## 4 Feature Selection Results  
Five types of meat images (beef, buffalo meat, goat meat, horse meat, and pork) were used to test the feature selection findings. Each type of meat image was divided into three classes: fresh, frozen, and rotten. A description of each meat is presented in Table 3 using GLCM metrics, including homogeneity, contrast, correlation, energy, and entropy. Importantly, each meat texture image has the same pixel size, namely 500 \(\times\) 500 [55,56] using a uniform pixel size for all meat texture images optimizes computer power: it can lower the computational load and speed up the data analysis process. However, this method has several disadvantages, including a lack of spatial resolution and difficulty in accurately capturing any scale variations between images. The texture of each meat image can still be studied more deeply using GLCM metrics, which include contrast, correlation, energy, homogeneity, and entropy. This GLCM measure can be helpful when comparing the textural properties of fresh, frozen, and rotten meat from different types of animals observed. Therefore, despite limitations in spatial resolution, GLCM analysis can still provide valuable insights into meat texture recognition and classification. It can also provide valuable insights into the detection and classification of meat texture, despite limitations in spatial resolution. Table 5 illustrates how effectively all feature sets differentiate the three classes: (a), (b), and (c). The average percentage of fresh meat images ranges from 51.52% to 183.21%, while for frozen meat images, the range is from 78.25% to 185.75%, and for rotten meat images, the range is from 34.62% to 115.79%. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Type of** **Meat**} & \multirow{2}{*}{**Class**} & \multicolumn{6}{c}{**GLCM Metrics**} & \multirow{2}{*}{**Minimum**} & \multirow{2}{*}{**Maximum**} & \multirow{2}{*}{**Average**} \\ \cline{3-3} \cline{5-10}  & & **Contrast** & & & & & & & \\ \hline \multirow{3}{*}{Beef} & Fresh Beef & 686.14 & 0.466 & 0.016 & 0.055 & \(-\)72.23 & \(-\)72.23 & 686.14 & 122.89 \\ \cline{2-10}  & Frozen Beef & 552.35 & 0.454 & 0.014 & 0.063 & \(-\)72.15 & \(-\)72.15 & 552.35 & 96.15 \\ \cline{2-10}  & Rotten Beef & 651.1 & 0.86 & 0.014 & 0.058 & \(-\)73.09 & \(-\)73.09 & 651.1 & 115.79 \\ \hline \multirow{3}{*}{Buffalo} & Fresh Buffalo Meat & 656.57 & 0.68 & 0.01 & 0.056 & \(-\)72.66 & \(-\)72.66 & 656.57 & 116.93 \\ \cline{2-10}  & Frozen Buffalo Meat & 551.83 & 0.644 & 0.012 & 0.056 & \(-\)73.53 & \(-\)73.53 & 551.83 & 95.80 \\ \cline{2-10}  & Rotten Buffalo Meat & 583.98 & 0.056 & 0.018 & 0.053 & \(-\)72.43 & \(-\)72.43 & 583.98 & 102.33 \\ \hline \multirow{3}{*}{Goat} & Fresh Goat Meat & 988.17 & 0.28 & 0.015 & 0.056 & \(-\)72.48 & \(-\)72.48 & 988.17 & 183.21 \\ \cline{2-10}  & Frozen Goat Meat & 999.097 & 0.474 & 0.012 & 0.05 & \(-\)70.90 & \(-\)70.90 & 999.097 & 185.75 \\ \cline{2-10}  & Rotten Goat Meat & 545.43 & 0.304 & 0.017 & 0.064 & \(-\)71.60 & \(-\)71.60 & 545.43 & 94.84 \\ \hline \multirow{3}{*}{Horse} & Fresh Horse Meat & 716.54 & 0.278 & 0.012 & 0.055 & \(-\)72.82 & \(-\)72.82 & 716.54 & 128.81 \\ \cline{2-10}  & Frozen Horse Meat & 624.06 & 0.488 & 0.013 & 0.06 & \(-\)73.25 & \(-\)73.25 & 624.06 & 110.28 \\ \cline{2-10}  & Rotten Horse Meat & 458.32 & 0.332 & 0.02 & 0.079 & \(-\)72.17 & \(-\)72.17 & 458.32 & 77.32 \\ \hline \multirow{3}{*}{Pork} & Fresh Pork & 329.53 & 0.376 & 0.025 & 0.056 & \(-\)72.37 & \(-\)72.37 & 329.53 & 51.52 \\ \cline{2-10}  & Frozen Pork & 462.78 & 0.392 & 0.024 & 0.051 & \(-\)71.99 & \(-\)71.99 & 462.78 & 78.25 \\ \cline{1-1} \cline{2-10}  & Rotten Pork & 244.98 & 0.34 & 0.029 & 0.064 & \(-\)72.30 & \(-\)72.30 & 244.98 & 34.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of GLCM feature extraction results for contrast, correlation, energy, homogeneity, and entropy metrics. The image of fresh meat shows that fresh pork has less varied textures than other meats.