Fruits and vegetables are highly nutritious agricultural produce with tremendous human health benefits. They are also highly perishable and as such are easily susceptible to spoilage, leading to a reduction in quality attributes and induced food loss. Cold chain technologies have over the years been employed to reduce the quality loss of fruits and vegetables from farm to fork. However, a high amount of losses (≈50%) still occur during the packaging, pre-cooling, transportation, and storage of these fresh agricultural produce. This study highlights the current state-of-the-art of various advanced tools employed to reducing the quality loss of fruits and vegetables during the packaging, storage, and transportation cold chain operations, including the application of imaging technology, spectroscopy, multi-sensors, electronic nose, radio frequency identification, printed sensors, acoustic impulse response, and mathematical models. It is shown that computer vision, hyperspectral imaging, multispectral imaging, spectroscopy, X-ray imaging, and mathematical models are well established in monitoring and optimizing process parameters that affect food quality attributes during cold chain operations. We also identified the Internet of Things (IoT) and virtual representation models of a particular fresh produce (digital twins) as emerging technologies that can help monitor and control the uncharted quality evolution during its postharvest life. These advances can help diagnose and take measures against potential problems affecting the quality of fresh produce in the supply chains. Plausible future pathways to further develop these emerging technologies and help in the significant reduction of food losses in the supply chain of fresh produce are discussed. Future research should be directed towards integrating IoT and digital twins for multiple shipments in order to intensify real-time monitoring of the cold chain environmental conditions, and the eventual optimization of the postharvest supply chains. This study gives promising insight towards the use of advanced technologies in reducing losses in the postharvest supply chain of fruits and vegetables.

This study examines the potential of applying computational intelligence modelling to describe the drying kinetics of persimmon fruit slices during vacuum drying (VD) and hot-air-drying (HAD) under different drying temperatures of 50 °C, 60 °C and 70 °C and samples thicknesses of 5 mm and 8 mm. Kinetic models were developed using selected thin layer models and computational intelligence methods including multi-layer feed-forward artificial neural network (ANN), support vector machine (SVM) and k-nearest neighbors (kNN). The statistical indicators of the coefficient of determination (R²) and root mean square error (RMSE) were used to evaluate the suitability of the models. The effective moisture diffusivity and activation energy varied between 1.417 × 10⁻⁹ m²/s and 1.925 × 10⁻⁸ m²/s and 34.1560 kJ/mol to 64.2895 kJ/mol, respectively. The thin-layer models illustrated that page and logarithmic model can adequately describe the drying kinetics of persimmon sliced samples with R² values (>0.9900) and lowest RMSE (<0.0200). The ANN, SVM and kNN models showed R² and RMSE values of 0.9994, 1.0000, 0.9327, 0.0124, 0.0004 and 0.1271, respectively. The validation results indicated good agreement between the predicted values obtained from the computational intelligence methods and the experimental moisture ratio data. Based on the study results, computational intelligence methods can reliably be used to describe the drying kinetics of persimmon fruit.

The effect of heating and freezing pretreatments on rapeseed oil yield and the volume of oil energy under uniaxial compression loading was investigated. Four separate experiments were carried out to achieve the study objective. The first and second experiments were performed to determine the compression parameters (deformation, mass of oil, oil yield, oil expression efficiency, energy, volume of oil and volume of oil energy). The third and fourth experiments identified the optimal factors (heating temperatures: 40, 60 and 80 °C, freezing temperatures: −2, −22 and −36 °C, heating times: 15, 30 and 45 min and speeds: 5, 10 and 15 mm/min) using the Box–Behnken design via the response surface methodology where the oil yield and volume of oil energy were the main responses. The optimal operating factors for obtaining a volume of oil energy of 0.0443 kJ/mL were a heating temperature of 40 °C, heating time of 45 min and speed of 15 mm/min. The volume of oil energy of 0.169 kJ/mL was reached at the optimal conditions of a freezing temperature of −36 °C, freezing time of 37.5 min and speed of 15 mm/min. The regression model established was adequate for predicting the volume of oil energy only under heating conditions.

Via image-based macroscopic, analysis of a briquettes’ surface structure, particle size, and distribution was determined to better understand the behavioural pattern of input material during agglomeration in the pressing chamber of a briquetting machine. The briquettes, made of miscanthus, industrial hemp and pine sawdust were produced by a hydraulic piston press. Their structure was visualized by a stereomicroscope equipped with a digital camera and software for image analysis and data measurements. In total, 90 images of surface structure were obtained and quantitatively analysed. Using Nikon Instruments Software (NIS)-Elements software, the length and area of 900 particles were measured and statistically tested to compare the size of the particles at different surface locations. Results showed statistically significant differences in particles’ size distribution: larger particles were generally on the front side of briquettes and vice versa, smaller particles were on the rear side. As well, larger particles were centred in the middle of cross sections and the smaller particles were centred on the bottom of the briquette.

The effects of different drying temperatures (50, 60, 70 °C) on the quality of rose (Rose electron) petals were evaluated in this study. Drying time decreased from 1680 s to 600 s with increased infrared temperature. The temperature and time were increased from 50 °C to 70 °C and 30 min to 60 min, respectively, and a decrease in the fruit color quality was observed. The projected area (PA) of rose petals was affected significantly from temperature. After the drying process, the largest PA was observed as 33.35 cm² (50 °C, 30 min), while the smallest achieved at 70 °C, 60 min (27.96 cm²). Depending on the temperature values (50, 60, 70 °C), the average projection area of dry samples of the rose petals decreased 2.17 times compared to the projection area of fresh samples. The dried samples demonstrated an increase in the total phenolic (TP) content compared to the fresh samples. The maximum TP (44.49 mg GAE/g) was achieved at 45 min and 70 °C rose petals sample. The results concluded that infrared drying for 45 min at 70 °C could be recommended for drying rose (rosa electron) petals.