我们开发了一个结合了表面增强拉曼散射（SERS）和机器学习算法（MLA）的检测平台，用于快速、灵敏地检测变压器油中的糠醛。这对于判断变压器油纸绝缘老化程度至关重要。首先，我们合成了具有尺寸依赖性的银纳米颗粒（AgNPs），并将其应用在镀金膜的多晶硅片Si@Au上，形成Si@Au-Ag SERS基底。通过这个基底，我们成功地对不同浓度的糠醛变压器油溶液进行了检测，并获得了相应的拉曼光谱数据集。接着，我们采用了两种不同的MLA，分别是PCA+ANN和ANN，建立了定量校准曲线，将检测到的拉曼光谱数据转化为对应的糠醛浓度。通过我们建立的模型，相关系数R达到了0.958，表明了模型的高准确性。

We have developed a detection platform that combines Surface-Enhanced Raman Scattering (SERS) with Machine Learning Algorithms (MLA) for the rapid and sensitive detection of furfural in transformer oil. This is crucial for assessing the degree of aging in transformer oil-paper insulation. Firstly, we synthesized silver nanoparticles (AgNPs) with size-dependent properties using a hydrothermal method. These were then spin-coated onto a gold-plated polycrystalline silicon substrate (Si@Au) to form the Si@Au-Ag SERS substrate. With this substrate, we successfully detected furfural in transformer oil solutions of different concentrations, obtaining the corresponding Raman spectroscopic dataset. Subsequently, we employed two different MLAs, namely PCA+ANN and ANN, to construct quantitative calibration curves, enabling the conversion of detected Raman spectroscopic data into corresponding furfural concentrations. The regression model we established achieved a correlation coefficient (R) of 0.958, indicating high accuracy of the model.