Artificial Neural Networks for the Resolution of Dopamine and Serotonin Complex Mixtures Using a Graphene-Modified Carbon Electrode

Marta Bonet-San-Emeterio, Andreu González-Calabuig, Manel del Valle

Research output: Contribution to journalArticleResearch

9 Citations (Scopus)
1 Downloads (Pure)

Abstract

This work explores an electrode modified with electrochemically reduced graphene oxide (ERGO) for the voltammetric resolution of mixtures of neurotransmitters and its most common interferents. This enhanced sensitivity device coupled with advanced chemometric tools, such as artificial neural networks (ANNs), is able to resolve and quantify complex mixtures with overlapped signals. In this case, it has been applied to dopamine (DA), serotonin (5-hydroxytryptamine, 5-HT) and its main physiologic interferents, ascorbic acid (AA) and uric acid (UA), which play a relevant role in human body. The results obtained for individual analysis make evident a higher sensitivity of the developed sensor than the unmodified electrode. Furthermore, it has been attained an ANN response model with good correlation ability allowing the separation and quantification of each compound with comparison slope of predicted vs. expected concentrations with correlation better than 0.974. In short, the developed ERGO-modified sensor not only improved the signal but it also permitted resolving and quantifying each compound in complex mixtures when the proper chemometric treatment was used.
Original languageEnglish
Pages (from-to)390-397
Number of pages8
JournalElectroanalysis
Volume31
Issue number2
Publication statusPublished - 1 Feb 2019

Keywords

  • Graphene
  • chemometrics
  • dopamine
  • serotonin
  • voltammetry.

Fingerprint

Dive into the research topics of 'Artificial Neural Networks for the Resolution of Dopamine and Serotonin Complex Mixtures Using a Graphene-Modified Carbon Electrode'. Together they form a unique fingerprint.

Cite this