# Prediction of the Moisture Content in Corn Straw Compost Based on Their Dielectric Properties
## 3 Results
### PCA-SVR Model Prediction Results  
The full-frequency and PCA-selected characteristic variables were used as independent variables for SVR modeling, and the moisture content of straw compost was used as the dependent variable to establish the SVR regression model. The RBF kernel function with excellent stability and accuracy was used as the SVR function. When applying the RBF-SVR regression method, the training set was optimally searched for parameters by a grid search to determine the penalty factor \(c\) and kernel parameter \(g\) of SVR to ensure optimization of the model parameters. Table 3 shows the \(c\) and \(g\) optimization results. The three data sets of \(\varepsilon^{\prime}\), \(\varepsilon^{\prime\prime}\), and \(\varepsilon^{\prime}\) and \(\varepsilon^{\prime\prime}\) combined variables were selected using the cross-validation method for the full frequency and SPA characteristic frequency variables, respectively, and the results are presented in Table 3.  
The results in Table 3 reveal that all models can respond to some extent to the internal information of the samples. From the perspective of the selected variables, the prediction \begin{table}
\begin{tabular}{c c c c} \hline
**Moisture** & \multicolumn{3}{c}{**Bulk Density/(kg.m\({}^{-3}\))**} \\
**Content/\%** & **Level I** & **Level II** & **Level III** \\ \hline
5 & 665.6467 & 683.7607 & 720.7207 \\
10 & 677.9661 & 695.6522 & 730.5936 \\
15 & 692.6407 & 711.1111 & 747.6636 \\
20 & 704.8458 & 723.9819 & 765.5502 \\
25 & 720.7207 & 737.3272 & 784.3137 \\
30 & 761.9048 & 816.3265 & 855.6150 \\
35 & 802.0175 & 829.0155 & 874.3169 \\ \hline \end{tabular}
\end{table} 
Table 1: Bulk density values of samples at various moisture contents. \begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & \
\begin{tabular}{c} **Number of Extracted** \\ **Principal Components** \\ \end{tabular} &
\begin{tabular}{c} **Cumulative Contribution** \\ **Rate \%** \\ \end{tabular} \\ \hline \(\varepsilon^{\prime}\) & 1 & 99.90701 \\ \(\varepsilon^{\prime\prime}\) & 1 & 99.91443 \\ \(\varepsilon^{\prime}\) and \(\varepsilon^{\prime\prime}\) & 2 & 95.10533 \\ \hline \end{tabular}
\end{table} 
Table 2: Principal Component Analysis Extraction Results. \begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & \
\begin{tabular}{c} **Number of Extracted** \\ **Principal Components** \\ \end{tabular} &
\begin{tabular}{c} **Cumulative Contribution** \\ **Rate \%** \\ \end{tabular} \\ \hline \(\varepsilon^{\prime}\) & 1 & 99.90701 \\ \(\varepsilon^{\prime\prime}\) & 1 & 99.91443 \\ \(\varepsilon^{\prime}\) and \(\varepsilon^{\prime\prime}\) & 2 & 95.10533 \\ \hline \end{tabular}
\end{table} 
Table 3: Prediction Results and Parameter Optimization of Support Vector Machine Model. results of the SVR models based on PCA have limited effect or are even slightly better than those of the SVR models based on the full variables. The SVR model developed using \(\varepsilon^{\prime\prime}\) was extracted by PCA and increased by 0.1539 compared with the that developed using full variables in terms of the prediction set \(R_{\mathrm{p}}\)2. However, because of the redundant information in the full variable, using the variable is not feasible. The results revealed that the predictive performance of the variables after PCA extraction did not decrease even though the number of model operations decreased. Therefore, the SVR model established after the variables were extracted by PCA can predict the moisture content of straw compost accurately. Optimization of \(c\) and \(g\) parameters resulted in different information variables, which could be attributed to the fact that grid \(c\) and \(g\) coordinate parameters were substituted into SVR for training set modeling when grid search was performed, and the minimum root mean square error in the training set was used to select the best \(c\) and \(g\) parameters. Since different information variables have distinct training set modeling data, the optimal \(c\) and \(g\) parameters are optimized differently.