## 3 Results and Discussion
### Comparison of Spectral Data Preprocessing Methods  
As shown in Figure 1, the spectrum displayed additional absorption peaks in the long-wave band (4000-7000 cm\({}^{-1}\)), primarily reflecting the NIRS absorption characteristics of water (-OH) and various components in YCXY tea samples of differing quality grades. However, the NIRS signals indicative of tea quality were relatively weak and likely masked by the absorption peaks of other components, which significantly impacted the accuracy of the prediction model [33]. Therefore, spectral preprocessing was essential before model development to minimize noise and improve the signal-to-noise ratio of the spectral data. In this study, various spectral preprocessing methods were applied to the NIR spectra of YCXY tea samples of differing qualities, and partial least squares (PLS) regression was used to develop NIRS prediction models. Model performance was evaluated using two parameters, RMSECV and Rc\({}^{2}\), where a higher Rc\({}^{2}\) and a lower RMSECV indicated better preprocessing efficiency. The results of all preprocessing methods are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & **Data Statistics** & **All Samples** & **Calibration Set** & **Prediction Set** \\ \hline \multirow{3}{*}{Quality scores} & Max & 93.00 & 93.00 & 90.00 \\  & Min & 70.00 & 70.00 & 80.00 \\ \cline{1-1}  & Average & 78.60 & 80.14 & 83.65 \\ \cline{1-1}  & SD & 3.52 & 3.62 & 3.18 \\ \hline \hline \end{tabular} Note: Max: maximum; Min: minimum; SD: standard deviation.  
\end{table}
Table 1: Quality scores of YCXY tea.  
Figure 3 illustrates that different spectral preprocessing methods have distinct impacts on the original spectra of YCXY tea, leading to significant variations in the performance of PLS models for predicting quality scores. Without any spectral preprocessing, the PLS model shows the poorest performance (Rc\({}^{2}\) = 0.652, RMSECV = 1.644). The model's poor predictive performance is attributed to the substantial noise inherent in the original spectrum, which compromises its effectiveness when used directly for modeling. Denoising the original spectra with various preprocessing techniques improves the PLS model performance to varying degrees. Among these methods, MSC+SD achieves the best results, with the PLS model yielding an Rc\({}^{2}\) of 0.743 and an RMSECV of 1.152, representing a 29.93% reduction in RMSECV compared to the model without preprocessing. MSC effectively reduces noise caused by specular reflection and unevenness in near-infrared diffuse reflection spectra, while also correcting baseline shifts and inconsistencies. The second derivative technique complements this by eliminating baseline drift, enhancing spectral features, and resolving spectral band overlap. Together, the MSC+SD approach significantly improves the signal-to-noise ratio, resulting in optimal model performance (Figure 4). This demonstrates that spectral preprocessing significantly enhances the signal-to-noise ratio, consistent with previous research findings [34]. Nevertheless, as shown in Figure 3, the current NIRS prediction models for YCXY tea quality scores still fall short of accurately predicting the quality of unknown samples. Further refinement is needed by identifying characteristic spectral intervals that are closely related to tea quality to enhance the model's predictive accuracy.  
Figure 3: Comparison of PLS model results for quality scores across different preprocessing methods.