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.

中文翻译：

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使用简单的电化学传感器结合机器学习智能分析马来酰肼

成功设计了一种基于低成本一次性激光诱导多孔石墨烯 (LIPG) 柔性电极的简单电化学传感平台，用于结合机器学习 (ML) 智能分析马铃薯和花生中的马来酰肼 (MH)。LIPG 电极使用计算机控制的直接激光写入微加工系统通过简单的一步激光诱导程序在商用聚酰亚胺薄膜上图案化，并显示出优异的柔韧性、3D 多孔结构、大比表面积和良好的导电性。通过选择数据集的大小，包括训练集的大小和测试集的大小，结合 ML 模型的性能指标，为最佳 MH 浓度范围提出了数据分区技术。选择不同的算法，例如人工神经网络 (ANN)、随机森林 (RF) 和最小二乘支持向量机 (LS-SVM) 来构建 ML 模型。评估了三个 ML 模型，LS-SVM 模型显示出独特的优势。进一步测量了实际应用的回收率和 RSD，以评估所选 LS-SVM 模型的可行性。这对于利用电化学传感平台对农产品安全中的有害残留进行智能分析具有重要的理论和实践意义。进一步测量了实际应用的回收率和 RSD，以评估所选 LS-SVM 模型的可行性。这对于利用电化学传感平台对农产品安全中的有害残留进行智能分析具有重要的理论和实践意义。进一步测量了实际应用的回收率和 RSD，以评估所选 LS-SVM 模型的可行性。这对于利用电化学传感平台对农产品安全中的有害残留进行智能分析具有重要的理论和实践意义。