Issue 43, 2017

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

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

Supplementary files

Article information

Article type
Paper
Submitted
11 شوال 1438
Accepted
30 ذو القعدة 1438
First published
03 ذو الحجة 1438

Anal. Methods, 2017,9, 6117-6123

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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