Dataset for the artificial neural network-based evaluation of ionization in supercritical fluid chromatography-mass spectrometry using electrospray (ESI) and atmospheric pressure chemical ionization (APCI)

Raw data used for the evaluation of ionization in supercritical fluid chromatography-mass spectrometry using electrospray (ESI) and atmospheric pressure chemical ionization (APCI) pubhlished in article "Artificial Neural Networks: An Innovative Approach Used for Elucidation of Ionization Processes in Supercritical Fluid Chromatography-Mass Spectrometry" in Analytical Chemistry, 2025. Data set contains: (i) 226 molecular descriptors calculated by CDK Descriptor Calculator (v.1.4.8) from 3D structures of the 107 analytes optimized by semi-empirical AM1 quantum mechanical calculations using the MOPAC application of Chem 3D Pro version 14.0 software (CambridgeSoft) (Excel sheet), (ii) MS responses (peak areas) for each compouned analyzed using SFC-ESI-MS with selected ion monitoring (SIM) of protonated and deprotonated molecular ions and using 224 make-up solvent compositions correlated to QC samples and splitting ratio, (iii) MS responses (peak areas) for each compouned analyzed using SFC-APCI-MS with selected ion monitoring (SIM) of protonated and deprotonated molecular ions and using 224 make-up solvent compositions correlated to QC samples and splitting ratio, (iv) weights assigned to each molecular descriptor based on each experimental conditions by artificial neural network created using the neural network simulator in Matlab R2023a with the deep learning toolbox V.23.2 (The MathWorks, Inc., Massachusetts, USA) and a sigmoid activation function, a backpropagation learning algorithm with 500 learning cycles (Excel sheet).