Intelligent and antimicrobial packaging technologies are transforming meat preservation by enhancing food safety, enabling real-time quality monitoring, and extending shelf life. This review critically examines advancements in intelligent systems, including radio frequency identification (RFID), gas sensors, time-temperature indicators (TTIs), and colorimetric indicators for continuous freshness assessment. A key focus is natural compound-based chromogenic indicators, which establish visual spoilage detection via distinct color transitions. Concurrently, antimicrobial systems integrating inorganic compounds, organic bioactive agents, and natural antimicrobials effectively inhibit microbial growth. Strategic incorporation of these agents into polymeric matrices enhances meat safety, supported by standardized evaluation protocols for regulatory compliance and quality assurance. Future research should prioritize optimizing sensitivity, cost-efficiency, and sustainability, alongside developing biodegradable materials to balance food safety with reduced environmental impact, advancing sustainable food supply chains.

Mass transfer is central to food processing but remains difficult to quantify because food materials are heterogeneous, multiphase, porous, biologically structured, and dynamically changing. Under these conditions, experiments alone cannot fully capture the spatiotemporal complexity of transport behavior, making modeling and simulation essential for mechanism interpretation, process prediction, and engineering optimization. Existing reviews mainly address specific operations or numerical methods, with limited synthesis of governing equations, simulation workflows, application implementation, and practical applicability. This review examines food mass transfer by linking coupled momentum, heat, and mass transfer laws with governing equation selection, simulation workflow, and representative food processing applications. Governing formulations for Fickian diffusion, conservation-based transport, heat–mass coupling, multicomponent transfer, Darcy-type porous-medium flow, and related model extensions are summarized, together with their assumptions, geometric applicability, and dimensionless criteria. A unified simulation workflow is then organized, covering transport type identification, governing equation and physical model selection, geometric representation, parameter determination, initial and boundary condition specifications, numerical method and simulation tool selection, numerical implementation, validation, and transferability assessment. Representative applications are discussed for drying, heat–mass coupled processes, multicomponent transfer, transport in porous foods, and redistribution in multi-ingredient or multilayer foods. Overall, future progress requires more integrated, structure-aware, experimentally validated, transferable, and application-oriented simulation frameworks.

Chinese yam (Dioscorea spp.) is widely used as food and traditional medicine, but varietal differences may affect dried yam slice quality. This study compared ten Chinese yam varieties, examined relationships between raw-material quality indicators and dried yam slice quality, and evaluated hyperspectral imaging for rapid assessment before processing. Significant varietal differences were observed in reducing sugar content, total phenolic content, and texture properties (p < 0.05), and trait–quality relationships were variety-dependent. Spectral preprocessing, variable selection, and regression modelling were used to predict the reference values of reducing sugar content and total phenolic content obtained using the specified analytical procedures, together with fresh-slice hardness. The best models combined principal component analysis with decision tree regression (PCA-DTR), competitive adaptive reweighted sampling with partial least squares regression (CARS-PLSR), and CARS with random forest regression (CARS-RFR), respectively. Prediction-set coefficients of determination ($R_P^2$) were 0.9891, 0.9335, and 0.9314, with root mean square errors of prediction (RMSEP) of 0.0981%, 0.0905 mg gallic acid equivalents/100 g dry weight, and 130.3973 gf, and residual predictive deviation (RPD) values of 9.7535, 3.9438, and 3.8820, respectively. These results support hyperspectral imaging with chemometrics for rapid assessment and selection of yam raw materials for dried yam slice processing.