Predicting the Diagnostic Information of Tandem Mass Spectra of Environmentally Relevant Compounds Using Machine Learning.

Acquisition and processing of informative tandem mass spectra (MS2) is crucial for numerous applications, including library-based (tentative) identification, feature prioritization, and prediction of chemical and toxicological characteristics. However, for environmentally relevant compounds, approaches to automatically assess the quality of the MS2 spectra are missing. This work focused on developing a machine learning-based approach to automatically evaluate the diagnostic information of MS2 spectra (e.g., number, distribution, and intensity of diagnostic fragments) of environmentally relevant compounds analyzed with electrospray ionization. For this, approximately 1400 MS2 spectra of 204 environmental contaminants, acquired with different collision energies using liquid chromatography coupled to high-resolution mass spectrometry, were used to train a random forest classifier to distinguish between spectra providing good or poor diagnostic information. Prior to training, validation, and testing, spectra were manually labeled based on criteria such as number, intensity, range of fragments present, molecular ion intensity, and noise levels. Subsequently, feature engineering and selection were applied to retrieve relevant variables from raw MS2 spectra as inputs for the classifier. The optimal set of features based on model performances was selected and used to train a final model, which showed an accuracy of 84%, a precision of 88%, and a recall of 75%. Results show that the combination of selected features and the machine learning model used here can effectively distinguish between MS2 spectra providing good or poor diagnostic information according to the defined criteria. The developed model has the potential to improve a broad range of applications that rely on MS2 data.

The determination of two emerging perfluoroalkyl substances and related halogenated sulfonic acids and their significance for the drinking water supply chain.

In the present study analytical methodologies were developed for two newly emerging polar perfluorinated alkyl substances (PFAS), namely F3-MSA, and HFPO-DA, in order to assess the occurrence and levels of these PFAS in Dutch and Belgian waters. Two separate methods were needed for analysing F3-MSA and HFPO-DA. A mixed-mode and a reversed phase C18 method were developed for F3-MSA and HFPO-DA, respectively, using a high resolution Orbitrap Fusion mass spectrometer for detection, yielding satisfactory LOD and LOQ results for both analytes. A sample campaign was performed collecting single grab samples from various locations and different stages of the drinking water production chain. Whereas both PFAS were absent in groundwaters, they were found to be present in surface waters, river bank and dune infiltrates, process water, and drinking water, demonstrating the persistence and mobility of both compounds. Based on provisional health-based guideline values (0.15 µg L-1 for HFPO-DA, 11.9 mg L-1 for F3-MSA), the current levels in drinking water from the suppliers involved in this study do not pose a health risk for the human population. Common removal processes used in drinking water production appeared to remove these polar compounds at most partially. At locations close to potential sources of these chemicals (e.g. fluoropolymer production sites), the quality of surface water or river bank filtrate abstracted for production of drinking water must therefore be monitored.

Application of effect-directed analysis to identify mutagenic nitrogenous disinfection by-products of advanced oxidation drinking water treatment.

Advanced oxidation processes are important barriers for organic micropollutants in (drinking) water treatment. It is however known that medium pressure UV/H2O2 treatment may lead to mutagenicity in the Ames test, which is no longer present after granulated activated carbon (GAC) filtration. Many nitrogen-containing disinfection by-products (N-DBPs) result from the reaction of photolysis products of nitrate with (photolysis products of) natural organic material (NOM) during medium pressure UV treatment of water. Identification of the N-DBPs and the application of effect-directed analysis to combine chemical screening results with biological activity would provide more insight into the relation of specific N-DBPs with the observed mutagenicity and was the subject of this study. To this end, fractions of medium pressure UV-treated and untreated water extracts were prepared using preparative HPLC and tested using the Ames fluctuation test. In addition, high-resolution mass spectrometry was performed on all fractions to assess the presence of N-DBPs. Based on toxicity data and read across analysis, we could identify five N-DBPs that are potentially genotoxic and were present in relatively high concentrations in the fractions in which mutagenicity was observed. The results of this study offer opportunities to further evaluate the identity and potential health concern of N-DBPs formed during advanced oxidation UV drinking water treatment.