|Abstract Title:||Suspect screening of aquatic environmental matrices using high resolution analysis and in silico tools for broad scope tentative contaminant identification|
|Presenter Name:||Mr Leon Barron|
|Company/Organisation:||Analytical & Environmental Sciences Division, King's College London|
Abstract Information :
Characterising the breadth of emerging environmental contaminants is a dynamic challenge. Recent attention has focussed on liquid chromatography-high resolution mass spectrometry (LC-HRMS) for large and retrospectively mineable dataset capture. For preliminary identification of new/additional compounds, most works rely on HRMS data interpretation alone. Chromatographic data is often limited due to the unavailability and/or cost of reference standards for comparison. Prediction of retention has proved problematic especially for ionisable compounds under gradient conditions. Herein, a selection of analytical approaches developed for suspect screening of river water and wastewater in London is presented for compounds of both forensic and environmental interest. The work focuses on using both full-scan high resolution mass spectrometry data mining and machine learning tools for gradient chromatographic retention time prediction to tentatively identify new contaminants more rapidly. Data screening using these combined tools applied to river and waste water from the London area showed that the number of new compounds identified through in silico data processing halved in comparison to the use of HRMS alone. Approximately >30 additional compounds tentatively identified on any one day in influent/effluent wastewater and in receiving river water are discussed. The generalised performance of the approach was investigated using retention data for >1,100 compounds present in several complex matrices and across ten gradient reversed-phase LC-HRMS methods from different laboratories. Blind test predictions yielded an absolute accuracy of 1.02 ±0.54 min across all methods. Optimised and replicated network dependency on molecular descriptor data is also presented. Finally, recent advances using this approach to predict passive sampler uptake rate constants and combined retention time-collisional cross section predictions for methods using ion mobility - high resolution mass spectrometry (LC-IM-HRMS) are introduced.