Charged aerosol detector response modeling for fatty acids based on experimental settings and molecular features: a machine learning approach

Please always quote using this URN: urn:nbn:de:bvb:20-opus-261618
  • The charged aerosol detector (CAD) is the latest representative of aerosol-based detectors that generate a response independent of the analytes' chemical structure. This study was aimed at accurately predicting the CAD response of homologous fatty acids under varying experimental conditions. Fatty acids from C12 to C18 were used as model substances due to semivolatile characterics that caused non-uniform CAD behaviour. Considering both experimental conditions and molecular descriptors, a mixed quantitative structure-property relationship (QSPR)The charged aerosol detector (CAD) is the latest representative of aerosol-based detectors that generate a response independent of the analytes' chemical structure. This study was aimed at accurately predicting the CAD response of homologous fatty acids under varying experimental conditions. Fatty acids from C12 to C18 were used as model substances due to semivolatile characterics that caused non-uniform CAD behaviour. Considering both experimental conditions and molecular descriptors, a mixed quantitative structure-property relationship (QSPR) modeling was performed using Gradient Boosted Trees (GBT). The ensemble of 10 decisions trees (learning rate set at 0.55, the maximal depth set at 5, and the sample rate set at 1.0) was able to explain approximately 99% (Q\(^2\): 0.987, RMSE: 0.051) of the observed variance in CAD responses. Validation using an external test compound confirmed the high predictive ability of the model established (R-2: 0.990, RMSEP: 0.050). With respect to the intrinsic attribute selection strategy, GBT used almost all independent variables during model building. Finally, it attributed the highest importance to the power function value, the flow rate of the mobile phase, evaporation temperature, the content of the organic solvent in the mobile phase and the molecular descriptors such as molecular weight (MW), Radial Distribution Function-080/weighted by mass (RDF080m) and average coefficient of the last eigenvector from distance/detour matrix (Ve2_D/Dt). The identification of the factors most relevant to the CAD responsiveness has contributed to a better understanding of the underlying mechanisms of signal generation. An increased CAD response that was obtained for acetone as organic modifier demonstrated its potential to replace the more expensive and environmentally harmful acetonitrile.show moreshow less

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Metadaten
Author: Ruben Pawellek, Jovana Krmar, Adrian Leistner, Nevena Djajić, Biljana Otašević, Ana Protić, Ulrike Holzgrabe
URN:urn:nbn:de:bvb:20-opus-261618
Document Type:Journal article
Faculties:Fakultät für Chemie und Pharmazie / Institut für Pharmazie und Lebensmittelchemie
Language:English
Parent Title (English):Journal of Cheminformatics
Year of Completion:2021
Volume:13
Issue:1
Article Number:53
Source:Journal of Cheminformatics (2021) 13:53. https://doi.org/10.1186/s13321-021-00532-0
DOI:https://doi.org/10.1186/s13321-021-00532-0
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
5 Naturwissenschaften und Mathematik / 54 Chemie / 540 Chemie und zugeordnete Wissenschaften
Tag:Charged aerosol detector (CAD); Fatty acids; Gradient boosted trees (GBT); High-performance liquid chromatography (HPLC); Quantitative structure-property relationship modeling (QSPR)
Release Date:2022/04/07
Open-Access-Publikationsfonds / Förderzeitraum 2021
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International