In Silico Chromatographic Retention Index Prediction for Enhanced Identification of Unknown Micropollutants in Wastewater

01 May 2023 → 30 April 2024
Regional and community funding: Special Research Fund
Research disciplines
  • Natural sciences
    • Analytical separation and detection techniques
    • Instrumental methods
Chromatography In silico prediction Structural elucidation Micropollutants
Project description

High resolution mass spectrometry predictions software cannot always reliably predict the elemental


composition of (larger) molecules while structural information obtained by MS remains limited. The

development of the predictive algorithm is purely based on retentive data and molecular descriptors,

which allow confirmation and/or invalidation of the possible structural formulas through the

implementation of the optimized predictive retention index model. These techniques are developed

and introduced in the framework of this PhD.