## 4 Conclusions  
The rapid detection and quantitative analysis of coal quality can guide the adjustment of boiler combustion conditions in real time, which is of great significance to promote the clean and efficient utilization of coal and the energy conservation and CO\({}_{2}\) emission reduction in the thermal power industry. Laser-induced breakdown spectroscopy (LIBS) technology has the characteristics of small sample demand, simple sample preparation, and simultaneous measurement of multiple elements, which has great application potential in the rapid analysis of coal quality. In this paper, the filed-portable laser-induced breakdown spectrometer coupled with three chemometrics methods are used to quantitatively analyze the ash content, volatile content and calorific value of coal commonly used in China power plants. The LIBS data set partitioning methods, random selection (RS), Kennard-Stone (KS), and sample partitioning based on joint X-Y distances (SPXY), and the quantitative models, partial least square regression (PLSR), support vector regression (SVR) and random forest (RF), were combined in this paper to establish the appropriate quantitative model for coal industrial indicator prediction. The model performance and the prediction accuracy of the methods are systematically analyzed and compared. The results show that SPXY data set partitioning combined with the RF model has better prediction performance than that of the PLSR and SVR models coupled with RS and KS methods. The \(R^{2}\) of ash content by the RF and SPXY method is 0.9843, the \(RMSEP\) of ash content is 1.3303 and the mean relative error is 7.47%. The \(R^{2}\) of volatile matter is 0.9801, \(RMSEP\) is 0.7843 and \(MRE\) is 2.19%. The \(R^{2}\) of calorific value is 0.9844, \(RMSEP\) is 0.7324 and \(MRE\) is 2.27%. The study demonstrates that laser breakdown spectroscopy (LIBS) coupled with the sample partitioning method based on joint X-Y distances (SPXY) and random forest (RF) model could be a prosperous way to attain the on-site quick analysis of the ash content, volatile and calorific value. The field-portable laser-induced breakdown spectrometer used in this study coupled with the chemometrics method can rapidly and simultaneously analyze the ash, volatile and calorific value using a single instrument. The key point of the application of this method is the effective data preprocessing and quantitative algorithm optimization which requires a  
Figure 4: The prediction results of ash content, volatile matter and calorific value of coal by RF based on (**a1–a3**) RS; (**b1–b3**) KS; (**c1–c3**) SPXY. large amount of sample testing and training. And the consistency of the sample surface also influences the predicting result, which makes it necessary to keep the surface smooth and consistent for all samples during testing. On this basis, this technology can be used as a real-time and rapid detection method for coal for a variety of occasions, such as coal mines, coal transportation, coal enterprises, etc. Conceptualization, Y.D. and Y.L. ; methodology, Q.W. and X.S. ; software, S.W., M.N. and Y.L. ; writing--original draft preparation, Y.D. ; writing--review and editing, Y.D., Q.W., S.W., X.S., M.N. and Y.L. ; supervision, Y.L. ; project administration, Y.D. ; funding acquisition, Y.D. All authors have read and agreed to the published version of the manuscript. This work was supported by the China National Energy Group Science and Technology Project (Project No. GJNY-22-90) and National Natural Science Foundation of China (Project No. 516761000). Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request. We want to thank all participants and assistants for their support.