Analyzing the Nitrate Content in Various Bell Pepper Varieties Through Non-Destructive Methods Using Vis/NIR Spectroscopy Enhanced by Metaheuristic Algorithms

Destructive methods, though traditionally used to evaluate fruit safety, frequently do not deliver complete and detailed information. Non-destructive methods, especially spectroscopy, provide an effective solution for fast, efficient, and non-invasive assessments of quality and safety. This study utilized visible and near-infrared (Vis-NIR) spectroscopy to quantify the nitrate content in three cultivars of bell pepper—orange, yellow, and red—across a spectral range spanning 350 to 1150 nanometers. The nitrate content was assessed destructively, and spectral data were examined through partial least squares regression (PLSR). Model efficacy was measured using the root mean square error (RMSE) and coefficient of determination (R2). The R2 values, indicative of the model’s predictive efficacy, were determined to be 0.77, 0.85, and 0.81 for the yellow, red, and orange types, respectively. To optimize wavelength selection and improve model performance, a hybrid approach was utilized, integrating a support vector machine (SVM) with four meta-heuristic algorithms: particle swarm optimization (PSO), genetic algorithm (GA), imperialistic competitive algorithm (ICA), and ant colony optimization (ACO). The SVM-PSO approach proved to be the most efficient in pinpointing 15 key wavelengths. Following this, three modeling techniques—PLSR, multiple linear regression (MLR), and artificial neural network (ANN)—were utilized with the identified wavelengths. Among these, ANN represented the best performance, achieving validation R2 values of 0.99, 0.97, and 0.92 for the yellow, red, and orange varieties, respectively. Compared to traditional PLSR and MLR models, which reached validation R2 values up to 0.93, the ANN model demonstrated a significant improvement in prediction accuracy. This quantitative improvement highlights the advantage of combining hybrid meta-heuristic wavelength selection with ANN modeling. The results underscore the promise of visible/near-infrared (Vis/NIR) spectroscopy, integrated with sophisticated modeling approaches, as an effective non-invasive method for estimating nitrate concentrations in bell peppers. This technique represents a significant advancement in supporting food safety measures and quality assurance processes.