Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand.
Electrochemistry and Optical Spectroscopy Center of Excellence (EOSCE), Chulalongkorn University, Bangkok 10330, Thailand.
Analyst. 2021 Oct 25;146(21):6351-6364. doi: 10.1039/d1an01148k.
Electrochemical sensors and biosensors have been successfully used in a wide range of applications, but systematic optimization and nonlinear relationships have been compromised for electrode fabrication and data analysis. Machine learning and experimental designs are chemometric tools that have been proved to be useful in method development and data analysis. This minireview summarizes recent applications of machine learning and experimental designs in electroanalytical chemistry. First, experimental designs, , full factorial, central composite, and Box-Behnken are discussed as systematic approaches to optimize electrode fabrication to consider the effects from individual variables and their interactions. Then, the principles of machine learning algorithms, including linear and logistic regressions, neural network, and support vector machine, are introduced. These machine learning models have been implemented to extract complex relationships between chemical structures and their electrochemical properties and to analyze complicated electrochemical data to improve calibration and analyte classification, such as in electronic tongues. Lastly, the future of machine learning and experimental designs in electrochemical sensors is outlined. These chemometric strategies will accelerate the development and enhance the performance of electrochemical devices for point-of-care diagnostics and commercialization.
电化学传感器和生物传感器已成功应用于广泛的领域，但电极制备和数据分析的系统优化和非线性关系受到了限制。机器学习和实验设计是已被证明在方法开发和数据分析中有用的化学计量学工具。这篇综述总结了机器学习和实验设计在分析电化学中的最新应用。首先，讨论了实验设计，如全因子、中心复合和 Box-Behnken，作为系统的方法来优化电极制备，以考虑单个变量及其相互作用的影响。然后，介绍了机器学习算法的原理，包括线性和逻辑回归、神经网络和支持向量机。这些机器学习模型已被用于提取化学结构与其电化学性质之间的复杂关系，并分析复杂的电化学数据，以提高校准和分析物分类的能力，例如在电子舌中。最后，概述了机器学习和实验设计在电化学传感器中的未来。这些化学计量学策略将加速电化学器件的开发，并提高其在即时诊断和商业化方面的性能。
