<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This paper presents rapid and low cost analytical method for the determination of the fluoxetine enantiomeric composition in biological samples (urine). The combination of synchronous fluorescence spectrometry and inverse multivariate calibration methods was used. The chiral recognition of the fluoxetine was based on the creating of the diastereomeric complexes with <italic>β</italic>-cyclodextrin. A net analytical signal of diastereomeric complexes was obtained by the addition of aliquot part of urine into calibration and validation sets. This step ensures the elimination of the urine matrix effect. The synchronous fluorescence spectra at the constant wavelength differences (Δλ) of 30 and 50 nm, based on RMS %RE values, were chosen for chemometric analysis. Principal component regression (PCR) and partial least square method (PLS) were compared to determine the enantiomeric composition. The most suitable results were provided by the PLS model constructed from the synchronous data at Δλ = 50 nm. The calculated figure of merit was used for validation of proposed method.</p></abstract>

This paper presents rapid and low cost analytical method for the determination of the fluoxetine enantiomeric composition in biological samples (urine). The combination of synchronous fluorescence spectrometry and inverse multivariate calibration methods was used. The chiral recognition of the fluoxetine was based on the creating of the diastereomeric complexes with β-cyclodextrin. A net analytical signal of diastereomeric complexes was obtained by the addition of aliquot part of urine into calibration and validation sets. This step ensures the elimination of the urine matrix effect. The synchronous fluorescence spectra at the constant wavelength differences (Δλ) of 30 and 50 nm, based on RMS %RE values, were chosen for chemometric analysis. Principal component regression (PCR) and partial least square method (PLS) were compared to determine the enantiomeric composition. The most suitable results were provided by the PLS model constructed from the synchronous data at Δλ = 50 nm. The calculated figure of merit was used for validation of proposed method.

Determination of enantiomeric composition of fluoxetine by synchronous fluorescence spectrometry coupled with multivariate calibration in biological samples

Institute of Analytical Chemistry, Faculty of Chemical and Food Technology, Slovak University of Technology, Radlinského 9, SK-812 37 Bratislava,

Acta Chimica Slovaca

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This paper presents rapid and low cost analytical method for the determination of the fluoxetine enantiomeric composition in biological samples (urine). The...

Determination of enantiomeric composition of fluoxetine by synchronous fluorescence spectrometry coupled with multivariate calibration in biological samples

Data publikacji: 08 gru 2016

Zakres stron: 163 - 168

DOI: https://doi.org/10.1515/acs-2016-0028

Słowa kluczowe
chiral analysis, fluoxetine enantiomers, synchronous fluorescence spectrometry, multivariate calibration, biological samples

© 2016 Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Determination of enantiomeric composition of fluoxetine by synchronous fluorescence spectrometry coupled with multivariate calibration in biological samples

Data publikacji: 08 gru 2016

Zakres stron: 163 - 168

DOI: https://doi.org/10.1515/acs-2016-0028

Słowa kluczowechiral analysis, fluoxetine enantiomers, synchronous fluorescence spectrometry, multivariate calibration, biological samples

© 2016 Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
chiral analysis, fluoxetine enantiomers, synchronous fluorescence spectrometry, multivariate calibration, biological samples