TY - GEN A1 - Lisec, Jan T1 - CorrectOverloadedPeaks (R package to correct APCI-HR-MS peak data) N2 - Metabolomics, the analysis of potentially all small molecules within a biological system, has become a valuable tool for biomarker identification and the elucidation of biological processes. While metabolites are often present in complex mixtures at extremely different concentrations, the dynamic range of available analytical methods to capture this variance is generally limited. Here, we show that gas chromatography coupled to atmospheric pressure chemical ionization mass spectrometry (GC-APCI-MS), a state of the art analytical technology applied in metabolomics analyses, shows an average linear range (LR) of 2.39 orders of magnitude for a set of 62 metabolites from a representative compound mixture. We further developed a computational tool to extend this dynamic range on average by more than 1 order of magnitude, demonstrated with a dilution series of the compound mixture, using robust and automatic reconstruction of intensity values exceeding the detection limit. The tool is freely available as an R package (CorrectOverloadedPeaks) from CRAN (https://cran.r-project.org/) and can be incorporated in a metabolomics data processing pipeline facilitating large screening assays. KW - Software KW - Metabolomics KW - R package KW - Data processing PY - 2019 UR - https://github.com/cran/CorrectOverloadedPeaks/ PB - GitHub CY - San Francisco, CA, USA AN - OPUS4-57853 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Lisec, Jan T1 - MetabolomicsBasics (R package to process and analyze metabolomics datasets) N2 - Raw data from metabolomics experiments are initially subjected to peak identification and signal deconvolution to generate raw data matrices m × n, where m are samples and n are metabolites. We describe here simple statistical procedures on such multivariate data matrices, all provided as functions in the programming environment R, useful to normalize data, detect biomarkers, and perform sample classification. KW - Software KW - Mass Spectrometry KW - R package KW - Data processing PY - 2018 UR - https://github.com/cran/MetabolomicsBasics/ PB - GitHub CY - San Francisco, CA, USA AN - OPUS4-57854 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Zweigle, J. A1 - Capitain, C. A1 - Simon, Fabian Michael A1 - Roesch, Philipp A1 - Bugsel, B. A1 - Zwiener, C. T1 - Non-extractable PFAS in functional textiles − Characterization by complementary methods: oxidation, hydrolysis, and fluorine sum parameters N2 - Per- and polyfluoroalkyl substances (PFAS) are widely used for durable water-repellent finishing of different fabrics and textiles like outdoor clothing, carpets, medical textiles and more. Existing PFAS extraction techniques followed by target analysis are often insufficient in detecting widely used side-chain fluorinated polymers (SFPs) that are barely or non-extractable. SFPs are typically copolymers consisting of a non-fluorinated backbone with perfluoroalkyl side-chains to obtain desired properties. We compared the accessible analytical information and performance of complementary techniques based on oxidation (dTOP assay, PhotoTOP), hydrolysis (THP assay), standard extraction, extractable organic fluorine (EOF), and total fluorine (TF) with five functional textiles and characterized 7 further textiles only by PhotoTOP oxidation. The results show that when applied directly to textile samples, oxidation by dTOP and PhotoTOP and also hydrolysis by the THP are able to capture large fractions of the TF in form of perfluoroalkyl side-chains present in the textiles while methods relying on extracts (EOF, target and non-target analysis) were much lower (e.g., factor ~25-50 lower). The conversion of large fractions of the measured TF into PFCAs or FTOHs from fluorinated side chains is in contrast to previous studies. Concentrations ranged from