## 3 Results and Discussion
### Effective Wavelengths  
Support vector machine (SVM) models combined with genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and Imperialist Competitive Algorithm (ICA) were employed to determine effective wavelengths for different bell pepper varieties. Based on average correlation, mean squared error (MSE), and relative percent difference (RPD), an average of 15 effective wavelengths were identified per variety. The following parameter settings were used for wavelength selection: GA (population size: 120, generations: 10, crossover rate: 30%, mutation rate: 30%), PSO (particle count: 40, iterations: 10, personal best count: 8), ACO (ant count: 40, iterations: 10, evaporation rate: 4), and ICA (country count: 40, imperialism count: 11). Algorithm performance was evaluated using average convergence of mean RMSE, average correlation, and RPD across all samples. Convergence indicates improved results with increased iterations or computational time. Figures 3-9 and Table S2 present Feature Select outputs for quality parameters of the three studied varieties based on these criteria. PSO and ACO demonstrated decreasing average RMSE and satisfactory average correlation convergence. Considering computational efficiency, with ACO requiring more time, PSO emerges as the preferred method among the four algorithms. These findings align with previous research on effective wavelength determination for polyphenol oxidase and peroxidase enzyme activity detection in bell peppers [61]. In another study [62], visible/near-infrared (Vis/NIR) spectroscopy to assess the viability of two peanut seed types was used. To optimize wavelength selection for this assessment, they applied a variety of meta-heuristic algorithms, including World Competitive Competition (WCC), League Championship Algorithm (LCA), Genetics (GA), particle swarm optimization (PSO), ant colony optimization (ACO), Imperial Competitive Algorithm (ICA), Learning Automata (LA), Heat Transfer Optimization (HTS), Forest Optimization (FOA), Discrete Symbiotic Search (DSOS), and Cuckoo Optimization (CUK). These algorithms effectively extracted wavelength-related information from the spectral data. Notably, all algorithms demonstrated high accuracy in predicting the allometric coefficient of the seeds, achieving correlation coefficients exceeding 0.985 and errors below 0.0036. Tables S3-S5 present the effective wavelengths for red, yellow, and orange varieties, ranked by their influence on quality parameters. Wavelengths determined using the SVM-PSO method reveal spectral bands with consistent positions and shapes but notable intensity fluctuations. These bands are likely associated with the overtones and combination tones of hydrogen-containing structural groups, predominantly O-H and C-H. Originating primarily from the C-H and O-H groups within fruit constituents, these absorptions may be attributed to various compounds, including water, sugars, other carbohydrates, organic acids, polyphenols, certain vitamins, and specific amino acids [63]. Figure 3: The firmness of the three bell pepper varieties using SVM. These diagrams compare the performance of algorithms in terms of accuracy and error score. RMSE variations (**A**–**C**) and average correlation (**D**–**F**) for red, yellow, and orange varieties, respectively. Figure 4: The pH of the three bell pepper varieties using SVM. These diagrams compare the performance of algorithms in terms of accuracy and error score. RMSE variations (**A–C**) and average correlation (**D–F**) for red, yellow, and orange varieties, respectively. Figure 5: The SSC of the three bell pepper varieties using SVM. These diagrams compare the performance of algorithms in terms of accuracy and error score. RMSE variations (**A–C**) and average correlation (**D–F**) for red, yellow, and orange varieties, respectively. Figure 6: The TA of the three bell pepper varieties using SVM. These diagrams compare the performance of algorithms in terms of accuracy and error score.