A narrative review of machine learning integrated electrochemical sensors for smart environmental monitoring
TL;DR
A detailed explanation of ML-based electrochemical sensors and their advantages and applications in environmental monitoring and the advantages of ML integration with nanomaterials and the Internet of Things are provided.
Abstract
In the recent era, industrialization, urbanization, and unethical agricultural practices have caused environmental degradation and therefore, there is an utmost need for fast and efficient monitoring systems. The solution to the above is electrochemical sensors; these are one of the useful analytical tools used for environmental monitoring because of their high efficiency, sensitivity, less tedious, cost effective. However, these electrochemical sensors can experience signal noise, interference peaks from complex mixtures of analytes, and interpretation problems, especially with nonlinear electrochemical responses. In recent years, machine learning (ML) has become one of the most groundbreaking methods to improve the performance of electrochemical sensors by making it easy for complex signal processing, pattern recognition, and highly accurate modelling. Combining ML with electrochemical sensors enables advanced environmental monitoring with real-time analysis, simultaneous determination of multiple analytes, and improved selectivity and sensitivity. This review provides a detailed explanation of ML-based electrochemical sensors and their advantages and applications in environmental monitoring. This article also discusses the advantages of ML integration with nanomaterials and the Internet of Things for determining toxic dyes, pesticides, heavy metals, etc.