## 3 Results
### Results of Combined Features by Wavelengths and Texture Information  
Table 5 shows the results of using texture features and combined spectral-texture features to predict coal quality indices. Firstly, the prediction performance of the model utilizing only texture features decreases compared with the results obtained using only spectral features (Table 5). Secondly, the texture features for optimal performance are not uniform among the three texture extraction methods. The Gabor-filtered texture features exhibit the best performance in Ash, VM, and MC, with a prediction set \(R_{p}^{2}\) of 0.811, 0.927, and 0.957, respectively. In CV, Gabor is also identified as the optimal texture characterization method, primarily due to the minimum \(RMSE_{p}\) of 0.994 while excluding the maximum \(R_{p}^{2}\) of 0.646 from the GLEM. The best model performance for FC was achieved based on the HS features with a prediction set \(R_{p}^{2}\) of 0.514. This indicates that Ash, VM, and MC \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Coal Quality Indices**} & \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Number of Wavelengths**} & \multirow{2}{*}{**Modeling**} & \multicolumn{4}{c}{**Calibration Set**} & \multicolumn{4}{c}{**Prediction Set**} \\ \cline{4-11}  & & & & \(R_{e}^{2}\) & \(RMSE_{e}\) & \(R_{ev}^{2}\) & \(RMSE_{ev}\) & \(R_{p}^{2}\) & \(RMSE_{p}\) & \(RPD\) \\ \hline \multirow{8}{*}{FC (\%)} & \multirow{8}{*}{SPA} & \multirow{8}{*}{6} & PLSR & 0.802 & 2.308 & 0.731 & 2.694 & 0.845 & 1.539 & 2.631 \\  & & & RF & 0.971 & 0.877 & 0.789 & 2.385 & 0.830 & 1.614 & 2.507 \\  & & & BPNN & 0.864 & 1.913 & 0.727 & 2.715 & 0.236 & 3.418 & 1.184 \\  & & & LSSVR & 0.877 & 1.818 & 0.810 & 2.262 & 0.794 & 1.774 & 2.283 \\  & & & PLSR & 0.898 & 1.752 & 0.829 & 2.269 & 0.121 & 1.857 & 1.104 \\  & & & RF & 0.959 & 1.108 & 0.715 & 2.929 & 0.208 & 1.763 & 1.163 \\  & & & BPNN & 0.896 & 1.764 & 0.774 & 2.608 & \(-\)3.790 & 4.333 & 0.473 \\  & & & LSSVR & 0.981 & 0.755 & 0.925 & 1.503 & 0.649 & 1.172 & 1.748 \\  & & & PLSR & 0.888 & 1.789 & 0.846 & 2.099 & 0.865 & 1.179 & 2.813 \\  & & & RF & 0.966 & 0.979 & 0.767 & 2.582 & 0.909 & 0.964 & 3.439 \\  & & & BPNN & 0.801 & 2.383 & 0.603 & 3.375 & 0.717 & 1.703 & 1.947 \\  & & & LSSVR & 0.894 & 1.743 & 0.845 & 2.105 & 0.884 & 1.093 & 3.033 \\  & & & PLSR & 0.819 & 2.231 & 0.720 & 2.776 & 0.817 & 1.539 & 2.422 \\  & & & RF & 0.937 & 1.312 & 0.533 & 3.584 & 0.816 & 1.543 & 2.414 \\  & & & BPNN & 0.863 & 1.938 & 0.622 & 3.224 & 0.042 & 3.523 & 1.058 \\  & & & LSSVR & 0.899 & 1.665 & 0.799 & 2.353 & 0.878 & 1.256 & 2.965 \\  & & & PLSR & 0.886 & 1.304 & 0.855 & 1.471 & 0.897 & 0.938 & 3.232 \\  & & & RF & 0.973 & 0.637 & 0.935 & 0.983 & 0.935 & 0.745 & 4.066 \\  & & & BPNN & 0.959 & 0.786 & 0.919 & 1.096 & 0.933 & 0.756 & 4.007 \\  & & & LSSVR & 0.983 & 0.510 & 0.973 & 0.630 & 0.973 & 0.480 & 6.315 \\  & & & PLSR & 0.953 & 0.842 & 0.923 & 1.074 & 0.947 & 0.646 & 4.490 \\  & & & RF & 0.981 & 0.535 & 0.867 & 1.413 & 0.911 & 0.835 & 3.474 \\  & & & BPNN & 0.988 & 0.428 & 0.801 & 1.732 & 0.653 & 1.651 & 1.757 \\ CV (MJ/kg) & & & LSSVR & 0.995 & 0.267 & 0.981 & 0.535 & 0.991 & 0.260 & 11.156 \\  & & & PLSR & 0.974 & 0.636 & 0.964 & 0.750 & 0.956 & 0.550 & 4.958 \\  & & & RF & 0.975 & 0.619 & 0.886 & 1.334 & 0.937 & 0.663 & 4.113 \\  & & & BPNN & 0.962 & 0.774 & 0.919 & 1.127 & 0.909 & 0.793 & 3.349 \\  & & & LSSVR & 0.975 & 0.626 & 0.964 & 0.755 & 0.962 & 0.512 & 5.321 \\  & & & PLSR & 0.874 & 1.357 & 0.826 & 1.596 & 0.768 & 1.425 & 2.148 \\  & & & RF & 0.916 & 1.109 & 0.423 & 2.904 & 0.447 & 2.200 & 1.392 \\  & & & BPNN & 0.988 & 0.397 & 0.683 & 2.152 & 0.367 & 2.354 & 1.301 \\  & & & LSSVR & 0.987 & 0.441 & 0.949 & 0.860 & 0.945 & 0.696 & 4.401 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._have advantages in texture feature-based modeling, which may be related to the fact that these three metrics can reflect the physical attributes of coal and thus affect the texture features.