In this study, a quantitative structure-property relationship model was built in order to link molecular descriptors and chromatographic parameters as inputs towards CAD responsiveness. Aminoglycoside antibiotics, sugars, and acetylated amino sugars, which all lack a UV/vis chromophore, were selected as model substances due to their polar nature that represents a challenge in generating a CAD response. Acetone, PFPA, flow rate, data rate, filter constant, SM5_B(s), ATS7s, SpMin1_Bh(v), Mor09e, Mor22e, E1u, R7v+, and VP as the most influential inputs were correlated with the CAD response by virtue of ANN applying a backpropagation learning rule. External validation on previously unseen substances showed that the developed 13-6-3-1 ANN model could be used for CAD response prediction across the examined experimental domain reliably (R2 0.989 and RMSE 0.036). The obtained network was used to reveal CAD response correlations. The impact of organic modifier content and flow rate was in accordance with the theory of the detector's functioning. Additionally, the significance of SpMin1_Bh(v) aided in emphasizing the often neglected surface-dependent CAD character, while the importance of Mor22e as a molecular descriptor accentuated its dependency on the number of electronegative atoms taking part in charging the formed particles. The significance of PFPA demonstrated the possibility of using evaporative chaotropic reagents in CAD response improvement when dealing with highly polar substances that act as kosmotropes. The network was also used in identifying possible interactions between the most significant inputs. A joint effect of PFPA and acetone was shown, representing a good starting point for further investigation with different and, especially, eco-friendly organic solvents and chaotropic agents in the routine application of CAD.
Keywords: Artificial neural networks; Charged aerosol detector; Molecular descriptors; Prediction; QSPR.