After harvesting, fruits and vegetables (F&Vs) suffer over 30% losses during transportation and distribution due to vigorous internal metabolism and external microbial invasion. Traditional packaging and preservation technologies are limited by high energy consumption, heavy pollution, and single functionality. Metal-organic frameworks (MOFs), with ultra-high specific surface area, adjustable structure, and diverse functions, have become a core direction for novel collaborative preservation and show great potential as carriers for next-generation preservation technologies for F&Vs. This review first analyzed the research overview of MOF-based collaborative preservation via bibliometric analysis, then briefly outlined MOF classification, design principles, synthesis methods, and parameter control. It elaborates on preservation mechanisms in terms of gas environment regulation, antibacterial activity, environmental stress protection, active substance loading and stabilization, intelligent quality detection and response, and synergy with other technologies. Furthermore, it highlighted the application progress in gas regulation, microbial control, preservation packaging optimization, and multifunctional packaging integrating quality monitoring and preservation. Despite advantages of multi-mechanism collaboration, green sustainability, and artificial intelligence adaptability, MOF-based preservation faces challenges including toxicity verification, scaling costs, and lack of standards. In this regard, this review innovatively proposes strategic pathways toward green synthesis, AI-driven material design, and industrial translation. This work offers a foundational reference for advancing MOF-integrated postharvest F&V preservation from laboratory innovation to commercial deployment, contributing to global food security and sustainability objectives.

Accurately predicting key freshness indicators is essential for ensuring the safety and quality of aquatic products. This study developed an intelligent freshness assessment system by integrating electronic nose (E-nose) detection of volatile organic compounds with low-field nuclear magnetic resonance (LF-NMR) analysis of water phase states, coupled with machine learning modeling. Using refrigerated salmon and shrimp as models, the system achieved accurate prediction of total viable count (TVC), total volatile basic nitrogen (TVB-N), and thiobarbituric acid-reactive substances (TBARS). The bimodal data effectively captured synergistic spoilage characteristics, namely the exponential rise of volatile amines/sulfides and the concurrent decline of immobile water content. Among 12 machine learning algorithms evaluated, tree-based ensemble models, particularly decision tree and random forest regression, delivered optimal prediction performance, with R2 values exceeding 0.95 for all three indicators in both species. The proposed system successfully establishes a quantitative link between multi-source sensing signals and key biochemical freshness indices, offering a practical shift from traditional subjective grading toward real-time, numerical early warning in the aquatic cold chain. It should be noted that this study was conducted under controlled laboratory conditions, further validation is required to assess system performance under real-world supply chain variability, including temperature fluctuations, packaging differences, and broader species coverage.

The present investigation aimed to find the optimal conditions for the spray drying conditions of finger millet milk blended with jamun juice, with a novel approach of producing the powder without any additional carrier agents. A three-factor three-level RSM design was employed to optimize inlet spray drying temperature (T; 150-170 degrees C), ratio of millet milk to jamun juice (MJ; 1-3) and flow rate (FR; 150-250 mL/h) based on powder yield, color change (Delta E), total phenolic content (TPC), DPPH assay (DPPH) and solubility (S). The optimum drying conditions were determined as 150 degrees C inlet spray drying temperature, 212.89 mL/h flow rate and 1:1 MJ ratio, yielding a desirability of 0.995. Under these conditions, the product showed a powder yield of 34.07%, high total phenolic content (119.34 g GAE/100g), 80.13% DPPH, Delta E of 22.84 and solubility of 55.02%. Structural and molecular characterization of the optimal powder using SEM, XRD and FTIR confirmed amorphous morphology with retained bioactive compounds. The developed product contains the nutritional benefits of both finger millet and jamun, offering potential for use in the development of value-added food products.

Demand and the urgent need for energy conservation and sustainable resource management. Addressing this challenge requires desalination systems that are both energy-efficient and adaptable to climate variability. This study proposes a hybrid seawater greenhouse (SWGH) system that integrates humidification–dehumidification (HDH) desalination with a pyramid solar still (PSS) through thermal coupling to enhance overall energy utilization and freshwater productivity. To enable long-term performance assessment, convolutional neural network (CNN), gated recurrent unit (GRU), and hybrid CNN–GRU deep learning models were developed to predict freshwater production using historical and projected climatic data, including global horizontal irradiation (GHI) and air temperature (T2M), over the period 1985–2034. The CNN–GRU model demonstrated superior predictive performance and was coupled with thermodynamic simulations of the hybrid system to estimate long-term freshwater yield. Results indicate an annual cumulative freshwater production of approximately 4250-4400 L/m²·year. The HDH subsystem, with a surface area of 300 m², contributed 1450–1500 L/m²·year, while the 150 m² solar still unit produced 2800–2900 L/m²·year annually. These findings confirm that thermal integration between the desalination units significantly improves overall freshwater production while reducing thermal losses. The novelty of this work lies in the combined application of a CNN–GRU predictive framework with a thermally integrated hybrid solar desalination system, enabling reliable long-term forecasting under climate-sensitive conditions. The proposed approach provides a scalable, energy-efficient solution for sustainable freshwater generation and offers a data-driven decision-support tool for adaptive water management in arid coastal environments.

