Predicting reversed-phase liquid chromatographic retention times of pesticides by deep neural networks

To be able to predict reversed phase liquid chromatographic (RPLC) retention times of contaminants is an asset in order to solve food contamination issues. The development of quantitative structure–retention relationship models (QSRR) requires selection of the best molecular descriptors and machine-...

Descripción completa

Detalles Bibliográficos
Autor principal: Parinet, Julien
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8671870/
https://www.ncbi.nlm.nih.gov/pubmed/34950792
http://dx.doi.org/10.1016/j.heliyon.2021.e08563

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8671870/
https://www.ncbi.nlm.nih.gov/pubmed/34950792
http://dx.doi.org/10.1016/j.heliyon.2021.e08563

Ejemplares similares