Laser-induced breakdown spectroscopy (LIBS) is a potential technology for online coal property analysis, but successful quantitative measurement of calorific value using LIBS suffers from relatively low accuracy caused by the matrix effect. To solve this problem, the support vector machine (SVM) and the partial least square (PLS) were combined to increase the measurement accuracy of calorific value in this study. The combination model utilized SVM to classify coal samples into two groups according to their volatile matter contents to reduce the matrix effect, and then applied PLS to establish calibration models for each sample group respectively. The proposed model was applied to the measurement of calorific values of 53 coal samples, showing that the proposed model could greatly increase accuracy of the measurement of calorific values. Compared with the traditional PLS method, the coefficient of determination ( R 2 ) was improved from 0.93 to 0.97, the root-mean...

中文翻译：

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支持向量机与偏最小二乘相结合的激光诱导击穿光谱法对煤热值定量分析的准确性提高

激光诱导击穿光谱法（LIBS）是在线煤性质分析的一项潜在技术，但是使用LIBS成功定量测量热值会受到基体效应造成的相对较低准确性的困扰。为了解决这个问题，本研究将支持向量机（SVM）和偏最小二乘（PLS）结合起来以提高发热量的测量精度。组合模型利用支持向量机根据煤样中的挥发物含量将其分为两类，以减少基体效应，然后应用PLS分别建立各样组的标定模型。将该模型应用于53个煤样的热值测量，表明该模型可以大大提高热值测量的准确性。