## 1 Introduction
However, data processing methods were not used to detect and improve the sulfur emission lines in their study. From recent studies, it is possible to directly measure the composition of coal using LIBS as well as elemental analysis. [PERSON] et al. [10] attempted to employ multivariate analysis to extract coal ash content information from LIBS spectra rather than from the concentrations of the main ash-forming elements. [PERSON] et al. [11] have made efforts to measure ash in real time by using LIBS. [PERSON] et al. [12] tested the analytical methods with partial correlation and principal component regression to extract the correlation between the amount of volatile matter and the LIBS spectral information based on coal structure. [PERSON] et al. [13] applied the multivariate dominant factor based on the PLS model to demonstrate an overall improvement in performance compared with the conventional PLS model for various coal properties such as ash content, volatile matter content, and calorific value. In spite of these previous research studies on coal calorific values, there still remains different perspective issues to be resolved. As more coal-fired power plants in Korea use up to four types of coal blending to cut costs; quantitative analysis of blended coal calorific values is needed. If only one type of coal is used in the coal-fired power plant, pre-analyzed data values can be used. However, using blended coal makes it difficult to analyze in real-time. Moreover, if the coal samples are not uniformly mixed in the conveyor system, heterogeneous distribution of sulfur can occur. The prediction of sulfur concentration by LIBS in conjunction with PLSR, especially for blended coal samples, is the main concern of this work. From other previous research, two challenges which can be major obstacles in detecting sulfur emission lines with LIBS have been identified [14; 15]. The most important challenge is that the spectrometer of the LIBS system used in this study can only detect from 200 to 800 nm spectral range. The strongest intensity of sulfur emission lines usually can be detectedbeyond this limit, at 125-180 nm (vacuum ultraviolet range) [16] and longer than 900 nm (near-infrared range) [17]. When using LIBS, the 125-180 nm region cannot be measured, and the measurement accuracy is low in the region above 900 nm [9]. To overcome this problem, the data quality of weak sulfur ionic emission lines in the 400-600 nm region needs to be enhanced. Therefore, to determine the proper data processing method, two kinds of methods were investigated. These two methods have been employed in NIR spectroscopy studies but rarely in LIBS studies. In this study, a reduction in the prediction error for sulfur analysis will be attempted by applying an appropriate data processing method with LIBS. The calorific values will be analyzed without using Dulong's equation. Ten original coal samples were blended with varying blending ratio. Therefore, sixty blended coal samples were used to construct the regression model in this work. Furthermore, one kind of coal sample that was not applied to consist of the calibration line was verified with our method utilized to estimate the sulfur concentration and calorific value of unknown samples. The blended samples mixed with the unknown original sample were investigated for verification.