Liquid Chromatographic Retention Time Prediction Models to Secure and Improve the Feature Annotation Process in High-Resolution Mass Spectrometry
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Publisher
Elsevier BV
Reference14 articles.
1. Development and application of retention time prediction models in the suspect and non-target screening of emerging contaminants;R Aalizadeh;Journal of Hazardous Materials,2019
2. Suspect screening of large numbers of emerging contaminants in environmental waters using artificial neural networks for chromatographic retention time prediction and high resolution mass spectrometry data analysis;R Bade;Science of the Total Environment,2015
3. Critical evaluation of a simple retention time predictor based on LogKow as a complementary tool in the identification of emerging contaminants in water;R Bade;Talanta,2015
4. Gradient liquid chromatographic retention time prediction for suspect screening applications: A critical assessment of a generalised artificial neural network-based approach across 10 multi-residue reversed-phase analytical methods;L P Barron;Talanta,2016
5. Retip: Retention Time Prediction for Compound Annotation in Untargeted Metabolomics;P Bonini;Anal. Chem,2020
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