## 3 Results and Discussion  
### Calibration Results  
The raw NIR spectra of strawberry, grape, and apple are presented in Figure 1. The spectra exhibit high absorption in the range of 1300-1500 nm, which is likely caused by the water content in the fruits. The bands between 930 and 1000 nm and in the wavelength range of 1400-1500 nm can be assigned to the 1 st and 2 nd overtones of O-H vibrations. Meanwhile, the bands between 1100 and 1200 nm can be assigned to the 2 nd overtones of C-H vibrations. Furthermore, it is apparent that the spectra of the three fruits have varying shapes and intensities. Specifically, the grape spectra exhibit the most fluctuating background, while the apple spectra display the smallest. These differences in spectral characteristics may be attributed to the diverse shapes and compositions of the measured fruits, which could potentially impede the transferability of models across fruits. A PLS model using 15 latent variables was developed on the raw spectra of strawberries to predict SSC. The model showed good performance on the validation set with an RMSEP of 0.53 \({}^{\circ}\)Brix and Rp of 0.91 (Table 2). However, the same model applied to grapes and apples resulted in less accurate predictions, with RMSE values of 3.47 and 16.40, respectively, which were 6.5 and 30.9 times higher than those on strawberry data. The R values of grape and apple also decreased to 0.84 and 0.01, respectively. The PLS model for strawberries cannot be applied to grapes and apples directly. Due to the fact that NIR spectra frequently contain interferences of background information, drift, and noise, the raw NIR spectra were subjected to spectral preprocessing. To improve the accuracy of the model, spectral preprocessing techniques including CWT, SG smooth, and SNV were applied, resulting in a more accurate model for strawberries. The best model, which used CWT, had an RMSEP of 0.41 \({}^{\circ}\)Brix and an R value of 0.95, representing a decrease of 7.8% and an increase of 4.2%. Additionally, the generalization of the model to grapes and apples showed remarkable improvement, with a decrease in RMSEP to 2.41 and 4.90, respectively, corresponding to 5.9 and 11.9 times the RMSEP on the strawberry data. Therefore, CWT was selected as the spectral preprocessing method for the following analysis. The NIR spectra of three different fruits with CWT preprocessing are shown in Figure 2A. Compared with the raw spectra (Figure 1), the intensity distribution differences between the three fruits were significantly smaller. Differences were only observed in the spectra at 1100-1500 nm, which represent the absorption of the 1 st overtone of the -OH \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Preprocessing Methods**} & \multirow{2}{*}{_nLV_} & \multicolumn{2}{c}{**Calibration**} & \multicolumn{2}{c}{**Validation**} \\ \cline{3-6}  & & **RMSEC** & **Rc** & **RMSEP** & **Rp** \\  & & (\({}^{\circ}\)**Brix)** & & (\({}^{\circ}\)**Brix)** & **Rp** \\ \hline None & & & & & \\  Strawberry & 15 & 0.32 & 0.95 & 0.53 & 0.91 \\  Grape & - & - & - & 3.47 & 0.84 \\  Apple & - & - & - & 16.40 & 0.01 \\  CWT & & & & & \\  Strawberry & 19 & 0.30 & 0.96 & 0.41 & 0.95 \\  Grape & - & - & - & 2.40 & 0.74 \\  Apple & - & - & - & 4.90 & 0.12 \\  SG Smooth & & & & & \\  Strawberry & 12 & 0.54 & 0.86 & 0.78 & 0.80 \\  Grape & - & - & - & 2.94 & 0.67 \\  Apple & - & - & - & 25.44 & 0.28 \\  SNV & & & & & \\  Strawberry & 19 & 0.18 & 0.99 & 0.41 & 0.95 \\  Grape & - & - & - & 6.48 & 0.34 \\  Apple & - & - & - & 14.45 & 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Strawberry models established with different spectral preprocessing methods and the application to different fruits. Figure 1: NIR spectra of strawberry, grape, and apple. group. This difference may be due to variations in water content among the different fruits.