Application of machine learning to improve the accuracy of electrochemical sensors: A review

Abstract

Electrochemical sensors are devices that convert chemical signals into electrical signals, having been widely applied in various fields. However, traditional electrochemical sensors are prone to interference from various issues, which leads to inaccurate measurement results. The development of artificial intelligence (AI) technologies offers new approaches to address these issues. Among these, machine learning (ML) techniques can analyze large volumes of sensor data, identify complex patterns and relationships, and thereby enhance the accuracy and stability of sensors. This paper provides a review of the latest research over the past five years on ML applications addressing challenges such as nonlinear sensor signal relationships, low-concentration accuracy, signal drift, and interference resistance. It also summarizes the performance of various algorithms in different application scenarios. Finally, the paper discusses the challenges faced by ML technologies in improving the accuracy of electrochemical sensors and outlines future development directions.