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. Fatty acids from C12 to C18 were used as model substances due to semivolatile characterics that caused non-uniform CAD behaviour. Considering both experimental conditions and molecular descriptors, a mixed quantitative structure-property relationship (QSPR) modeling was performed using Gradient Boosted Trees (GBT). The ensemble of 10 decisions trees (learning rate set at 0.55, the maximal depth set at 5, and the sample rate set at 1.0) was able to explain approximately 99% (Q²: 0.987, RMSE:

SUBMITTER: Pawellek R 

PROVIDER: S-EPMC8281619 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature