Food gels are soft materials in which three-dimensional (3D) networks of proteins, polysaccharides, lipids, or their composites immobilize water, oil, or gas and provide tunable rheological, mechanical, and functional properties. Performance assessment remains inconsistent across studies, as researchers often report gel strength or water-holding capacity alone and rarely compare different gel systems using common criteria. The assumption that denser networks perform better also remains largely untested. This review organizes the field around a cross-scale pathway from external triggers and intermolecular interactions to mesoscale networks and macroscopic functions. It applies this framework to hydrogels, oleogels, and bigels and links their preparation methods to multiscale characterization. Network densification does not necessarily improve performance: it may enhance water-holding capacity and barrier properties but also restrict flavor release, reduce enzyme accessibility, and compromise swallowing safety. Therefore, the optimal network structure should be defined for each application rather than maximized. These trade-offs are relevant to delivery systems, fat replacement, 3D printing, edible coatings, and intelligent indicators. Future research should establish unified structure–digestion–sensory metrics, validate data-driven models externally, and test gel performance under realistic storage and use conditions.

Postharvest fruit quality differs among various fruits and alters during sorting, packaging, transportation and storage. Therefore, it is important to have an efficient and objective grading method that causes little mechanical damage to ensure the uniformity of products and decrease market price and losses in the supply chain. This paper describes the grading norms, detection techniques, processing algorithms, structural designs and practical uses in different types of fruits. It examines the external features, internal quality and hidden faults by using machine vision, visible-near-infrared spectroscopy, hyperspectral and X-ray imaging, acoustic and mechanical detection, electronic noses and the integration of multiple sensors. In addition, it also investigates conventional machine learning, deep learning, transfer learning and lightweight implementations. The research has developed from grading according to size, weight and color to a complete evaluation of ripeness, juice volume, hardness, internal defects and shelf life. Moreover, single detection devices are joined together to construct integrated systems including feeding, separation, inspection, classification, redirection, packaging and data management. However, the application is restricted by the discrepancy between the grading criteria and measurable results, the lack of cross-species and batch generalization ability, and the difficulty in coordinating multiple sensors in real time. The systems should maintain a balance between production speed, mechanical damage and costs. Some matters needing attention are to standardize the quality description, choose multi-source fusion, develop adaptive lightweight models, design modular structures and guarantee end-to-end traceability. Solving these problems will facilitate the transition from accurate laboratory identification to reliable, economic and extensive commercial grading.