Quantitative structure-property relationship modeling of polar analytes lacking UV chromophores to charged aerosol detector response
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.
KeywordsQSPR Charged aerosol detector Artificial neural networks Molecular descriptors Prediction
Artificial neural network
Charged aerosol detector
European Directorate for the Quality of Medicines & HealthCare
Flow injection analysis
Liquid chromatography-mass spectrometry
Quantitative structure-property relationship
Coefficient of determination
Root mean square error
Variable sensitivity error
Variable sensitivity ratio
Thanks are due to Oliver Wahl (University of Würzburg) and ThermoFisher Scientific/Dionex Softron (Germering, Germany) for insightful discussions.
This work was financially supported by the Ministry of Education and Science of the Republic of Serbia (project no. 172033) and travels were funded by the Bayerische Forschungsallianz (Munich, Germany).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
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