###### Abstract
They concluded that the non-uniformity of the sample during the interaction between the laser and coal sample and the pyrolysis and combustion of coal were the main reasons for the large spectral fluctuation and poor calibration quality. The results showed that compared with the full-spectral region normalization method, the segmented spectral region normalization method improves the measurement accuracy. A partial least squares regression (PLSR) model combined with dominant factors was used to calculate the carbon content in coal, which reduced the root mean square error of prediction (RMSEP) of the partial least squares model from 5.52% to 4.47%. [PERSON] et al. [12] developed a rapid coal quality analyzer by the measurement of pulverized coal particle flow using LIBS, including pulverized coal feed module, spectral measurement module, and control module. The equipment combines an artificial neural network and genetic algorithm to predict the proximate analysis and total calorific value of pulverized coal. The results showed that the measurement accuracy of ash, volatile, fixed carbon, and total calorific value can meet the accuracy requirements of neutron-activated coal online analyzer. They [13] also used the PLSR model to analyze the volatile content, and calorific value results by using LIBS technology and Fourier transform infrared (FTIR) technology. Although the coefficient of determination (R\({}^{2}\)) value of LIBS assisted with FTIR improved little, it significantly improved the RMSEP, means absolute error (MAE), and mean relative error (MRE). The result showed that the combination of LIBS and FTIR can improve the prediction results for volatile content and calorific value, which obtain benefit from the synergy of atomic and molecular information from LIBS and FTIR spectra, respectively. In recent years, machine learning has been widely used in clustering [14], classification [15], and quantitation work with different mathematical principles. For the clustering model, [PERSON] et al. [16] used a combination of principal component analysis (PCA) and K-means clustering to classify coal into different categories. PCA was used to reduce the input variables for LIBS spectra into two-dimensional principal components, which could represent more than 90% of the variance information of the original spectra. Then, K-means clustering was established after PCA, and the accuracy of classification was 92.59%. The results showed that K-means clustering divides coal into different categories by distance and clustering centers. K-means has high separation requirements for sample clusters, which makes it challenging to establish complex boundaries between data categories. For the classification model, [PERSON] et al. [16] used PCA first to conduct the dimensional reduction, and then to classify 54 coal samples by using linear partial least squares discriminant analysis (PLS-DA) classification and nonlinear support vector machine (SVM) classification model with a radial basis kernel function. The results showed that the training set and correction set accuracy of PLS-DA were 97.22% and 99.07%, respectively, and the accuracy of the training set and test set of SVM was 99.07% and 88.89%, respectively. For quantification models, [PERSON] et al. [17] used various regression methods for the ash, volatile content, and heat values of 54 coals, including PLSR, support vector regression (SVR), artificial neural network (ANN), and principal component regression (PCR). They concluded that ANN had the best prediction ability and model efficiency, whose coefficient of determination of the training set on three items was more than 0.99 with a small RMSEP. Although the SVR performed better than ANN in the ash training set, its training time was much longer than ANN. Hence, ANN was more suitable for rapid, online, and in situ coal quality measurement, which provided a reference for selecting quantitation methods for LIBS application.