Predicting reversed-phase liquid chromatographic retention times of pesticides by deep neural networks
Author:
Funder
Agence Nationale de la Recherche
Publisher
Elsevier BV
Subject
Multidisciplinary
Reference22 articles.
1. Development and application of retention time prediction models in the suspect and non-target screening of emerging contaminants;Aalizadeh;J. Hazard Mater.,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;Bade;Sci. Total Environ.,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;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;Barron;Talanta,2016
5. Suspect screening of environmental contaminants by UHPLC-HRMS and transposable Quantitative Structure-Retention Relationship modelling;Bride;J. Hazardous Mater.,2021
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