Intelligent analysis of maleic hydrazide using a simple electrochemical sensor coupled with machine learning
Abstract
A simple electrochemical sensing platform based on a low-cost disposable laser-induced porous graphene (LIPG) flexible electrode for the intelligent analysis of maleic hydrazide (MH) in potatoes and peanuts coupled with machine learning (ML) was successfully designed. The LIPG electrode was patterned by a simple one-step laser-induced procedure on commercial polyimide film using a computer-controlled direct laser writing micromachining system and displayed excellent flexibility, 3D porous structure, large specific surface area, and preferable conductivity. A data partitioning technique was proposed for the optimal MH concentration ranges by selecting the size of datasets, including the size of the training set and the size of the test set combined with the performance metrics of ML models. Different algorithms such as artificial neural networks (ANN), random forest (RF), and least squares support vector machine (LS-SVM) were selected to build the ML models. Three ML models were evaluated, and the LS-SVM model displayed unique superiority. Both the recoveries and RSD of practical application were further measured to assess the feasibility of the selected LS-SVM model. This will have important theoretical and practical significance for the intelligent analysis of harmful residuals in agro-product safety using an electrochemical sensing platform.
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