Literature DB >> 25618660

Green analytical determination of emerging pollutants in environmental waters using excitation-emission photoinduced fluorescence data and multivariate calibration.

María Del Carmen Hurtado-Sánchez1, Valeria A Lozano2, María Isabel Rodríguez-Cáceres1, Isabel Durán-Merás1, Graciela M Escandar3.   

Abstract

An eco-friendly strategy for the simultaneous quantification of three emerging pharmaceutical contaminants is presented. The proposed analytical method, which involves photochemically induced fluorescence matrix data combined with second-order chemometric analysis, was used for the determination of carbamazepine, ofloxacin and piroxicam in water samples of different complexity without the need of chromatographic separation. Excitation-emission photoinduced fluorescence matrices were obtained after UV irradiation, and processed with second-order algorithms. Only one of the tested algorithms was able to overcome the strong spectral overlapping among the studied pollutants and allowed their successful quantitation in very interferent media. The method sensitivity in superficial and underground water samples was enhanced by a simple solid-phase extraction with C18 membranes, which was successful for the extraction/preconcentration of the pollutants at trace levels. Detection limits in preconcentrated (1:125) real water samples ranged from 0.04 to 0.3 ng mL(-1). Relative prediction errors around 10% were achieved. The proposed strategy is significantly simpler and greener than liquid chromatography-mass spectrometry methods, without compromising the analytical quality of the results.
Copyright © 2014 Elsevier B.V. All rights reserved.

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Keywords:  Emerging pollutants; Photoinduced fluorescence; Unfolded partial least-squares/residual bilinearization; Water samples

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Year:  2014        PMID: 25618660     DOI: 10.1016/j.talanta.2014.11.022

Source DB:  PubMed          Journal:  Talanta        ISSN: 0039-9140            Impact factor:   6.057


  1 in total

1.  Comparison between Two Linear Supervised Learning Machines' Methods with Principle Component Based Methods for the Spectrofluorimetric Determination of Agomelatine and Its Degradants.

Authors:  Mahmoud M Elkhoudary; Ibrahim A Naguib; Randa A Abdel Salam; Ghada M Hadad
Journal:  J Fluoresc       Date:  2017-03-01       Impact factor: 2.217

  1 in total

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