## 4 Discussion  
Coal is a complex, inhomogeneous mixture containing both organic and inorganic matters. The proximate analysis of coal is directly related to its quality which has an important role in managing the coal economy, ensuring environmental safety, and promoting resource reutilization. For example, volatile matter, which measures the substances released during the heating process of coal, is composed of gases, low-boiling organic compounds, and tars. This measure reflects the organic compound content in coal [60]. A few volatile organic compounds, such as benzene, toluene, and ethylbenzene, generated from coal combustion, present a significant threat to environment safety and human health due to their toxicity and carcinogenicity [61; 62]. In addition, the calorific value, as a measure of  
Figure 10: Predictive distribution of coal quality indices by combined spectra-textual feature based on hyperspectral images. The four samples in each index correspond to the minimum, 25%, 75%, and maximum values in the dataset from left to right, respectively. coal combustion performance, is critical for the power energy supply and also serves as the primary reference in coal trading. This is particularly relevant for China and India, the two largest coal consumers worldwide, which generated more than 50 percent of their electricity from coal in 2022 [63]. The ash index, which indicates the volume of coal remaining after combustion, is widely used as a waste material in building filling and road repair [64]. Overall, the analysis of coal quality is of considerable importance. Previous studies have primarily focused on analyzing coal spectral data to achieve proximate analysis of coal quality [13; 14; 15; 16; 65]. These studies typically utilize the spectral reflectance of coal obtained from point-based spectrometers, such as the SVC or ASD series [16; 18]. Based on the spectral data only, the spectral characteristics of materials related to coal quality indicators have been clarified, but this seems to present a limitation in the further enhancement of the coal proximate analysis. However, the variations in coal quality indices can also alter the physical properties of coal, which may produce different textural information in coal images [57; 58; 59]. Therefore, this study examines the impact of the texture features of coal images on coal quality indicators to enhance the prediction accuracy of hyperspectral-based models. The LSSVR model using combined spectral-textural features performed best in predicting coal quality indices, with root-mean-square errors (RMSEs) of 0.112%, 0.659%, 0.583%, 0.838%, and 0.217 MJ/kg for moisture, ash, volatile matter, fixed carbon, and calorific value, respectively. Meanwhile, the relative prediction deviations (RPDs) of all models were much greater than 3, which demonstrated the excellent predictive ability [53]. The results of other work related to this study should be thoroughly analyzed to compare the performance of coal quality indices prediction based on the hyperspectral images and other spectral data. [PERSON] et al. applied VNIR spectroscopy (400-2500 nm) and PLSR, RF, and XGBoost models to predict the quality indices of 78 coal samples with particle sizes of less than 0.1 mm. The RF regression achieved optimal RMSET of 0.39%, 4.82%, 3.94%, and 2.73 MJ/kg for moisture, ash, volatile matter, and calorific value [31]. The study highlights the modeling advantages of RF, but the predictions performance needs to be further improved compared with our results. [PERSON] et al. implemented the coal quality indices prediction by converting one-dimensional VNIR spectra (350-2500 nm) into two-dimensional data and utilizing a convolutional neural network and a limit learning machine model (DR_TELM). The predicted RMSET of DR_TELM for moisture, ash, volatile matter, fixed carbon, and calorific value were 0.533%, 1.833%. 1.111%, 1.808%, and 0.723 MJ/kg, respectively [66].