Yan Jiujiang, Hao Zhongqi, Zhou Ran, Tang Yun, Yang Ping, Liu Kun, Zhang Wen, Li Xiangyou, Lu Yongfeng, Zeng Xiaoyan
Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology (HUST), Wuhan, Hubei, 430074, PR China.
Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology (HUST), Wuhan, Hubei, 430074, PR China.
Anal Chim Acta. 2019 Nov 15;1082:30-36. doi: 10.1016/j.aca.2019.07.058. Epub 2019 Aug 2.
The determination accuracy of alloying elements in high alloy steel is generally poor in laser-induced breakdown spectroscopy (LIBS) due to their matrix effect. To solve this problem, an image quantitative analysis (IQA) method was proposed and verified by determining nickel (Ni) in 17 stainless steel samples in this work. The results showed that the coefficient of determination (R) was increased from 0.9833 of a conventional spectrum quantitative analysis (SQA) method to 0.9996 of the IQA method, and the average relative error of cross-validation (ARECV) and root mean squared error of cross-validation (RMSECV) were decreased from 56.80% and 1.0818 wt% to 15.93% and 0.9866 wt%, respectively. Besides, the determinations of chromium (Cr) and silicon (Si) demonstrated the generalization ability of the IQA. This study provides an effective approach to improving the quantitative performance of LIBS through the combination of image processing and computer vision technology.
由于基体效应，激光诱导击穿光谱法（LIBS）中高合金钢中合金元素的测定精度普遍较差。为了解决这个问题，本文提出了一种图像定量分析（IQA）方法，并通过测定17个不锈钢样品中的镍（Ni）进行了验证。结果表明，测定系数（R）从传统光谱定量分析（SQA）方法的0.9833提高到了IQA方法的0.9996，交叉验证的平均相对误差（ARECV）和交叉验证的均方根误差（RMSECV）分别从56.80%和1.0818 wt%降至15.93%和0.9866 wt%。此外，铬（Cr）和硅（Si）的测定证明了IQA的泛化能力。本研究提供了一种通过图像处理和计算机视觉技术相结合来提高LIBS定量性能的有效方法。
