scholarly journals Development and validation of a rapid method to quantify neutral lipids by NP-HPLC-charged aerosol detector

2021 ◽  
Vol 102 ◽  
pp. 104022
Author(s):  
M.R. Infantes-Garcia ◽  
S.H.E. Verkempinck ◽  
J.M. Guevara-Zambrano ◽  
M.E. Hendrickx ◽  
T. Grauwet
Keyword(s):  
Rapid Method ◽  
Neutral Lipids ◽  
Charged Aerosol ◽  
Charged Aerosol Detector ◽  
Development And Validation

Development and validation of a stability-indicating HPLC method for topiramate using a mixed-mode column and charged aerosol detector

2018 ◽  
Vol 41 (8) ◽  
pp. 1716-1725 ◽  
Author(s):  
Eduardo Costa Pinto ◽  
Mariana da Silva Gonçalves ◽  
Lucio Mendes Cabral ◽  
Daniel W. Armstrong ◽  
Valéria Pereira de Sousa
Keyword(s):  
Mixed Mode ◽  
Hplc Method ◽  
Stability Indicating ◽  
Charged Aerosol ◽  
Charged Aerosol Detector ◽  
Development And Validation

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

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Ruben Pawellek ◽  
Jovana Krmar ◽  
Adrian Leistner ◽  
Nevena Djajić ◽  
Biljana Otašević ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Mobile Phase ◽  
Molecular Descriptors ◽  
Attribute Selection ◽  
Selection Strategy ◽  
Detector Response ◽  
Experimental Conditions ◽  
Molecular Features ◽  
Charged Aerosol ◽  
Charged Aerosol Detector

AbstractThe 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% (Q2: 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 (R2: 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.

HPLC Analysis of Phthalates Using Corona Charged Aerosol Detector

2014 ◽  
Vol 63 (10) ◽  
pp. 817-823 ◽  
Author(s):  
Shigetomo MATSUYAMA ◽  
Shinichi KINUGASA ◽  
Hajime OHTANI
Keyword(s):  
Hplc Analysis ◽  
Charged Aerosol ◽  
Charged Aerosol Detector
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