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Analytical and Bioanalytical Chemistry

, Volume 411, Issue 13, pp 2945–2959 | Cite as

Quantitative structure-property relationship modeling of polar analytes lacking UV chromophores to charged aerosol detector response

  • Klaus Schilling
  • Jovana Krmar
  • Nevena Maljurić
  • Ruben Pawellek
  • Ana ProtićEmail author
  • Ulrike HolzgrabeEmail author
Research Paper

Abstract

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

QSPR Charged aerosol detector Artificial neural networks Molecular descriptors Prediction 

Abbreviations

ANN

Artificial neural network

CAD

Charged aerosol detector

EDQM

European Directorate for the Quality of Medicines & HealthCare

FIA

Flow injection analysis

LC-MS

Liquid chromatography-mass spectrometry

PFPA

Pentafluoropropionic acid

QSPR

Quantitative structure-property relationship

R2

Coefficient of determination

RMSE

Root mean square error

VP

Vapor pressure

VSE

Variable sensitivity error

VSR

Variable sensitivity ratio

Notes

Acknowledgments

Thanks are due to Oliver Wahl (University of Würzburg) and ThermoFisher Scientific/Dionex Softron (Germering, Germany) for insightful discussions.

Funding information

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.

Supplementary material

216_2019_1744_MOESM1_ESM.pdf (292 kb)
ESM 1 (PDF 291 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute for Pharmacy and Food ChemistryUniversity of WürzburgWürzburgGermany
  2. 2.Faculty of Pharmacy, Department of Drug AnalysisUniversity of BelgradeBelgradeSerbia

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