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The carbon and sulfur content is an important index of coal property. In the present work, kernel-based extreme learning machine (K-ELM) model was built and applied to laser induced breakdown spectroscopy (LIBS) to improve the quantitative analysis accuracy of carbon and sulfur in coal. The different preprocessing techniques, input variables and model parameters were optimized by 5-fold cross validation...
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