Supplementary material for doctoral dissertation: Prediction of Retention and Ionization Behavior of Selected Analytes in Micellar Liquid Chromatography and Mass Spectrometry Using Machine Learning Algorithms

Abstract

Data table for model building.

The data table used for modeling includes the following key components:

1. Chromatographic conditions: The chromatographic conditions encompass parameters such as the concentration of Brij L23, the pH of the micellar component of the mobile phase, and the volume fraction of acetonitrile. These parameters are systematically varied within specific ranges according to the experimental plan based on the Box-Behnken design.

2. Calculated molecular descriptors: An important aspect of the data table is the pool of calculated molecular descriptors. These descriptors provide valuable insights into the molecular characteristics of the compounds under investigation. They aid in understanding the relationship between the compounds' structural properties and their observed retention behavior.

3. Observed response: The observed response parameter consists of retention factors, a fundamental measure in chromatography. These retention factors were measured and recorded during the experimental process. They offer critical information about the compounds' affinity for the stationary phase and their overall behavior in the MLC system.