Abstract：
Accurate detection of dissolved furfural in transformer oil is crucial for real-time monitoring of the aging state of transformer oil-paper insulation. While label-free surface-enhanced Raman spectroscopy (SERS) has demonstrated high sensitivity for dissolved furfural in transformer oil, challenges persist due to poor substrate consistency and low quantitative reliability. Herein, machine learning (ML) algorithms were employed in both substrate fabrication and spectral analysis of label-free SERS. Initially, a high-consistency Ag@Au substrate was prepared through a combination of experiments, particle swarm optimization-neural network (PSO-NN), and a hybrid strategy of particle swarm optimization and genetic algorithm (Hybrid PSO-GA). Notably, a two-step ML framework was proposed, whose operational mechanism is classification followed by quantification. The framework adopts a hierarchical modeling strategy, incorporating simple algorithms such as kernel support vector machine (Kernel-SVM), k-nearest neighbors (KNN), etc., to independently establish lightweight regression models on each cluster, which allows each model to focus more effectively on fitting the data within its cluster. The classification model achieved an accuracy of 100%, while the regression models exhibited an average correlation coefficient (R2) of 0.9953 and the root mean square errors (RMSE) consistently below 10-2. Thus, this ML framework emerges as a rapid and reliable method for detecting dissolved furfural in transformer oil, even in the presence of different interfering substances, which may also have potentiality for other complex mixture monitoring systems.
摘要：
变压器油中溶解糠醛的准确检测对于实时监测变压器油纸绝缘的老化状态至关重要。尽管无标记表面增强拉曼光谱（SERS）对变压器油中溶解的糠醛具有很高的灵敏度，由于基板一致性差和定量可靠性低，挑战依然存在。在这里，机器学习（ML）算法用于无标记SERS的基板制造和光谱分析。最初，通过实验组合制备了一种高稠度的Ag@Au衬底，粒子群优化神经网络(PSO-NN)，粒子群算法和遗传算法的混合策略(HybridPSO-GA)。值得注意的是,提出了一个两步机器学习框架，其运行机制是分类，然后是量化。该框架采用分层建模策略,结合了简单的算法，如核支持向量机(Kernel-SVM)，k-最近邻(KNN)，等。,在每个集群上独立建立轻量级回归模型，这允许每个模型更有效地专注于拟合其群集中的数据。分类模型达到了100%的准确率,而回归模型的平均相关系数（R2）为0.9953，均方根误差（RMSE）始终低于10-2。因此，这种ML框架是一种快速可靠的检测变压器油中溶解糠醛的方法，即使存在不同的干扰物质，这也可能有其他复杂的混合监测系统的潜力。
