It is a challenge task to enhance the analysis accuracy of laser-induced breakdown spectroscopy (LIBS) in high temperature applications when certified standard samples used for building calibration curves at high temperature are limited or not available. A novel LIBS quantitative analysis method for alloy steel at high temperature via feature-based transfer learning (FTL) is proposed. The spectral data of calibration samples at room temperature and the spectral data of uncalibrated samples at high temperature are together transferred into a high-dimensional feature space using kernel function mapping where an LIBS regression model is trained and established. For testing samples, the measured spectra at high temperature are mapped into the high-dimensional feature space with the same kernel parameters used in the training process, and then the concentration results can be obtained by the regression model. Experiments on certified alloy steel standard samples were conducted, in which 12 samples with both the concentration information and the measured spectra at room temperature and 8 samples only with the spectra measured at high temperature were used to train the analysis model. The 8 samples at high temperature were used for testing. The experimental results of the Cr concentration showed that with feature-based transfer learning, the mean relative error decreased from 32.31% to 6.08%. The proposed method does not need the element concentration for samples at high temperature to build the regression model, which provides a feasible and effective approach for LIBS analysis of samples at high temperature, such as fast industrial measurements in iron and steel smelting production processes.

中文翻译：

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基于高温合金钢样品中铬的基于特征的基于传递特征的传递学习LIBS分析的性能评估

当用于高温下建立校准曲线的认证标准样品有限或不可用时，如何提高高温应用中的激光诱导击穿光谱法（LIBS）的分析精度是一项艰巨的任务。一种新颖的在高温下LIBS定量分析方法，用于合金钢通过提出了基于特征的转移学习（FTL）。使用核函数映射在其中训练和建立LIBS回归模型的核函数映射，将室温下校准样品的光谱数据和高温下未校准样品的光谱数据一起传输到高维特征空间。对于测试样品，将在高温下测得的光谱映射到高维特征空间，并使用与训练过程中相同的内核参数，然后可以通过回归模型获得浓度结果。对认证的合金钢标准样品进行了实验，其中12个样品在室温下均具有浓度信息和实测光谱，而8个样品仅具有高温下测得的光谱来训练分析模型。将8个高温样品用于测试。Cr浓度的实验结果表明，基于特征的转移学习使平均相对误差从32.31％降低到6.08％。所提出的方法不需要建立高温样品中的元素浓度来建立回归模型，为高温LIBS分析样品提供了一种可行而有效的方法，例如钢铁冶炼生产过程中的快速工业测量。