THE USE OF ARTIFICIAL NEURAL NETWORKS FOR RETENTION TIME PREDICTION IN THE IDENTIFICATION OF METABOLITES AND TRANSFORMATION PRODUCTS IN ENVIRONMENTAL WATERS

摘要

The recent development of wide-scope high resolution mass spectrometry (HRMS) screening methods has resulted in a much improved capability for new compound identification in environmental samples. However, positive identifications at trace levels commonly seen still rely on analytical reference standards for chromatographic retention time and mass spectral comparisons. Chromatographic retention time prediction can play a role in increasing confidence in suspect screening efforts for new compounds, especially when standards are not available.