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Issue 43, 2017
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Molecularly imprinted polymer-coated paper as a substrate for highly sensitive analysis using paper spray mass spectrometry: quantification of metabolites in urine

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Abstract

Here, we proposed an analytical approach based on the use of a molecularly imprinted polymer-coated paper substrate (MIP-CPS) for paper spray ionization mass spectrometry (PS-MS) to improve its specificity. The new substrate developed was applied to detect and quantify dopamine, sarcosine, and butyric acid in synthetic human urine without derivatization or complex sample pre-treatments. The urinary levels of these metabolites can be correlated with several pathological conditions including heart disease, stress, neurological disorders, cancerous tumors, and AIDS. Calibration curves exhibited R2 > 0.99 for dopamine, sarcosine, and butyric acid. LODs and LOQs were found at μg L−1 (parts-per-billion) for dopamine and sarcosine, and pg L−1 (parts-per-trillion) for butyric acid. Precision and accuracy showed coefficients of variation and relative errors less than 18% for almost all analyses. Recovery test results ranged between 95.5 and 117.7%. Finally, we compared the analytical performance of the MIP-CPS with a traditional paper substrate and ESI. The MIP-CPS showed a superior performance in detecting dopamine and avoiding the ionization suppression commonly observed during the analysis of complex biological samples such as urine.

Graphical abstract: Molecularly imprinted polymer-coated paper as a substrate for highly sensitive analysis using paper spray mass spectrometry: quantification of metabolites in urine

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Publication details

The article was received on 05 Jul 2017, accepted on 22 Aug 2017 and first published on 25 Aug 2017


Article type: Paper
DOI: 10.1039/C7AY01648D
Citation: Anal. Methods, 2017,9, 6117-6123
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    Molecularly imprinted polymer-coated paper as a substrate for highly sensitive analysis using paper spray mass spectrometry: quantification of metabolites in urine

    T. P. P. Mendes, I. Pereira, M. R. Ferreira, A. R. Chaves and B. G. Vaz, Anal. Methods, 2017, 9, 6117
    DOI: 10.1039/C7AY01648D

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