Zhang Yuqing, Sun Chen, Yue Zengqi, Shabbir Sahar, Xu Weijie, Wu Mengting, Zou Long, Tan Yongqi, Chen Fengye, Yu Jin
Opt Express. 2020 Oct 12;28(21):32019-32032. doi: 10.1364/OE.404722.
As any spectrochemical analysis method, laser-induced breakdown spectroscopy (LIBS) usually relates characteristic spectral lines of the elements or molecules to be analyzed to their concentrations in a material. It is however not always possible for a given application scenario, to rely on such lines because of various practical limitations as well as physical perturbations in the spectrum excitation and recording process. This is actually the case for determination of carbon in steel with LIBS operated in the ambient gas, where the intense C I 193.090 nm VUV line is absorbed, while the C I 247.856 nm near UV one heavily interferes with iron lines. This work uses machine learning, especially a combination of least absolute shrinkage and selection operator (LASSO) for spectral feature selection and back-propagation neural networks (BPNN) for regression, to correlate a LIBS spectrum to the carbon concentration for its precise determination without explicitly including carbon-related emission lines in the selected spectral features.
作为任何一种光谱化学分析方法，激光诱导击穿光谱法（LIBS）通常将待分析元素或分子的特征光谱线与其在材料中的浓度联系起来。然而，对于给定的应用场景，由于各种实际限制以及光谱激发和记录过程中的物理扰动，并不总是能够依赖这些谱线。在环境气体中运行的LIBS测定钢中的碳时，实际情况就是如此，其中强烈的C I 193.090 nm真空紫外谱线被吸收，而近紫外的C I 247.856 nm谱线则与铁谱线严重干扰。这项工作使用机器学习，特别是结合最小绝对收缩和选择算子（LASSO）进行光谱特征选择以及反向传播神经网络（BPNN）进行回归，将LIBS光谱与碳浓度相关联，以便在所选光谱特征中不明确包含与碳相关的发射线的情况下精确测定碳浓度。