Background Foods for gastrointestinal target (FGIT) are becoming increasingly important in precision nutrition and personalized therapies due to their ability to localize, load and protect functional components and controlled release. 3D printing (3DP) technology offers a solution to customize FGIT with complex structures and precise modulation capabilities for high flexibility, intervention effects, and targeted delivery. Scope and approach This paper reviewed recent research advances in applying 3DP technology to the development of FGIT. The background and current status of FGIT was summarized from the perspectives of the gastrointestinal (GI) microenvironment, current forms and shortcomings of FGIT. The technological fundamentals of 3DP technologies that supported the development of FGIT were presented. The release mechanisms and potential applications of FGIT through 3DP technology were thoroughly analyzed. Finally, we explored the advantages and challenges of combining 3DP with FGIT. Key findings and conclusions The bioavailability of functional ingredients is influenced by the retention time of FGIT, pH, temperature, mucus, enzymes and symbiotic microorganisms of GI. Current FGIT predominantly exist in forms of gels, emulsions, microcapsules, and microspheres, exhibiting limitations with limited structural diversity, inflexible formulation design, low standardization and acceptance. 3DP technologies (powder-based, light-assisted, and extrusion-based) enable precise regulation of manufacturing and function of FGIT through pH-responsive, temperature-sensitive, and core-shell modification, featuring smart response and flexible modulation for the improve of functionality and bioavailability. Future research priorities need to focus on therapeutic efficacy, consumer acceptance, technological innovation, and legislative regulation. This review will provide reference implications for interdisciplinary research to advance functional ingredient delivery and personalized medicine.

The optimization of the extrusion process for coaxial three-dimensional (3D) printing of six fruit, vegetable, and grain gels (apple, kale, carrot, oat, Koshihikari rice, and brown rice) and four polysaccharide hydrogels (guar, fenugreek, flaxseed, and konjac gums) was investigated through rheological characterization, computational fluid dynamics (CFD) simulations, and printing validation experiments. All gels were found to exhibit shearthinning properties by Power-law model fitting. Koshihikari rice gel had the highest consistency coefficient (1165.58 Pa & sdot;sn), apple gel had the lowest consistency coefficient (97.59 Pa & sdot;sn), the polysaccharide hydrogel species of 10% konjac gum had the highest consistency coefficient (2523.57 Pa & sdot;sn), and carrot gel had the lowest flow index (0.09). Dynamic rheological analysis showed that the storage modulus (G ') was higher than the loss modulus (G '') for fruit, vegetable, and grain gels, indicating a solid-like behavior. Moreover, a combination of CFD simulations emphasized the relationship between rheological parameters (K, n) and extrusion pressure. Koshihikari rice gel and 10% konjac gum with high K values required high extrusion pressures (more than 40,000 Pa), whereas carrot gel with low n values (0.09) was more prone to fast extrusion due to the strong shearthinning effect. Printing experiments showed that viscosity gradient combinations (shell slightly higher than core) can optimize molding stability. In addition, the combination of high starch (grains) and high moisture (fruit and vegetables) materials can reduce shrinkage and cracking caused by changes in ambient humidity. This study provides a theoretical basis for material selection and process optimization for coaxial 3D printing of food products.

This study presents a comprehensive economic and life cycle analysis of a multi-energy system designed for the simultaneous generation of power, heat, cooling, and hydrogen in drying systems. The research evaluates the system's performance by focusing on key economic indicators such as Levelized Cost of Energy (LCOE), Internal Rate of Return (IRR), and Discounted Payback Time (DPT), while also examining environmental impacts through life cycle assessment (LCA) metrics. Additionally, the study assesses water demand, global warming potential, and other environmental effects. Results indicate that the system provides significant advantages in terms of efficiency and environmental sustainability, with solar Direct Normal Irradiation (DNI) being a critical factor influencing the system's economic performance. As the input temperature of the air turbine increases, the LCOE decreases to 0.0878 $/kWh, while the IRR reaches 27%. The liquid air storage tank (LAST) accounts for 25% of the total cost rate, making it the largest cost contributor. Sensitivity analysis reveals that higher DNI levels increase IRR and extend DPT, and they also cause a linear rise in LCOE, which reaches up to 0.0946 $/kWh. Overall, despite the relatively high initial investment, the integrated MES demonstrates strong long-term economic viability and notable reductions in environmental impacts, supporting its potential as a sustainable energy solution for future drying and industrial systems.

