## 3 Results
### A Universal Prediction Model for SSC of Multiple Blueberry Varieties  
#### 3.5.1 PLSR Model Based on Full-Wavelength Spectra with Different Pretreatments  
During the modeling process, the range of LVs was established as 1\(\sim\)25 to prevent over-fitting or under-fitting. Table 2 shows the prediction results of the full-wavelength PLSR model based on the original spectrum and different pretreatment methods. It can be seen from Table 2 that there are significant differences in the prediction performance of the models with different pretreatment methods. For the raw spectral data, the PLSR model had \(R_{p}^{2}=0.89\), RMSE = 0.40%, and RPD = 2.98 on the prediction set. In contrast, after MSC or SNV pretreatment, \(R_{p}^{2}\) increased to 0.93, RMSEP decreased to 0.34%, RPD reached 3.90 and 3.89, respectively, and the model performance significantly improved.  
In the combined treatment method, the model performance of S-G, MSC and SNV combined pretreatment was the best, and the PLSR model prediction results are plotted in Figure 6. The prediction set \(R_{p}^{2}\) of the combined preprocessing method reached 0.94, RMSEP decreased to 0.33%, RPD increased to 3.94 and the number of latent variables remained at 21, indicating that the model achieved the improvement of prediction performance while ensuring low complexity, which further proved the effectiveness and feasibility of the method in the above spectral data processing. For all pretreatment methods, the RPD values exceeded 3.0, and the RPD of the combined method was close to 4.0, indicating that the PLSR model established after treatment had strong robustness for SSC prediction of multi-varieties blueberry, and was suitable for universal detection scenarios of mixed varieties.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample Type** & **Sample Set** & **Quantities** & **Range** & **Mean Value** & **Standard Deviation** \\ \hline L25 & Calibration Set & 225 & 7.90\(\sim\) 17.05 & 11.71 & 1.87 \\  & Prediction Set & 74 & 8.55\(\sim\)15.65 & 11.62 & 1.48 \\ \hline Bluecrop & Calibration Set & 225 & 8.10\(\sim\)15.90 & 11.64 & 1.56 \\  & Prediction Set & 73 & 9.15\(\sim\)14.30 & 11.62 & 1.22 \\ \hline Lexi & Calibration Set & 225 & 7.75\(\sim\)18.00 & 12.54 & 1.72 \\  & Prediction Set & 73 & 9.30\(\sim\)15.10 & 12.46 & 1.19 \\ \hline Total & Calibration Set & 675 & 7.75\(\sim\)18.00 & 11.96 & 1.77 \\  & Prediction Set & 220 & 8.55\(\sim\)15.65 & 11.90 & 1.36 \\ \hline \hline \end{tabular}
\end{table}
Table 1: SSC statistics of three varieties of blueberries.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample Set** & **Pre-Processing** & **The Number of LVs** & \(\mathbf{R_{C}^{2}}\) & **RMSEC/\%** & \(\mathbf{R_{P}^{2}}\) & **RMSEP/\%** & **RPD** \\ \hline  & Raw & 21 & 0.95 & 0.38 & 0.89 & 0.40 & 2.98 \\  & S-G & 23 & 0.95 & 0.39 & 0.90 & 0.39 & 3.13 \\ Multi-variety & MSC & 23 & 0.95 & 0.32 & 0.93 & 0.34 & 3.90 \\ blueberry mixed & SNV & 24 & 0.96 & 0.32 & 0.93 & 0.34 & 3.89 \\ dataset & S-G+MSC & 24 & 0.96 & 0.36 & 0.93 & 0.33 & 3.89 \\  & S-G+SNV & 21 & 0.96 & 0.37 & 0.93 & 0.33 & 3.92 \\  & S-G+MSC+SNV & 21 & 0.96 & 0.37 & 0.94 & 0.33 & 3.94 \\ \hline \hline \end{tabular}
\end{table}
Table 2: SSC prediction results from PLSR models based on full-wavelength spectra with different preprocessing.