## 1 Introduction  
Bell pepper (_Capsicum annuum_ L) is a commercially valuable crop widely cultivated for its desirable flavor. Rich in essential vitamins (A, B, C, and E) and various antioxidant phytochemicals [1], it has become a dietary staple worldwide. The global production of bell pepper reached an impressive 793,000 tons in 2021 [2], driving the demand for efficient production and high-quality sorting methods. Bell pepper quality is a complex trait influenced by factors such as color, firmness, soluble solids, dry matter, and vitamin C content [3; 4; 5]. Its nutritional value has contributed to its prominent position in global diets [4]. Prolonged fruit retention in the parent plant can induce significant physiological changes, affecting aging and nutritional composition, including total soluble solids, ascorbic acid, pH, and titratable acidity [6]. Bell pepper respiration persists post-harvest, as ripening continues [7]. Consequently, carotenoid levels increase while chlorophyll content declines, accompanied by alterations in internal composition [8; 9]. Therefore, accurate assessment of harvested product quality is crucial for effective distribution and marketing. While mechanical and manual sorting often relies on appearance, it overlooks critical internal quality attributes. Advanced non-destructive methods are increasingly employed for evaluating, grading, and sorting fruits and vegetables. These techniques, encompassing optical (image processing, spectroscopy, hyperspectral imaging, thermal imaging), mechanical (acoustic-ultrasound, vibration, and impact sound analysis), electromagnetic, X-ray (X-ray CT), dielectric, and electronic nose technologies, have shown promise in enhancing post-harvest quality and market value. Agricultural engineering has successfully leveraged advanced sensors and machine vision to overcome the limitations of traditional destructive testing methods, reducing labor costs and human error in the classification process [4]. Machine vision and spectroscopy have emerged as promising non-destructive techniques for quality control in agricultural science. These technologies find applications across diverse fields including terrestrial and aerial mapping, agricultural product monitoring, precision agriculture, robotics, autonomous navigation, product evaluation, quality assurance, and prediction of physiochemical properties [10]. Extensive research has been conducted on employing machine vision to classify bell peppers based on maturity level and size [11], measure color parameters [12], and estimate overall maturity [13]. Near-infrared (NIR) spectroscopy is a versatile analytical technique with applications spanning medicine, agriculture, chemistry, and other fields. This environmentally friendly method offers a cost-effective alternative to traditional approaches, often eliminating the need for sample preparation [14]. In the realm of fruit analysis, NIR spectroscopy provides valuable spectral data that can be used to assess physiochemical properties and detect defects [15]. Recent studies have demonstrated the potential of non-destructive techniques for evaluating various quality parameters in fruits and vegetables. NIR spectroscopy has been successfully applied to predict total soluble solids in strawberries [16]. Drying methods have been investigated for their impact on apple, plum, and their mixtures [17]. Chlorophyll fluorescence measurements have enabled the determination of ripening stages and sorting of bell pepper fruit [18]. Other studies have focused on non-destructive prediction of internal apple characteristics such as texture, acidity, and starch content [19], peach variety detection [20], eggplant chilling injury assessment [21], measurement of quality parameters of apple fruit [22], quality measurement for three varieties of tomato [23].