Evaluation and application of machine learning-based retention time prediction for suspect screening of pesticides and pesticide transformation products in LC-HRMS.
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DOI: 10.1016/j.chemosphere.2020.129447
Abstract: Computational QSAR models have gradually been preferred for retention time prediction in data mining of emerging environmental contaminants using liquid chromatography coupled with mass spectrometry. Generally, the model performance relies on the components such as… read more here.
Keywords: pesticide transformation; time prediction; retention time; transformation products ... See more keywords