## 3 Results and Discussion
### Electronic Nose Detection
#### 3.1.6 Random Forest (RF)-Based Classification Model  
Random forest (RF) is an ensemble learning method that generates final predictions by aggregating the outputs of multiple decision trees through a voting mechanism. This approach is particularly effective for handling high-dimensional data and capturing non-linear relationships. When applied to reduced datasets, random forest can effectively identify the primary features and make accurate classification or regression predictions.  
One key difference between the random forest model and support vector machines (SVM) lies in the configuration of decision trees. Varying the number of decision trees trained can enhance the model's accuracy, as demonstrated in Table 4, which summarizes the evaluation metrics for configurations with 10, 50, and 100 trees.  
By comparing the prediction results of different numbers of decision trees in the table above, it is evident that the results for the oyster data in a 12 \({}^{\circ}\)C environment, after being reduced by Linear Discriminant Analysis (LDA) and processed through the random forest (RF) model, show significant improvements across various indicators as the number of decision trees increases. Conversely, the accuracy rates for other temperature conditions and algorithms after dimensionality reduction did not exhibit a significant improvement and, in some cases, even decreased. This phenomenon can be attributed to the fact that increasing the number of decision trees enhances the model's complexity, allowing it to capture more non-linear relationships and patterns within the data [25]. If the underlying patterns in the dataset are sufficiently complex, augmenting the number of trees can aid the model in better fitting these patterns, thereby improving the accuracy rate.  
Figure 5(b) illustrates the predicted storage time of oysters at 12 \({}^{\circ}\)C, utilizing a random forest model developed from data processed through the LDA algorithm. The model exhibits a classification accuracy greater than 95%, indicating its exceptional performance. A limited number of samples are inaccurately predicted for storage durations of 48, 72, and 96 h, which corresponds to the overlapping sample points for different storage times depicted in Figure 4(b).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Oyster Storage** **Temperatures**} & **Dimensionality Reduction Method** & **Classification Labels (by Detection Hours)** & **Number of Training Samples (Groups)** & **Number of Test Samples (Groups)** & **Number of Predicted Incorrect Samples (Groups)** & **Accuracy** \\ \hline
4 \({}^{\circ}\)C & PCA & 0–216 h & 555 & 370 & 53 & 80\% \\
4 \({}^{\circ}\)C & LDA & 0–216 h & 555 & 370 & 22 & 94\% \\
12 \({}^{\circ}\)C & PCA & 0–168 h & 411 & 274 & 49 & 82\% \\
12 \({}^{\circ}\)C & LDA & 0–168 h & 411 & 274 & 11 & 96\% \\
20 \({}^{\circ}\)C & PCA & 0–72 h & 180 & 120 & 8 & 93\% \\
20 \({}^{\circ}\)C & LDA & 0–72 h & 180 & 120 & 3 & 98\% \\
28 \({}^{\circ}\)C & PCA & 0–48 h & 159 & 106 & 14 & 87\% \\
28 \({}^{\circ}\)C & LDA & 0–48 h & 159 & 106 & 2 & 98\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: SVM evaluation report.