## 4 Conclusions  
This study employed Vis-NIR spectroscopy in absorbance mode for the non-destructive evaluation of quality properties in three bell pepper varieties within the spectral range of 350-1150 nm. To enhance data analysis, principal component analysis (PCA) was used for dimensionality reduction, and [PERSON]'s T\({}^{2}\) test identified outliers. Partial least squares (PLS) regression determined the optimal model for the full dataset. To identify effective wavelengths, the support vector machine (SVM) method was combined with genetic algorithm (GA),particle swarm optimization (PSO), ant colony optimization (ACO), and independent component analysis (ICA) metaheuristic optimization techniques. Subsequently, PLS, multiple linear regression (MLR), and artificial neural networks (ANNs) were employed for modeling using the selected wavelengths. Each algorithm identified an average of 15 wavelengths. SVM-PSO was chosen for non-destructive quality evaluation based on its superior correlation, fewer selected wavelengths, and faster computation time. While ANN exhibited the best performance in terms of validation R\({}^{2}\), root mean square error (RMSE), and relative prediction deviation (RPD), it was selected as the optimal prediction model. This research demonstrates the practical applicability of combining Vis/NIR spectroscopy with ANN and SVM-PSO variable selection for non-destructive quality evaluation in vegetables. The identified effective wavelengths hold potential for designing or optimizing spectroscopic instruments to predict quality properties in bell peppers.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app142310855/s1](https://www.mdpi.com/article/10.3390/app142310855/s1)]([https://www.mdpi.com/article/10.3390/app142310855/s1](https://www.mdpi.com/article/10.3390/app142310855/s1)), Table S1: R\({}^{2}\), RMSE and RPD values for calibration and validation sets of the regression model; Table S2: Average values of correlation, RMSE and RPD of meta-heuristic algorithms; Table S3: Effective wavelengths of the red varieties determined by different algorithms; Table S4: Effective wavelengths of the yellow varieties determined by different algorithms; Table S5: Effective wavelengths of the orange varieties determined by different algorithms; Table S6: R\({}^{2}\), RMSE and RPD value of the calibration and validation datasets of PLSR and MLR methods created based on the effective wavelengths; Table S7: R\({}^{2}\), RMSE and RPD value of the training and validation datasets of ANN method created based on the effective wavelengths.  
M.L.A.: Conceptualization, data curation, data analysis and writing--original draft preparation; Y.A.-G.: Investigation, supervision, conceptualization, data curation, data analysis and writing--original draft preparation; M.T. and M.K.: Methodology, validation, writing--review and editing, and investigation; H.S.E.-M.: Methodology, validation, writing--review and editing, and investigation; M.S. ([PERSON]): Investigation, resources, funding acquisition, writing--review and editing; M.S. ([PERSON]): Formal analysis, resources, funding acquisition, project administration and writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.  
Not applicable.  
Data Availability Statement: All data and materials are available upon reasonable request from the corresponding author.