## 2 Materials and Methods
### Screening for Sensitive Variables  
The temperature and humidity datasets, belonging to the same type of variables, tend to exhibit covariance. Therefore, this study utilizes the Uninformative Variable Elimination (UVE) method to select representative sensitive variables from the temperature and humidity variable data. The set of sensitive variables will have the ability to predict the dependent variable instead of the full set of temperature and humidity variables [35; 36].  
UVE is a feature variable screening method based on the regression coefficients of the partial-least-squares (PLS) model, originally proposed by [PERSON] et al. [37]. The method constructs a PLS model by combining the original factor matrix with a randomly generated noise matrix and obtains the coefficient matrix of the model. Finally, the set of sensitive variables is determined by finding the ratio of the mean to the standard deviation of the model's coefficient matrix. The specific implementation process is as follows:  
Step 1: The temperature and humidity variable matrix \(X(n\times p)\) and the randomly generated noise matrix \(N(n\times p)\) are summed to obtain the mixing matrix \(XN(n\times 2p)\).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Names** & **Ambient** & **Upper** & **Lower** & **Drying** & **Drying** & **Drying** & **Drying** & **Drying** \\
**Outlet** & **Outlet** & **Outlet** & **Section A** & **Section B** & **Section C** & **Section D** & **Section E** & **Pipe** \\ \hline Pearson correlation coefficient & 0.040 & 0.861 & 0.809 & 0.765 & 0.882 & 0.781 & 0.874 & 0.709 & 0.360 \\ Significance & 0.569 & \(<\)0.001 & \(<\)0.001 & \(<\)0.001 & \(<\)0.001 & \(<\)0.001 & \(<\)0.001 & \(<\)0.001 \\ \hline \end{tabular}
\end{table}
Table 2: Statistical table of Pearson correlation coefficient between characteristic factor (temperature) and corn moisture content.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Names** & **Ambient** & **Upper** & **Lower** & **Drying** & **Drying** & **Drying** & **Drying** & **Drying** & **Hot Air** \\
**Outlet** & **Outlet** & **Outlet** & **Section A** & **Section B** & **Section C** & **Section D** & **Section E** & **Pipe** \\ \hline Pearson correlation coefficient & \(-0.121\) & \(-0.749\) & \(-0.770\) & \(-0.714\) & \(-0.752\) & \(-0.721\) & \(-0.790\) & \(-0.816\) & \(-0.452\) \\ Significance & 0.083 & \(<\)0.001 & \(<\)0.001 & \(<\)0.001 & \(<\)0.001 & \(<\)0.001 & \(<\)0.00 Step 2: A PLS regression model is constructed using the mixing matrix \(XN\) as the model input and the outgoing corn moisture content matrix \(Y\) as the output, employing cross-validation. This results in a matrix of regression coefficients \(b(n\times 2p)\).  
Step 3: Calculate the mean \(M_{i}\) and standard deviation \(S_{i}\) of the matrix of regression coefficients, and find their ratio \(E_{i}\) as shown in Equation (2).  
\[E_{i}=\frac{M_{i}}{S_{i}}i=1,2,\cdots,2p \tag{2}\]  
Step 4: Calculate the maximum absolute value \(E_{max}=max[abs(E_{i})]\) of \(E_{i}\) in interval \([p+1,2p]\), and remove the bands with values less than \(E_{max}\) in the matrix in interval \([1,p]\). The remaining variables constitute the filtered sensitive variables.  
During the stage of constructing the prediction model, both the complete set of temperature and humidity variables and the eight sets of sensitive variables derived from UVE screening will be utilized as independent variables.