## 5 Conclusions  
In this study, a fast and nondestructive proximate analysis method for coal based on hyperspectral images is proposed. The SPA, Boruta, iVISSA, and CARS methods are applied to optimize the spectral feature wavelengths based on the MSC spectra. The optimal spectral information-based model for each metric was constructed and evaluated by applying PLSR, RF, BPNN, and LSSVR algorithms. The results show that the CARS-LSSVR model is superior in the prediction of coal quality indices. In the spatial information, the Gabor filtering method and histogram statistics can effectively extract the texture information from the characteristic wavelengths to establish the optimal spatial information-based model. Finally, the optimal prediction model of coal quality indices was constructed by combining the spectral feature wavelengths and texture feature information using the CARS-LSSVR approach. The prediction accuracies were as follows: Ash (\(R_{p}^{2}\) = 0.993, \(RMSE_{p}\) = 0.659%, \(RPD\) = 12.217), VM (\(R_{p}^{2}\) = 0.989, \(RMSE_{p}\) = 0.583%, \(RPD\) = 9.904), MC (\(R_{p}^{2}\) = 0.979, \(RMSE_{p}\) = 0.112%, \(RPD\) = 7.159), FC (\(R_{p}^{2}\) = 0.948, \(RMSE_{p}\) = 0.838%, \(RPD\) = 4.544), and CV (\(R_{p}^{2}\) = 0.994, \(RMSE_{p}\) = 0.217 MJ/kg, \(RPD\) = 13.849). The \(R_{p}^{2}\) values of the optimal model based on the combined spectral-texture features were improved by 0.009, 0.018, 0.207, 0.039, and 0.003, respectively, as compared with the spectral features. Meanwhile, comparing with the texture features, the \(R_{p}^{2}\) values were enhanced by 0.182, 0.062, 0.022, 0.434, and 0.375, respectively. In addition, the contributions of spectral and texture features to the model were briefly analyzed by SHAP and suggestions were provided. The results of this study demonstrate that the texture features in coal images can enhance the model performance for predicting coal quality indices. Machine learning coupled with combined spectral-texture features from hyperspectral data can be used to accurately determine coal quality indices, which provides a technical reference for further rapid nondestructive proximate analysis and quality assessment of coal. Future research will focus on expanding the coal sample size and applying deep learning networks to automatically extract hyperspectral features for constructing end-to-end coal quality indicator detection models. The effectiveness of the hyperspectral coal quality determination technique will also be tested in practical application scenarios, such as thermal power plants and coal chemical plants, beyond laboratory settings. Conceptualization, J.M. and H.Z. ; methodology, H.Z., J.M., and P.W. ; software, J.M. and Y.X. ; validation, J.M., H.Z., and M.W. ; writing--original draft preparation, J.M. ; writing--review and editing, H.Z., J.M., Y.Z., Y.S., and Y.X. ; visualization, J.M. ; project administration, H.Z. ; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China Science Foundation Project (41701488), the Open Research Fund of The State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, CUMT (SKLCRSM24 KF011), the China University of Mining and Technology (Beijing) LongRuan Technology Fund Student Innovation & Enterprise Program (XD2023016004) and the Doctoral Innovative Talents Cultivation Project at China University of Mining and Technology (Beijing) (BBJ2023025). Not applicable. The data used to support the findings of this study are available from the corresponding author upon request. * [PERSON] et al. (2020) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Reflectance spectroscopy based rapid determination of coal quality parameters. _Fuel_**2020**, _280_, 118676. [CrossRef]
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