## 4 Conclusions  
In this study, LIBS was used to determine the sulfur concentrations and calorific values of blended coals. The PLSR with data processing method helped to reduce the RMSE values and predict the unknown samples. In the RSD calculation, the SG smoothing method showed the lowest value in the largest number of samples and was determined to be an appropriate data processing method. Regarding the relative error, the highest relative error of original data in PLSR for sulfur concentration analysis was 24.61% and this could be reduced to 20.43% as a result of the SG smoothing method. Coal contains a small amount of sulfur compared to other elements; therefore, its concentration is hard to predict accurately and large errors are obtained. Even a slight improvement can be considered to be meaningful in predicting sulfur concentration. In the case of the calorific value analysis, the highest relative error of original data in the PLSR analysis was 2.35% and for the SG smoothing-processed data in the PLSR analysis it was 2.13%. The prediction ability for the unknown samples was evaluated by PLSR analysis. The relative errors in the unknown original sample prediction of sulfur concentration were 3.06% and 4.13%. In addition, the relative errors of the unknown blended sample prediction produced an error value ranging from 5.8 to 14.39%. For the prediction of the calorific value, all relative errors were lower than 3%. When comparing the lowest value found in the reference studies with the largest value of this study, there is only 0.45% difference. The RMSE average values for calorific value measurement were smaller than 1%, which were much smaller than the results of the sulfur concentration measurement. Therefore, this approach will be effective to improve detection ability of the sulfur content in coal for in situ monitoring in a coal-fired power plant. Furthermore, this technique combined with the data processing method is expected to contribute to reducing environmental pollution.  
Conceptualization, J.S.C. and S.J.M.; methodology, J.S.C.; software, J.S.C. and C.M.R.; validation, C.M.R.; formal analysis, J.S.C.; investigation, C.M.R.; data curation, C.M.R.; resources, J.H.C.; funding-acquisition, S.J.M.; writing--original draft preparation, J.S.C.; writing--review and editing, J.H.C. and S.J.M.; visualization, C.M.R.; supervision, S.J.M. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Property** & **Data Process** & **RMSEC Average (\%)** & **RMSECV Average (\%)** \\ \hline \multirow{2}{*}{Calorific value} & Original & 0.7826 & 0.9941 \\  & SG smoothing & 0.7109 & 0.8997 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The RMSE average values of calorific values for the original and SG smoothing-processed data.  
**Funding:** This work was partially supported by the Technology Innovation Program (or Industrial Strategic Technology Development Program-Development of technical support platform for welding material and process) (20017251, Development of laser welding automation system for electric coil joining system to manufacture electrical vehicle motors) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea). This work was partially supported by the National Research Foundation of Korea (NRF) entitled \"Development of Coal Analyzing System Using Laser Induced Breakdown Spectroscopy for Clean Coal Power Plant\" (No. NRF-2016 RID1A1B03935556). This work was also partially supported by the Basic Science Research Program through the National Research Foundation (NRF) of Korea entitled \"NRF-2016 RID1A1B04934910\".  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.