###### Abstract
used NIR spectroscopy to classify and predict ash, volatile matter, moisture, fixed carbon, and calorific value of coal samples by partial least-squares regression [13; 14; 15]. [PERSON] et al. analyzed NIR spectroscopy in 350-2500 nm using a convolutional neural network and achieved an RMSE of 1.75%, 2.27%, and 0.64 J/g for the determination of coal ash, volatile matter, and low heating value, respectively [16]. For coal calorific value, [PERSON] et al. developed two gross calorific value (GCV) prediction models based on NIR spectra and coal properties. Their comparison revealed that the model based on spectral reflectance provided more accurate predictions, with an \(R^{2}\) of 0.92 [17]. In addition, [PERSON] et al. also analyzed the spectral characteristics of coal and conducted a comprehensive prediction of ash, moisture, volatile matter, fixed carbon, and the calorific value of coal [18]. Raman spectroscopy (RS), laser-induced breakdown spectroscopy (LIBS), and Fourier transform infrared (FTIR) spectroscopy have also been used to analyze coal [19; 20; 21]. [PERSON] et al. developed a PLS calibration model based on FTIR to predict the fixed carbon, calorific value, ash, and volatile matter of coal [22]. [PERSON] et al. implemented the proximate and elemental analysis of coal based on LIBS by combining spectral normalization with PLSR and SVR, respectively [23]. Regarding the molecular composition of coal, [PERSON] et al. utilized Raman spectroscopy to analyze the macromolecular structure of coal and its flotation products, which is valuable for predicting the behavior of coal in industrial applications [24]. The analytical performance of these techniques is similar to that of NIR spectroscopy, but all of them still have potential for further improvement. Therefore, new explorations are necessary to achieve more accurate spectral analysis of coal. Moreover, coal quality indicators can be influenced not only by spectral reflectance but also by coal texture. For example, coal with varying ash and volatile matter content often exhibits differences in hardness and color. However, most research has focused on investigating the spectra of coal and has not attempted to develop a coal quality detection model using texture information. Hyperspectral imaging (HSI) has the advantages of simultaneous spectral capture and image acquisition, enabling the expansion of information from spectral to spatial dimensions [25]. HSI can implement the rapid and nondestructive detection of substances and visualize the spatial distribution, which is of great significance for on-line monitoring in many fields, such as industrial production [26] and medical detection [27]. Texture features, as an important representation of spatial information, can be combined with spectral information to achieve more effective object characterization. [PERSON] et al. extracted texture features from wheat stripe rust samples by using a grayscale covariance matrix (GLEM) and constructed a good recognition model [28]. [PERSON] et al. achieved the accurate classification of maize seeds from different years using histogram statistics (HS) and GLEM texture features extracted from hyperspectral images [29]. [PERSON] et al. extracted texture features from reflectance images of bananas using Gabor filters (Gabor), wavelet transforms, and the Tamura method and analyzed their performance in assessing the quality of bananas [30]. However, as far as we know, there is no study that attempts to apply the combined spectral-textural features of hyperspectral data for coal proximate analysis and the visualization of coal quality indicators. In conclusion, near-infrared spectroscopy can be used for the proximate analysis of coal. However, studies on the effect of coal's spatial information on proximate analysis remain insufficient, and the research on combining spectra and texture features for coal analysis has not yet been involved, and has not obtained a high accuracy.