## 3 Results
### Spectra Analysis and Preprocessing Results  
The raw spectra and the preprocessed spectra of all coal samples are shown in Figure 5. From the raw spectra in Figure 5a, it can be found that the spectral curves of the coal samples have similarity in that they have low reflectance in the visible near infrared (VNIR) band and increased reflectance with the shortwave infrared (SWIR) wavelength. The overall reflectance of coal is low, which is attributed to the black appearance of coal caused by the main carbon component. In addition, the absorption and reflection characteristics of \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Coal Quality**} & \multicolumn{4}{c}{**Calibration Set (46)**} & \multicolumn{4}{c}{**Prediction Set (15)**} \\ \cline{2-9}
**Indices** & **Min** & **Max** & **Mean** & \(Std_{c}\) & **Min** & **Max** & **Mean** & \(Std_{p}\) \\ \hline Ash (\%) & 17.79 & 60.24 & 36.48 & 11.51 & 22.56 & 57.70 & 34.16 & 8.06 \\ MC (\%) & 0.38 & 3.60 & 1.71 & 0.79 & 0.38 & 2.82 & 1.25 & 0.80 \\ VM (\%) & 9.54 & 30.49 & 20.00 & 6.52 & 10.5 & 30.18 & 21.97 & 5.77 \\ FC (\%) & 29.12 & 54.34 & 41.60 & 5.25 & 31.44 & 49.63 & 43.23 & 3.81 \\ CV (MJ\(\cdot\)kg\({}^{-1}\)) & 11.61 & 26.55 & 19.96 & 3.87 & 12.75 & 25.05 & 21.41 & 3.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical results of coal quality indices in calibration set and prediction set. The values in brackets indicate the number of samples in the calibration and prediction sets. the coal spectral reflectance are generally insignificant, and the more prominent spectral peaks and valleys are mainly concentrated in the shortwave infrared region. There are three obvious spectral reflection peaks at 2100 nm, 2250 nm, and 2400 nm. Additionally, the typical spectral absorption valleys can be clearly observed at 1900 nm, 2200 nm, and 2300 nm. In addition, it can be found that there are weak spectral absorption valleys near 1400 nm, 1700 nm, and 2150 nm. Coal is a complicated, mixed solid material, which mainly contains a large amount of organic matter (e.g., aromatic and aliphatic compounds, etc.) and a small amount of inorganic matter (e.g., SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), Fe\({}_{2}\)O\({}_{3}\), etc.) [34; 36]. The large amount of organic matter in the coal leads to a more pronounced spectral signature. The typical absorption valley at 2300 nm is consistent with a combination of asymmetric stretching and symmetric bending of CH\({}_{2}\) and CH\({}_{3}\)[34], while the weak spectral absorption valley at 2150 nm is associated with a combination of aromatic C-H stretching and C-C stretching [35]. In addition, the inorganic components of coal have a great influence on the spectra despite the small amount, and the typical absorption at 1900 nm and the weak absorption feature at 1400 nm are mainly due to the first overtone of O-H in the free water, which is closely related to the moisture content of coal. Clay minerals such as kaolinite, which are the host minerals for the inorganic components of coal [36], form a distinct absorption valley near 2200 nm due to the vibration of Al-OH [35]. However, due to the structural differences between the organic and inorganic components of coal, it is difficult to select the characteristic wavelengths directly by manual operation, so it is necessary to select the characteristic wavelength extraction method to extract the characteristic wavelengths of the suitable spectra. Figure 5b-d shows the spectra curves after pretreatment by SG, FD, and MSC methods, respectively. Specifically, the SG method reduces noise interference through the polynomial fitting, which makes the spectral curve smoother than Raw while maintaining the spectral characteristics. The FD method applies reflectance differentiation in the wavelength region, which reflects the information of reflectance change and may highlight the spectral features.