###### Abstract  
Proximate analysis, including ash, volatile matter, moisture, fixed carbon, and calorific value, is a fundamental aspect of fuel testing and serves as the primary method for evaluating coal quality, which is critical for the processing and utilization of coal. The traditional analytical methods involve time-consuming and costly combustion processes, particularly when applied to large volumes of coal that need to be sampled in massive batches. Hyperspectral imaging is promising for the rapid and nondestructive determination of coal quality indices. In this study, a fast and nondestructive coal proximate analysis method with combined spectral-spatial features was developed using a hyperspectral imaging system in the 450-2500 nm range. The processed spectra were evaluated using PLSR, with the most effective MSC spectra selected. To reduce the spectral redundancy and improve the accuracy, the SPA, Boruta, iVISSA, and CARS algorithms were adopted to extract the characteristic wavelengths, and 16 prediction models were constructed and optimized based on the PLSR, RF, BPNN, and LSSVR algorithms within the Optuna framework for each quality indicator. For spatial information, the histogram statistics, gray-level covariance matrix, and Gabor filters were employed to extract the texture features within the characteristic wavelengths. The texture feature-based and combined spectral-texture feature-based prediction models were constructed by applying the spectral modeling strategy, respectively. Compared with the models based on spectral or texture features only, the LSSVR models with combined spectral-texture features achieved the highest prediction accuracy in all quality metrics, with \(R_{p}^{2}\) values of 0.993, 0.989, 0.979, 0.948, and 0.994 for Ash, VM, MC, FC, and CV, respectively. This study provides a technical reference for hyperspectral imaging technology as a new method for the rapid, nondestructive proximate analysis and quality assessment of coal. hyperspectral detection; coal proximate analysis; feature extraction; texture information; prediction modeling 20 organic matter, and mineral assemblages [3]. Coal proximate analysis can be defined as a technique that measures ash, volatile matter, moisture, and fixed carbon content [4], and is the most commonly used method to characterize coal quality. The calorific value of coal is also included in this study, with a total of five indicators, which is relevant to coal utilization and trade. Currently, traditional proximate analysis of coal primarily relies on the chemical processes of combustion, with indicators based on the percentage of mass lost. Moisture content is measured first, as it is the initial component eliminated when coal is heated (105-110 \({}^{\circ}\)C) (ISO11722) [5]. Volatile matter is recorded as an indicator of the decomposition of substances when coal is heated to high temperatures (900 \({}^{\circ}\)C) for approximately 7 min (ISO 562) [6]. Ash is finally measured as the residue after the coal has been combusted to a constant mass at the final temperature (815 \({}^{\circ}\)C) (ISO 1171) [7]. Finally, fixed carbon is the remaining material in the fuel after the former three components have been determined. Calorific value indicates the energy produced by the complete combustion of coal [8]. Although these methods are highly accurate, they need to consume coal, which causes loss. Additionally, a large number of sampling tests are mandatory when there is a large amount of coal, which is even more time-consuming and laborious [9]. Spectroscopic analysis has been widely used in many fields for the qualitative and quantitative characterization of substances by measuring the reflectance or absorbance of spectra [10; 11; 12]. Previous studies have demonstrated that moisture, ash, volatile matter, and other components of coal can be determined using NIR spectroscopy. [PERSON] et al.