Charged aerosol detector response modeling for fatty acids based on experimental settings and molecular features: a machine learning approach

Charged aerosol detector response modeling for fatty acids based on experimental settings and molecular features: a machine learning approach

Abstract The charged aerosol detector (CAD) is the latest representative of aerosol-based detectors that generate a response independent of the analytes’ chemical structure. This study was aimed at accurately predicting the CAD response of homologous fatty acids under varying experimental conditions...

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Bibliographic Details
Main Authors:	Ruben Pawellek, Jovana Krmar, Adrian Leistner, Nevena Djajić, Biljana Otašević, Ana Protić, Ulrike Holzgrabe
Format:	Article
Language:	English
Published:	BMC 2021-07-01
Series:	Journal of Cheminformatics
Subjects:	High-performance liquid chromatography (HPLC) Charged aerosol detector (CAD) Gradient boosted trees (GBT) Quantitative structure–property relationship modeling (QSPR) Fatty acids
Online Access:	https://doi.org/10.1186/s13321-021-00532-0

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