Fresh-cut cucumber (FCC), as one of the most important raw material in vegetable salads, is highly susceptible to quality problems during storage. High voltage electrostatic field (HVEF), as a non-thermal, low energy consumption, and residue free physical field technology, can effectively control the microbial content in food. Electromagnetic fields (EMF) have also been found to have significant effects on microbial proliferation. In this study, a combination of HVEF and EMF was used to treat FCCs in modified atmosphere packaging (MAP) and their storage quality was monitored. It was found that the combination of HVEF and EMF treatment under appropriate parameter conditions can significantly reduce the initial microbial content in FCCs, from 3.71 Log CFU/g to <2.30 Log CFU/g, without having a negative impact on the original cell structure and enzyme system. Combined with MAP, it can effectively reduce the damage to the cell structure of FCCs during storage, thereby reducing weight loss rate and delaying the changes in pH, conductivity, and total soluble solid content during storage. In addition, delaying the changes in peroxidase, polyphenol oxidase and lipoxygenase enzyme activity could also reduce the generation of malondialdehyde and the membrane lipid oxidation and structural damage it brings, reduce the loss of chlorophyll, ascorbic acid, and phenolic substances, and ultimately maintain the sensory characteristics of cucumber slices such as color, texture, appearance, and flavor. Compared with the control group (6 days), the combination of HVEF and EMF treatment with MAP can effectively extend the shelf life of FCCs to 9-12 days.

The real-time monitoring of aquatic product spoilage is vital for global food safety. This review explores intelligent detection systems that integrate advanced sensors with novel equipment to track key spoilage markers like trimethylamine and pH shifts. These integrated systems utilize colorimetric, electrical, or spectroscopic signals to achieve high sensitivity, with some demonstrating detection limits below 10 ppm for target volatiles. By incorporating artificial intelligence, the equipment can perform non-destructive freshness analysis in real-time, achieving accuracy rates over 90

In the context of the global green transformation of agricultural practices, the innovation in sustainable drying technologies for agricultural products has become a critical pathway to ensure food security and achieve carbon neutrality. This paper systematically reviews the latest research progress, core challenges, and future development directions in this field. Based on the theoretical framework of heat and mass transfer mechanisms and drying kinetic models, it focuses on the breakthroughs of low-carbon technologies, such as solar assisted drying, phase change material heat storage, waste heat recovery, intelligent control, and novel pretreatment technologies, revealing their potentials in energy efficiency improvement and quality assurance. This study reveals that the existing technologies still face challenges such as high energy consumption, high carbon emission intensity, poor raw material adaptability, economic bottlenecks, and insufficient multi-field coupling mechanisms and experimental verification of drying models. In the future, it will be necessary to promote technological upgrading through integration of multiple energy sources, intelligent algorithms, and standardization systems, while optimizing system sustainability by leveraging whole life cycle evaluation and 4E analysis. This study provides theoretical support and solutions for the low-carbon, intelligent, and large-scale application of drying technologies for agricultural products.

Water vapor severely interferes with colorimetric/fluorescent gas sensors, undermining their accuracy in real-world applications. A universal, customizable framework for humidity compensation remains elusive. To address this challenge, a paradigm shift is proposed in which humidity responses are actively exploited for signal compensation rather than being suppressed. This study developed a fluorescent sensor array comprising 30 sensing units, establishing a hybrid composite architecture. This architecture integrates pH-indicating dyes (hemopyrrole hydrochloride, puerarin, fisetin) sensitive to spoilage-related volatile acidic/alkaline gases, combined with a dedicated riboflavin-based humidity label exhibiting highly reversible humidity response capabilities. This hybrid composite system combines organic fluorescent dyes with a PTFE microporous membrane support layer and a laminated polyethylene cover film, forming a multi-level, functionally integrated sensing module. This sensor module was affixed to the top-space inner surface of packaging for longan, button mushrooms, and snap beans. Fluorescence images captured with a smartphone were processed by a multitask deep convolutional neural network, enabling simultaneous, nondestructive tri-level classification of both freshness (fresh/sub-fresh/spoiled) and storage humidity (low/optimal/high). The model achieved 97.67

