Quantitative determination of dopamine in the presence of interfering substances supported by machine learning tools

In the field of neuroscience as well as in the clinical setting, the neurotransmitter dopamine (DA) is an analyte which is important for research as well as medical purposes. There are plenty of methods available to measure dopamine quantitatively, with voltammetric ones such as differential pulse voltammetry (DPV) being among the most convenient and simple ones. However, dopamine often occurs, either naturally or because of the requirements of involved enzymatic systems, alongside substances that can influence the signal it produces upon electrochemical conversion. An example for such substances is the magnesium ion, which itself is not electrochemically active in the potential range needed for DA oxidation, but influences the dopamine signal. We have characterized the properties of DPV signals subject to the interaction between DA and Mg2+ and show that, although these properties are changing in a nonlinear fashion when both concentrations are varying, relatively simple linear mathematical models can be used to determine dopamine concentrations quantitatively in the presence of magnesium ions. The focus of this study is thus, the mathematical treatment of experimental data in order to overcome an analytical problem and not the investigation of the chemical background of DA-Mg2+ interaction.