## 3 Results
### Comparison of PLSR Model Performance with Multiple Feature Selection Approaches and FOD Spectra  
Figure 5 illustrates a comparison of the performance of PLSR models built using various feature selection methods. Overall, the PLSR models based on LASSO and stepwise feature selection demonstrated superior performance in comparison to those using BO, GA, and RFE. Specifically, for predicting PSIImax, the performance of the LASSO-based PLSR model demonstrated a marked improvement with the increasing derivative order, reaching a maximum in the 1.1-order FOD spectra with R\({}^{2}\) = 0.57, RPD = 1.53, NRMSE = 0.13, and AIC\({}_{\text{scale}}\) = \(-\)2.31, after which it experienced a decline. For NPQ prediction, the optimal model was the LASSO-based PLSR constructed using the 0.8-order FOD spectra, achieving R\({}^{2}\) = 0.60, RPD = 1.60, NRMSE = 0.16, and AIC\({}_{\text{scale}}\) = \(-\)1.60. It is noteworthy that the performance of the LASSO-based PLSR model exhibited an enhancement with an increasing derivative order up to 0.8, followed by a brief period of stabilization and a subsequent gradual decline, marked by a substantial drop after the 1.3 order. For qL prediction, both the LASSO- and stepwise-based PLSR models exhibited robust performance. However, the effectiveness of these models varied across different FOD spectra. The performance of the LASSO-based PLSR model exhibited an enhancement that was progressive in nature with an increasing derivative order, reaching a peak at the 1.0-order FOD spectra (R\({}^{2}\) = 0.55, RPD = 1.51, NRMSE = 0.14, AIC\({}_{\text{scale}}\) = \(-\)1.41) before demonstrating a descending trend. In contrast, the performance of the stepwise-based PLSR model exhibited fluctuations, initially improving, and peaking at the 0.9-order FOD spectra (R\({}^{2}\) = 0.48, RPD = 1.40, NRMSE = 0.16, AIC\({}_{\text{scale}}\) = 0.03), then declining, recovering, and achieving its optimal performance at the 1.6-order FOD spectra (R\({}^{2}\) = 0.58, RPD = 1.55, NRMSE = 0.14, AIC\({}_{\text{scale}}\) = \(-\)1.58).  
In terms of \(\Phi\)P, the LASSO-based PLSR model at the 0.9-order FOD spectra was identified as the optimal model, achieving R\({}^{2}\) = 0.73, RPD = 1.94, NRMSE = 0.11, and AIC\({}_{\text{scale}}\) = \(-\)2.00. It was observed that the performance of the LASSO-based PLSR model exhibited a consistent enhancement as the derivative order increased from 0.0 to 0.9, but a decline was noted beyond the 0.9-order spectra. In the context of predicting \(\Phi\)N, the LASSO-based PLSR model demonstrated superior performance in comparison to other feature selection methods, especially for FOD spectra from the 0.7 to 1.3 order. The optimal performance was observed at the 0.7-order FOD spectrum, with R\({}^{2}\) = 0.62, RPD = 1.62, NRMSE = 0.12, and AIC\({}_{\text{scale}}\) = \(-\)2.89. For predicting \(\Phi\)F, the maximum values of R\({}^{2}\) (0.62) and RPD (1.63) were identified in the 1.2-order FOD spectra by the LASSO-based PLSR model (Highlighted by red circles), where the NRMSE was 0.14 and the AIC\({}_{\text{scale}}\) value was \(-\)0.47. The lowest AIC\({}_{\text{scale}}\) value, \(-\)1.43 (highlighted by a red circle), was obtained using the BO-based PLSR model in the 0.5-order FOD spectra (R\({}^{2}\) = 0.43, RPD = 1.33, NRMSE = 0.17). For \(\Phi\)F, the selection was adjusted to balance model complexity and predictive ability by choosing the model with the lowest AIC\({}_{\text{scale}}\) among those with an RPD greater than 1.4. The LASSO-PLSR model using 0.8-order FOD spectra was ultimately selected for predicting \(\Phi\)F, with R\({}^{2}\) = 0.54, RPD = 1.48, NRMSE = 0.15, and AIC\({}_{\text{scale}}\) = \(-\)1.13 (highlighted by red squares).