Liu Jun, Wang Ji, Li Xiaopei, Lin Hai, Liu Tiancheng, Zhou Bingyan, He Xiaoyong
Department of Information Science, Zhanjiang Preschool Education College Zhanjiang 524084 Guangdong China.
College of Electronic and Information Engineering, Guangdong Ocean University Zhanjiang 524088 China
RSC Adv. 2025 Jan 16;15(3):1549-1556. doi: 10.1039/d4ra07007k.
This work employs the femtosecond laser-ablation spark-induced breakdown spectroscopy (fs-LA-SIBS) technique for the quantitative analysis of magnesium alloy samples. It integrates four machine learning models: Random Forest (RF), Support Vector Machine (SVM), Partial Least Squares (PLS), and -Nearest Neighbors (KNN) to evaluate their classification performance in identifying magnesium alloys. In regression tasks, the models aim to predict the content of four elements: manganese (Mn), aluminum (Al), zinc (Zn), and nickel (Ni) in the samples. For classification tasks, the models are trained to recognize different types of magnesium alloy samples. Performance evaluation is based on sensitivity, specificity, and accuracy. The results indicate that the RFR model performs optimally for regression tasks, while the Random Forest Classification (RFC) model outperforms other models in classification tasks. This work confirms the feasibility of quantitative analysis and identification of magnesium alloys using the fs-LA-SIBS technique combined with machine learning methods. It establishes a technical foundation for real-time monitoring of alloys in subsequent laser-induced breakdown spectroscopy (LIBS) instruments.
本工作采用飞秒激光烧蚀火花诱导击穿光谱（fs-LA-SIBS）技术对镁合金样品进行定量分析。它集成了四种机器学习模型：随机森林（RF）、支持向量机（SVM）、偏最小二乘法（PLS）和K近邻（KNN），以评估它们在识别镁合金方面的分类性能。在回归任务中，这些模型旨在预测样品中四种元素的含量：锰（Mn）、铝（Al）、锌（Zn）和镍（Ni）。对于分类任务，对模型进行训练以识别不同类型的镁合金样品。性能评估基于灵敏度、特异性和准确性。结果表明，RFR模型在回归任务中表现最佳，而随机森林分类（RFC）模型在分类任务中优于其他模型。本工作证实了结合机器学习方法使用fs-LA-SIBS技术对镁合金进行定量分析和识别的可行性。它为后续激光诱导击穿光谱（LIBS）仪器中合金的实时监测奠定了技术基础。
