## 3 Results
### Feature Wavelength Selection
#### 3.2.2 Methodology for Feature Wavelength Point Screening  
(1) Selection of Feature Points Based on IRIV  
The IRIV algorithm was utilized for feature wavelength point selection on the full spectrum region. The selection process incorporated establishing a PLS prediction model using 5-fold cross-validation and using RMSECV as the evaluation metric to classify informative variables. During each iteration, uninformative and interfering variables were eliminated, while the relevant feature variables were retained. The analysis of the selection process revealed a substantial reduction in the number of variables: from 2074 to 241 after three iterations. By the fifth iteration, uninformative and interfering variables were successfully eradicated, leading to the final retention of 27 feature variables through backward selection. The variable selection process is demonstrated in Figure 6a, and the chosen feature wavelengths are presented in Figure 6b. (2) Selection of Feature Points Based on VCPA  
Feature interval selection was accomplished using the VCP algorithm, configured with a Decreasing Exponential Function (EDF) for the VCPA feature variable selection index. This process entailed 50 iterations of Binary Matrix Sampling (BMS) with a mean of 100 samples for each iteration. An optimal subset ratio was established at 10%, yielding  
Figure 5: Feature wavelength selection using BiPLS algorithm. (**a**) Screening feature interval. (**b**) RMSECV as a function of the number of principal Components. Figure 6: Feature wavelength selection process and distribution using IRIV algorithm. (**a**) Selection process. (**b**) Distribution of feature wavelengths. 100 remaining variables following the execution of EDF. A PLS model was constructed using 5-fold cross-validation, with RMSECV serving as the evaluation metric to gauge the modeling performance of randomly partitioned subsets and choose the optimal subset. Throughout the 50 iterations, the EDF process purged variables with minimal contribution rates. The RMSECV was calculated for all variable combinations among the remaining variables, and the variable combination with the lowest RMSECV was chosen as the optimal subset. Ultimately, 12 feature wavelength points were selected. The variable selection process and the dispersion of feature wavelength points for VCPA are portrayed in Figure 7.  
(3) Selection of Feature Points Based on RF  
The initialization parameters of RF were configured, setting the algorithm iteration count at 1000 and the number of variables encompassed within the initial subset of random frog-leaping to five. Through this process, the selection probability for each wavelength was calculated. Wavelengths demonstrating a lower likelihood of selection during the iteration were indicative of interfering or redundant information within the full spectral data. After 604 iterations, the error was found to be at its minimal point. Adopting the principle that higher selection probability equates to greater variable importance, all variables were ranked according to their respective probabilities. Subsequently, RF concluded with the screening of 50 feature variables. Figure 8 illustrates the variable screening process via the RF algorithm and the dispersion of characteristic wavelength points. Figure 8: The process and dispersion of feature wavelength selection employing the RF algorithm. **(a)** Process of variable selection. **(b)** Selection of feature variables via RF. Figure 7: Feature wavelength selection process and distribution using VCPA algorithm. **(a)** Selection process. **(b)** Distribution of feature wavelength. (4) Feature point selection based on CARS  
The feature interval selection process and distribution employing the CARS algorithm are depicted in Figure 9. The quantity of selected variables declines as the number of Monte Carlo samples escalates.