Feature-based molecular networking for identification of organic micropollutants including metabolites by non-target analysis applied to riverbank filtration.

Due to growing concern about organic micropollutants and their transformation products (TP) in surface and drinking water, reliable identification of unknowns is required. Here, we demonstrate how non-target liquid chromatography (LC)-high-resolution tandem mass spectrometry (MS/MS) and the feature-based molecular networking (FBMN) workflow provide insight into water samples from four riverbank filtration sites with different redox conditions. First, FBMN prioritized and connected drinking water relevant and seasonally dependent compounds based on a modification-aware MS/MS cosine similarity.

Seasonal performance assessment of four riverbank filtration sites by combined non-target and effect-directed analysis.

Targeting the most relevant organic micropollutants (OMP) in routine analysis appears difficult due to formation of transformation products of unknown concentration or toxicity. Performance assessment of water purification processes is still based upon limited target data. Therefore, we broadened the assessment of the removal efficiencies with combined non-target and effect-directed analysis at four riverbank filtration (RBF) sites in Germany.

Impact of seasonality, redox conditions, travel distances and initial concentrations on micropollutant removal during riverbank filtration at four sites.

Riverbank filtration (RBF) is a reliable water purification technique that has proven to be suitable for the removal of organic micropollutants. Its removal efficiency and dependency on a variety of factors such as redox conditions, temperatures, geology, travel times, level of initial micropollutant concentrations and seasonality were investigated during three seasonal sampling campaigns. Two anoxic (silty sand, Ems river) and two oxic (gravel, Ruhr river) RBF sites in Germany with different travel distances (42-633 m) were studied.

Determination of food allergens by LC-MS: Impacts of sample preparation, food matrix, and thermal processing on peptide detectability and quantification.

Food allergies are a growing worldwide concern and the contamination of products with food allergens represents a significant health risk to allergic consumers. With the introduction of reference doses, quantitative methods are needed for the monitoring of allergen levels, and the potential of LC-MS/MS is of hugely growing interest. In this study, we demonstrate that relevant food matrices (bakery products and chocolates) and thermal food processing substantially influence the quantification of 18 marker peptides from various nut and peanut allergens via targeted proteomics.