Integral methods for automatic quantification of fast-scan-cyclic-voltammetry detected neurotransmitters

Area of Science:

• Electrochemistry
• Neuroscience
• Analytical Chemistry

Background:

• Basal levels of electroactive neurotransmitters are estimated using oxidative charge measurements.
• Current methods rely on manual integration interval selection, introducing errors and limiting automation.
• Automated quantification is crucial for analyzing large datasets in neurotransmitter research.

Purpose of the Study:

• To develop novel, automated methods for selecting integration boundaries in oxidative current measurements.
• To improve the accuracy and efficiency of quantifying electroactive neurotransmitters.

Main Methods:

• Development of algorithms for automatic identification of integration boundaries.
• Application of these methods to electrochemical data from in vitro and in vivo experiments.

Main Results:

• The novel methods successfully quantify oxidation reactions automatically.
• Accurate quantification was achieved for both in vitro and in vivo samples.
• The methods demonstrated capability in analyzing multiple analytes simultaneously in vitro.

Conclusions:

• Automated boundary selection significantly enhances the accuracy and reliability of electroactive neurotransmitter quantification.
• These methods overcome limitations of manual peak identification, enabling high-throughput analysis.
• The developed techniques are applicable to diverse electrochemical studies involving oxidation reactions.