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  • Title: A novel methodology for the determination of neutral perfluoroalkyl and polyfluoroalkyl substances in water by gas chromatography-atmospheric pressure photoionisation-high resolution mass spectrometry.
    Author: Ayala-Cabrera JF, Contreras-Llin A, Moyano E, Santos FJ.
    Journal: Anal Chim Acta; 2020 Mar 01; 1100():97-106. PubMed ID: 31987157.
    Abstract:
    Here, we developed and validated a new gas chromatography-atmospheric pressure photoionisation-high-resolution mass spectrometry (GC-APPI-HRMS) method combined with headspace solid-phase microextraction (HS-SPME) for the determination of neutral perfluoroalkyl and polyfluoroalkyl substances (PFASs) in water samples. The method includes fluorotelomer olefins (FTOs), fluorotelomer alcohols (FTOHs), fluoroctanesulfonamides (FOSAs) and sulfonamido-ethanols (FOSEs). The feasibility of the GC-APPI interface for the ionisation of the target compounds was evaluated, achieving the best results using negative-ion dopant-assisted ionisation with acetone and a source and capillary temperatures of 225 °C and 175 °C, respectively. Under optimal conditions, FTOs and FTOHs mass spectra showed intense in-source CID fragment ions from the fluoroalkyl chain but also the superoxide [M+O2]-• adduct ion. For FOSAs, [M-H]- was the main ion generated, while FOSEs mass spectra showed fragment ions corresponding to the different cleavages of the functional group. The high ionisation efficiency achieved with the GC-APPI interface provided limits of the detection lower than those obtained using traditional GC-MS ionisation techniques, with a high sensitivity, selectivity and precision. For water analysis, a fast and simple HS-SPME procedure was developed, avoiding evaporation steps, which could lead to the loss of the most volatile compounds. The developed HS-SPME GC-APPI-HRMS method showed a good analytical performance for the analysis of river water samples, providing very low limits of detection (0.02-15 ng L-1), good repeatability (RSD < 11%) and trueness (relative error < 12%).
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