فرهاد خوشنام

Quality degradation during drying fruits and vegetables is mainly due to challenges in adaptation of advanced quality monitoring and control during processing. This research explores non-invasive and invasive measurements to analyze the quality of purple carrots, golden kiwifruit, blueberry and raspberry during drying. Quantitative changes in moisture content (MC), total anthocyanin (TA), β-Carotene (BC), lutein, vitamin-C (VC), total phenolic contents (TPC) and total flavonoids (TF) along with colorimetric and physical changes were evaluated and compared with 3D point cloud based digital data. First-order kinetic model provided better fits (R2B C = 0.96, R2V C = 0.99, and R2M C = 0.96), confirming first-order degradation behavior during drying. The integration of kinetic modeling (zero- and first-order) with machine learning enabled accurate prediction of drying-induced quality changes in selected products. Hybrid gradient boost regressor (Hybrid-GBR) achieved the best results for MC across all products, with Rp2 = 0.988 (RMSEP ≈ 0.037) for purple carrot, Rp2 = 0.963 (RMSEP = 0.068) for raspberry, and Rp2 = 0.980 (RMSEP = 0.041) for blueberry. For secondary metabolites (TA and TPC), hybrid Gaussian process regressor (Hybrid-GPR) and Hybrid-GBR models consistently outperformed conventional methods, resulting in test Rp2 = 0.901, RMSEP = 50.4 (TA, raspberry) and Rp2 = 0.867, RMSEP = 0.067 (BC, blueberry). For vitamin C in golden kiwifruit, Hybrid-PLSR performed best (Rp2 = 0.905, RMSEP = 67.9). These findings show that low-cost 3D point cloud imaging can be integrated with spectral imaging with broader dataset for more accurate, real-time quality monitoring for dynamic optimization of drying process.