## 3 Results
### Diffuse Transmission Spectroscopy Analysis
#### 3.1.4 Comparison of Preprocessing on Transmittance PLSR Model  
This study employed a series of advanced spectral preprocessing methods to enhance modeling performance and analytical accuracy. The preprocessing methods utilized included normalization, smoothing, SNV, MSC, autoscaling, and centering. These steps are designed to effectively reduce noise and eliminate interfering signals, thereby extracting more meaningful features from the raw spectral data.  
Normalization adjusts the intensity values of spectral data to a standard range, which helps eliminate differences in spectral intensity due to variations in sample thickness or concentration, thereby enhancing comparability between different samples. Smoothing, such as with the Savitzky-Golay (SG) filter, reduces noise in the spectral data while preserving the main trends and features of the signal, effectively mitigating the impact of random noise. SNV transformation standardizes each data point within every spectrum to reduce spectral variations caused by sample scattering, correcting for scattering effects due to differences in particle size and distribution. MSC compares and corrects the measured spectra against a reference spectrum to effectively eliminate scattering effects, which is particularly useful when significant variations exist between different spectra. Autoscaling standardizes spectral data so that each variable has the same variance, balancing the importance of different wavelength points and preventing any single wavelength from disproportionately influencing the results during modeling. Centering adjusts the average value of the spectral data to zero, eliminating baseline shifts between different spectra. This step is crucial for subsequent multivariate analysis and modeling, ensuring that all spectral data are compared on the same baseline.  
The combined application of these preprocessing methods significantly enhances the quality of spectral data, providing a more reliable and accurate foundation for subsequent modeling and analysis. This integration not only improves the predictive performance of the model but also increases its stability and robustness, thereby making it better suited for practical problem-solving applications.  
As shown in Table 3, the model using raw data also exhibited high \(R_{p}^{2}\) values, and the performance of the PLSR models was significantly influenced by the different spectral preprocessing methods. Among these, MSC preprocessing demonstrated the best performance, with an \(R^{2}\) of 0.99 for the calibration set and 0.97 for the prediction set, as well as the lowest RMSEC (0.38 \({}^{\circ}\)Brix) and RMSEP (0.80 \({}^{\circ}\)Brix). Although normalization and SG preprocessing reduced the number of latent variables, their performance did not surpass that of MSC. Overall, MSC was identified as the most effective preprocessing method, improving both model fitting and prediction accuracy. The modeling effect after MSC preprocessing is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Preprocessing** & **LVs** & \(R_{\mathbf{C}}^{2}\) & \(R_{\mathbf{p}}^{2}\) & **RMSEC (\({}^{\circ}\)Brix)** & **RMSEP (\({}^{\circ}\)Brix)** \\ \hline RAW & 10 & 0.99 & 0.96 & 0.39 & 0.79 \\ Normalization & 7 & 0.95 & 0.94 & 0.87 & 0.95 \\ SG & 7 & 0.94 & 0.95 & 0.87 & 0.95 \\ SNV & 10 & 0.99 & 0.96 & 0.42 & 0.90 \\ MSC & 10 & 0.99 & 0.97 & 0.38 & 0.80 \\ Autoscaling & 7 & 0.96 & 0.94 & 0.82 & 0.91 \\ Center & 10 & 0.99 & 0.96 & 0.39 & 0.79 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of diffuse transmittance PLSR models using different preprocessing methods.