Detection and prediction of concentrations of neurotransmitters using voltammetry and pattern recognition

Neurotransmitters (NTs) are substances in the brain which are responsible for the transmission of neurological impulses. Changes in their concentrations are associated with numerous behavioral and physiological processes and neurological disorders. As opposed to the traditional chromatographic and capillary electrophoresis, using electrochemical sensors is a fast and inexpensive way to determine concentrations of NTs. In this study we measure the combination of dopamine (DA) and serotonin (SE) with glassy carbon electrodes and differential pulse voltammetry. The major challenge using this method is to differentiate between different NTs, since the signal obtained from the electrode represents the interactive effect of both NTs present. We address this problem through methods of pattern recognition which relate the voltammetric measurements provided by the sensor to the concentration of individual NTs. Two methods of pattern recognition were applied (PCR and PLS-regression). The best rates of correct classification for the validation sets ranged at 42-62% (DA) and 33-50% (SE). When the ranges for correct prediction were extended to include one level above and below the true concentration level, the rates values ranged at 81-91% (DA) and 91-100%(SE). These findings suggest that pattern recognition can be used to model the interaction between different neurotransmitters to predict actual concentrations of neurotransmitters using voltammetry.