for gas chromatography–electron ionization–accurate-mass/high-resolution mass spectrometry screening of environmental samples" @default.
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• W4200264695 doi "https://doi.org/10.1007/s00216-021-03810-w" @default.
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