Jump to main content
Jump to site search


A comparative model combining carbon atomic and molecular emissions based on partial least squares and support vector regression correction for carbon analysis in coal using LIBS

Author affiliations

Abstract

The aim of this study was to analyze the carbon contents in coal samples by laser-induced breakdown spectroscopy (LIBS). The 266 nm laser radiation was utilized for laser ablation and plasma generation under atmospheric conditions. The correlated carbon atomic and molecular emission lines were determined for the variables of the multiple linear regression (MLR) model. Then, the plasma temperatures of different coal samples were compared to characterize the necessity of residue correction from the MLR model. Finally, the partial least squares regression (PLSR) and support vector regression (SVR) were proposed to correct the residue errors of the MLR model. R2, RMSECV, and RMSEP for the MLR model were 0.86%, 3.20%, and 3.41%, whereas these values for the MLR model coupled with the PLSR correction model were 0.99%, 0.13%, and 2.46%, respectively; moreover, these values for the MLR model coupled with the SVR correction model were 0.99%, 0.00039%, and 1.43%. The results showed that the combination of carbon atomic and molecular emissions with both PLSR and SVR correction could improve the measurement accuracy, and the SVR correction helped in achieving better measurement accuracy.

Graphical abstract: A comparative model combining carbon atomic and molecular emissions based on partial least squares and support vector regression correction for carbon analysis in coal using LIBS

Back to tab navigation

Publication details

The article was received on 28 Nov 2018, accepted on 14 Jan 2019 and first published on 23 Jan 2019


Article type: Paper
DOI: 10.1039/C8JA00414E
Citation: J. Anal. At. Spectrom., 2019, Advance Article

  •   Request permissions

    A comparative model combining carbon atomic and molecular emissions based on partial least squares and support vector regression correction for carbon analysis in coal using LIBS

    M. Dong, L. Wei, J. Lu, W. Li, S. Lu, S. Li, C. Liu and J. H. Yoo, J. Anal. At. Spectrom., 2019, Advance Article , DOI: 10.1039/C8JA00414E

Search articles by author

Spotlight

Advertisements