To achieve rapid, nondestructive, and precise measurement of moisture content during freeze-drying of apple slices, this study compared the performance of near-infrared spectroscopy (NIR) combined with various pretreatment-feature selection methods. Several machine learning approaches were compared as well. Savitsky-Golay (SG) and the standard normal variable transformation (SNV) were selected for the pretreatment of NIR spectra acquired during apple freeze-drying. Feature wavelengths were extracted using isometric variable selection methods such as the Competitive Adaptive Reweighted Sampling (CARS), Kmeans, Synergistic Partial Least Squares (si-PLS), and the Stochastic and Continuous Projection Algorithm (SPA). Finally, for the multivariate quantitative analysis of NIR data, 12 different machine learning algorithms were used for modeling. Results indicate that different combinations of pretreatment/feature wavelength selection and modeling methods lead to significant variations in predictive performance. Except for XGB, nonlinear regression models consistently outperformed linear regression models. Furthermore, the gain effects of pretreatment and feature wavelength selection were more pronounced in linear regression models. Among these, the SG-SNV-SPA-RFR model demonstrated optimal predictive performance (RMSEP = 0.0293, Rp2 = 0.9876). This study demonstrated that the combination of NIR technology with machine learning can be employed for intelligent nondestructive monitoring of moisture content changes during the freeze-drying process, providing a promising strategy for intelligent freeze-drying of fruits and vegetables.

Conventional hot water blanching may cause nutrient loss and tissue softening in freeze-dried apples. Therefore, this study employed physical fields (ultrasound/microwave) as pre-treatment methods for apples before freeze-drying, investigating the effects of different pre-treatment techniques on apple drying kinetics, microporosity, and total phenolic content. Additionally, a combined pretreatment method of mannitol immersion coupled with ultrasound/microwave treatment was proposed, and the mechanism by which mannitol enhances ultrasonic/microwave pretreatment was elucidated. Results indicated that physical field pretreatment reduced drying time by 15-20%. However, ultrasound pretreatment decreased total phenolic content, while microwave pretreatment caused macroscopic shrinkage in freeze-dried apples (shrinkage rate >30%). Furthermore, X-ray micro-computed tomography and Fourier transform infrared spectroscopy revealed that hot water blanching, ultrasound, and microwaving all caused varying degrees of damage to apple cell tissue, leading to differences in macroscopic properties. The addition of mannitol significantly increased phenolic retention in ultrasonic-pretreated samples by maintaining cell wall structure and regulating ice crystal formation, while also mitigating shrinkage in microwave-pretreated samples (shrinkage rate <10%). Thus, mannitol immersion combined with ultrasound/microwave pretreatment offers an efficient method for freeze-dried fruits and vegetables.

Background Small berries are rich in bioactive compounds and possess high nutritional value, while the high perishability of fresh small berries severely limits their consumptions and utilizations. Drying is an effective preservation strategy that can extend the shelf life and enhance their added values. Notably, the cuticular wax layer of small berries impedes the moisture migration and thus results in serious degradation of bioactive compounds during drying. Scope and approach This review systematically describes the criteria of dried small berries and summarizes both conventional and emerging drying technologies, focusing on the dehydration principles, practical applications, and their respective limitations. A critical comparison of these drying techniques is conducted, particularly regarding their effects on the retention of bioactive compounds, physical structure, and sensory properties of dried small berries. In parallel, it analyzes the impact mechanism of various pretreatment techniques on optimizing the drying processes. The performance of various emerging drying technologies in processing small berries is further compared in commercial level. Finally, the optimal strategies for processing small berries are proposed. Key findings and conclusion Appropriate pretreatments can disrupt the wax layer structure, enhance cell membrane permeability, and inactivate oxidative enzymes, thereby significantly improving drying efficiency and product quality. Emerging drying technologies combined with environmentally friendly pretreatment not only enables the production of high-quality dried small berries but also enhances the process sustainability through minimized water pollution, reduced carbon emissions, and efficient use of energy resources. However, how to balance the drying efficiency, product quality, and energy consumption during the processing of small berries remains a critical bottleneck. In the future, the integration of artificial intelligence for the real-time control and process optimization is identified as a promising pathway toward efficient, intelligent, and sustainable drying systems of small berries.

Drying extends beyond physical dehydration; it is a complex, multiphase process where chemical reactions significantly govern product quality. Evaluating drying from this perspective enables the intentional selection of technologies and conditions to inhibit or promote specific chemical transformations. Building on an analysis of fundamental reaction mechanisms and their corresponding regulatory strategies, this work systematically evaluates how different drying methods influence these reactions and the resulting product attributes. A “material-driven, quality-oriented, and strategy-coupled” framework is proposed as a systematic approach to guide technology selection. The review indicates that multidisciplinary integration is essential to address challenges such as complex reaction systems and imprecise kinetic models. Furthermore, continuous multi-physics optimization and the integration of drying with advanced pre-treatment technologies are identified as key drivers for process enhancement. Achieving precise micro-level regulation remains the ultimate goal of future drying research, which will increasingly rely on the synergy of intelligent sensing systems and digital twins.