## 3 Results
### Diffuse Reflectance Spectroscopy
#### 3.2.5 Comparison of Wavelength Selection Methods on Diffuse Reflectance PLSR  
This study compared the performance of four feature wavelength selection methods--CARS, SPA, RF, and MC-UVE--in the establishment of PLSR models using diffuse reflectance spectra. By evaluating and comparing the performance of these methods, we gained insights into their respective strengths and limitations. As shown in Table 8, the modeling parameters for the four feature wavelength selection methods are described in detail.  
Firstly, the CARS method selected the fewest feature wavelengths, totaling only 90. The PLSR model utilizing CARS achieved a coefficient of determination \(R_{C}^{2}\) of 0.97 on the calibration set and an \(R_{p}^{2}\) of 0.96 on the prediction set. The RMSEC was 0.49 \({}^{\circ}\)Brix, and the RMSEP was 0.67 \({}^{\circ}\)Brix. These results indicated that the CARS method achieved high-precision modeling with a reduced number of feature wavelengths while maintaining excellent error control and overall performance.  
In contrast, the SPA method achieved the highest \(R_{C}^{2}\) of 0.98 on the calibration set, but its \(R_{p}^{2}\) on the prediction set was lower, at 0.90, with higher RMSE values. This discrepancy may be attributed to overfitting due to the inclusion of too many feature wavelengths. The RF method exhibited balanced performance, with stable \(R^{2}\) and RMSE values on both the calibration and prediction sets, although its errors were slightly higher compared with those of CARS. The MC-UVE method performed the least effectively among the methods, showing lower \(R^{2}\) values and higher RMSE on both the calibration and prediction sets, indicating suboptimal performance in feature selection. As shown in Figure 12, these results illustrated the prediction performance of the diffuse reflectance PLSR models for kiwi  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Preprocessing** & **LVs** & \(R_{C}^{2}\) & \(R_{p}^{2}\) & \
\begin{tabular}{c} **RMSEC** \\ (\({}^{\circ}\)**Brix)** \\ \end{tabular} &
\begin{tabular}{c} **RMSEP** \\ (\({}^{\circ}\)**Brix)** \\ \end{tabular} \\ \hline RAW & 9 & 0.97 & 0.84 & 0.51 & 0.92 \\ Normalization & 10 & 0.97 & 0.78 & 0.53 & 0.89 \\ MSC & 7 & 0.95 & 0.49 & 0.64 & 0.89 \\ SNV & 8 & 0.96 & 0.43 & 0.59 & 0.88 \\ Center & 10 & 0.98 & 0.86 & 0.43 & 0.86 \\ Autoscaling & 11 & 0.98 & 0.92 & 0.41 & 0.81 \\ SG & 9 & 0.94 & 0.96 & 0.66 & 0.68 \\ SG + GY & 9 & 0.94 & 0.94 & 0.71 & 0.69 \\ SG + MSC & 6 & 0.93 & 0.94 & 0.75 & 0.75 \\ SG + SNV & 8 & 0.95 & 0.93 & 0.63 & 0.81 \\ SG + Center & 9 & 0.94 & 0.96 & 0.66 & 0.69 \\ SG + Autoscaling & 9 & 0.93 & 0.94 & 0.66 & 0.82 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of diffuse reflectance PLSR models using different preprocessing methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **LVs** & **No. of Wavelengths** & \(R_{C}^{2}\) & \(R_{p}^{2}\) & \
\begin{tabular}{c} **RMSEC** \\ (\({}^{\circ}\)**Brix)** \\ \end{tabular} &
\begin{tabular}{c} **RMSEP** \\ (\({}^{\circ}\)**Brix)** \\ \end{tabular} \\ \hline CARS & 9 & 90 & 0.97 & 0.96 & 0.49 & 0.67 \\ SPA & 9 & 297 & 0.98 & 0.90 & 0.38 & 1.04 \\ RF & 9 & 236 & 0.95 & 0.96 & 0.62 & 0.68 \\ MC-UVE & 9 & 278 & 0.84 & 0.91 & 1.09 & 1.01 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results of diffuse reflectance PLSR models using different effective wavelength selections.