Strategies to Enrich Electrochemical Sensing Data with Analytical Relevance for Machine Learning Applications: A...

Area of Science:

• Electrochemistry
• Machine Learning
• Analytical Chemistry

Background:

• Electrochemical data is rich but challenging for machine learning (ML) due to complex matrices.
• Sensors often struggle to identify or quantify targets amidst non-target substances.
• Advanced ML techniques require robust, varied electrochemical data for effective application.

Purpose of the Study:

• To review recent advances in integrating ML with electrochemical analysis.
• To highlight strategies for enhancing the analytical context of electrochemical data.
• To inspire researchers in leveraging ML for electroanalytical science.

Main Methods:

• Discussed five strategies for building electrochemical systems for ML.
• Covered electrode preparation, signal recording, and data analysis methods.
• Explored approaches for acquiring and augmenting datasets for ML models.

Main Results:

• Identified key strategies to improve electrochemical data quality for ML.
• Demonstrated methods to ensure data variability across targets and concentrations.
• Provided insights into dataset preparation for training and validation.

Conclusions:

• Effective integration of ML in electroanalysis requires optimized electrochemical systems.
• Developing methods for generating high-quality, varied data is crucial for ML success.
• This review offers a roadmap for advancing ML applications in electroanalytical science.