Laser-induced breakdown spectroscopy (LIBS), an emerging elemental analysis technique, provides a fast and low-cost solution for coal characterization without complex sample preparation. However, LIBS spectra contain a large number of uninformative variables, resulting in reduction in the predictive ability and learning speed of a multivariate model. Variable selection based on a single criterion usually leads to a lack of diversity in the selected variables. Coupled with spectral uncertainty in LIBS measurements, this can degrade the reliability and robustness of the multivariate model when analysing spectra obtained at different times and conditions. This work proposes a validated ensemble method for variable selection which uses six base algorithms and combines the returned variable subsets based on the cross-validation results. The proposed method is tested on two sets of LIBS spectra obtained within one month under variable experimental conditions to quantify the properties of coal, including fixed carbon, volatile matter, ash, calorific value and sulphur. The results show that the multivariate model based on the proposed method outperforms those using benchmark variable selection algorithms in six out of the seven tasks by 0.3%–2% in the coefficient of determination for prediction. This study suggests that variable selection based on ensemble learning improves the predictive ability and computational efficiency of the multivariate model in coal property analysis. Moreover, it can be used as a reliable method when the user is not sure which variables to choose in LIBS application.

中文翻译：

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经验证的用于煤性质分析的激光诱导击穿光谱数据的整体变量选择

激光诱导击穿光谱法（LIBS）是一种新兴的元素分析技术，无需复杂的样品制备即可提供快速低成本的煤表征方法。但是，LIBS光谱包含大量的非信息变量，导致多元模型的预测能力和学习速度降低。基于单个标准的变量选择通常会导致所选变量缺乏多样性。加上LIBS测量中的光谱不确定性，当分析在不同时间和条件下获得的光谱时，这可能会降低多元模型的可靠性和鲁棒性。这项工作提出了一种用于变量选择的经过验证的集成方法，该方法使用六种基本算法，并根据交叉验证结果组合了返回的变量子集。该方法在可变的实验条件下，在一个月内获得的两组LIBS光谱上进行了测试，以量化煤的性质，包括固定碳，挥发性物质，灰分，热值和硫。结果表明，基于所提出方法的多元模型在七个任务中的六个任务中，使用基准变量选择算法的预测模型的预测系数要高出0.3％–2％。这项研究表明，基于集成学习的变量选择提高了煤属性分析中多元模型的预测能力和计算效率。此外，当用户不确定在LIBS应用程序中选择哪个变量时，它可以用作可靠的方法。