## 3 Results and Discussion
### Comparison of Results Obtained by SPA, UVE, and BFWA  
Three different algorithms (SPA, UVE, and BFWA) were used in the wavelength selection, and a PLSR model was utilized to compare the predicted values. Figure 4 shows the results of the selection of different wavelength numbers by different methods. Compared with SPA and UVE, the BFWA achieved better prediction performance. The correlation coefficient \(R_{p}\) values obtained by the BFWA was higher compared to the two other methods in selecting different numbers of wavelengths. Meanwhile, the \(RMSEP\) values were lower. The results showed that the BFWA performed better in predicting moisture content.  
The accuracy and real-time of the results are important considerations in the development of online detection of sample moisture content systems. On this basis, the selection of optimal wavelengths is essential for reducing the amount of data acquisition and processing. As shown in Figure 4, the results of the model were stable when the number of selected wavelengths was 12. The selection of optimal wavelengths is an effective way to reduce the calculation time of prediction models and the amount of data processing. Considering the two aspects of accuracy and real-time results, 12 was a reasonable number of selected wavelengths. The results obtained by three different methods are shown in Table 1.  
Figure 3: Mean reflectance spectra of five soybeans at different drying times (0, 10, 40, 50, and 70 min).  
As shown in Table 1, the \(R_{c}\), \(RMSEC\), \(R_{p}\), and \(RMSEP\) values obtained by the BFWA were better compared to SPA and UVE. For the calibration sets, the averages of \(R_{c}\) values were higher by 3.5%  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Method** & \(R_{c}\)1  & \(R_{p}\)1  & \(R_{p}\)1  & \(R_{p}\)1  & \(R_{p}\)1  & \(R_{p}\)1  & \(R_{p}\)1  & \(R_{p}\)1  & \(R_{p}\)1  & \(R_{p}\)1  & \(R_{p}\)1  & \(R_{p}\)1  & \(R_{p}\)1  & \(R_{p}\)1  & \(R_{p}\)1  & \(R_{p}\)1 [ENDFOOTNOTE] & \(R_{p}\)1  & \(R_{p}\)1  & \(R_{p}\)1 [ENDFOOTNOand 3.6%, and the averages of \(RMSEC\) values were reduced by 34.7% and 34.9%. For the prediction sets, the averages of \(R_{p}\) values were higher by 3.6% and 4.1%, and the averages of \(RMSEP\) values were reduced by 30.3% and 31.2%. Compared to the other two methods, the standard deviation (Std) values of \(R_{c}\), \(RMSEC\), \(R_{p}\), and \(RMSEP\) obtained by BFWA method were smaller than those obtained by the SPA and the UVE method, which indicated that the BFWA method performed better in stability convenience. Although the accuracy values of the BFWA model were slightly lower than that of the full wavelength model, the wavelength number is only 12.8% of full wavelength, which helps to develop online multispectral imaging systems. For online systems, fewer wavelengths will reduce the time of data acquisition, transformation, and processing to meet real-time requirements in application.  
The 12 wavelengths were 457.96, 509.48, 567.44, 586.76, 612.52, 657.6, 696.24, 747.76, 773.52, 850.8, 908.76, and 934.52 nm of a selective operation by BFWA. These wavelengths were mostly centered around the absorption peaks shown in Figure 3's characteristic curves, which play an important role in soybean moisture prediction. Figure 5 shows a scatter plot for predicting the moisture content of soybeans from the prediction set. The diagram indicates that a good correlation exists between the actual value and predicted value of soybean moisture content.