Ultrafine grinding (UFG) has emerged as an effective strategy for particle-scale regulation and interfacial activation, and has shown considerable engineering value in food powder processing, release of functional components, and value-added utilization of by-products. During size reduction to the micrometer scale, increases in specific surface area, elevation of surface energy, and microstructural reconstruction often occur simultaneously, thereby markedly affecting dissolution/leaching kinetics, dispersion stability, processing adaptability, and functional performance. However, the links among grinding methods, multi-scale breakage mechanisms, property evolution, and application outcomes remain insufficiently integrated because breakage kinetics, energy dissipation, multiphase flow–particle interactions, and particle agglomeration are highly coupled. In this context, this review focuses primarily on food materials, with traditional Chinese medicinal materials (TCMs) as a supplementary point of comparison, to summarize recent advances in UFG. First, based on the classification of dry and wet routes, various UFG approaches are reviewed with emphasis on their energy-transfer mechanisms, equipment characteristics, material-processing boundaries, and process differences from conventional equipment. Second, from the perspectives of micromechanics, energy consumption, and particle-size evolution, recent advances in fracture and energy theories, multiscale particle mechanics models, CFD–DEM coupling, and population balance modeling are summarized. Furthermore, the effects of UFG on powder morphology, particle-size distribution, flowability, hydration/solubility, and functional properties of food and TCMs are discussed, with particular attention to cell-wall disruption, release of bioactive constituents, structural reconstruction, and over-grinding effects. Finally, recent progress and future directions are reviewed for applications of UFG in enhanced bioactivity, synergistic modification, novel food development, value-added utilization of food by-products, and efficacy enhancement of TCM formulations. This review provides a systematic reference for mechanistic elucidation, model prediction, and engineering application of UFG in food systems.
Controlled-release food packaging (CRP) seeks to maintain antimicrobial, antioxidant, or ripening-control agents within an effective concentration window, thereby overcoming the burst release and rapid activity loss frequently observed in conventional active packaging. Translation remains limited, however, because release behaviour is commonly evaluated in simplified simulants, mechanistic interpretations are often borrowed uncritically from pharmaceutical delivery, and material innovation is insufficiently connected to food-matrix partitioning, package architecture, manufacturability, and food-contact safety. This review critically examines CRP as a coupled functional system governed by release mechanism, carrier material, fabrication architecture, food composition, and storage environment. Diffusion, swelling and polymer relaxation, erosion, partitioning, and stimulus-responsive release are evaluated across biopolymer, biodegradable polyester, nanocomposite, and porous-host platforms. The available evidence indicates that multilayer, reservoir, encapsulation, and core–shell architectures generally provide more reliable control of burst release than increasing active-agent loading alone. Nevertheless, comparison across studies is hindered by inconsistent release media, loading normalization, boundary conditions, kinetic modelling, food validation, and migration assessment. A standardized reporting framework and a packaging-adapted readiness rubric are therefore proposed. Future progress will depend on realistic food-level validation, safety-by-design, scalable manufacturing, regulatory evidence, and demonstrated reduction of food loss under relevant supply-chain conditions.
Rapid urbanization, climate instability, land scarcity, and fragile global supply chains are transforming food security into a systems-level engineering challenge, especially in dense, import-reliant cities. This review explores how engineering-driven strategies can strengthen urban food security through an integrated framework linking sustainable production, resource-efficient processing, nutritional adequacy, and food safety. We examine three key upstream production pathways, namely controlled environment agriculture, precision fermentation, and circular bioeconomy systems, as complementary solutions for enhancing food availability under spatial and resource constraints, with attention to the energy-cost-sustainability trade-offs shaping their deployment in tropical urban systems. Resource-efficient processing, preservation, and process intensification are evaluated for their roles in improving product stability, minimizing post-harvest losses, and enhancing water and energy efficiency. Recognizing that food security extends beyond supply, we emphasize the importance of nutritional quality, techno-functional performance, digestibility, and safety as alternative proteins and novel ingredients become more prevalent. The review also highlights how New Approach Methodologies (NAMs), including advanced in vitro, in silico, and omics-based tools, can modernize food safety governance for increasingly complex food systems. Central to this work is an integrated engineering-nutrition-safety framework, connecting production systems, process optimization, nutritional design, safety-by-design, and traceability to four key food security pillars – availability, utilization, stability, and sustainability. Comparative insights from Singapore, Southeast Asia, and Latin America demonstrate that successful implementation requires region-specific adaptation to local infrastructure, climate, and regulation. Ultimately, this review argues that resilient urban food systems require coordinated, systems-based design where engineering efficiency, nutritional value, safety assessment, and sustainability function as interconnected priorities.
Foam formation remains a major operational challenge in fermentative bioprocesses, affecting process stability, product quality, and industrial scalability. In the last decade, artificial intelligence (AI)–based approaches, including computer vision (CV), machine learning (ML), and deep learning (DL), have been increasingly applied for automated foam monitoring and control. This systematic review and meta-analysis were conducted in accordance with PRISMA 2020 guidelines to critically evaluate AI-driven methods for foam detection, quantification, and control in fermentation systems. Systematic searches of PubMed, Scopus, Web of Science, and IEEE Xplore identified 566 records published between 2015 and 2025, of which 167 studies met the inclusion criteria and 89 were eligible for quantitative synthesis. Random-effects meta-analysis demonstrated that deep learning–based models significantly outperformed classical image processing approaches for foam-related tasks, with pooled coefficients of determination reaching R² = 0.88 (95
3D printing is gradually reshaping the landscape of food processing by offering innovative solutions that promote sustainability and personalised nutrition. This review traces the journey of 3D food printing, beginning with its early development and moving through to current practices and future possibilities. It discusses the main printing techniques used in the field such as inkjet printing, binder jetting, laser sintering, and extrusion. Essential parameters like nozzle height, diameter, speed, printing accuracy, shrinkage, and production rate are explored in the context of various food products. The paper also highlights the importance of digital tools, including CAD modeling and slicing software, for designing printable food structures, as well as advanced features like AI-driven controls, real-time modifications, and multi-ingredient printing. Post-processing methods such as baking, steaming, frying, freezing, and glazing are examined to present a complete overview of the workflow. Furthermore, the study compares conventional food production methods with 3D printing, showcasing improvements in customization, resource efficiency, and creative design. Applications in fields like healthcare, space exploration, defence, gastronomy, and food waste management are also discussed. In closing, the review addresses emerging trends such as new material innovations, smart production systems, and social and regulatory considerations, reinforcing the potential of 3D food printing as a forward-thinking and sustainable approach in modern food technology.
Yoghurt fermentation is a dynamic and multiscale process in which microbial activity, mineral equilibrium, and protein reorganization collectively govern acidification kinetics, gel formation, texture, and product consistency. Conventional monitoring approaches, predominantly based on intermittent pH or titratable acidity measurements, provide limited insight into early biochemical, ionic, and microstructural transitions that critically influence fermentation outcomes. In response to these limitations, a wide range of advanced, non-destructive, and real-time monitoring techniques has emerged in recent years. This review systematically consolidates and evaluates developments across acoustic and mechanical sensing, optical spectroscopy and imaging, electrochemical and dielectric methods, molecular tools, and artificial intelligence–enabled hybrid systems for yoghurt fermentation monitoring and prediction. The comparative analysis links each technique to the specific physicochemical or biological phenomena it interrogates, while assessing practical considerations such as invasiveness, cost, data complexity, and industrial readiness. The review highlights how different methods complement one another in capturing acidification dynamics, casein micelle destabilization, gelation, and microbial performance, and emphasizes the growing role of data fusion and machine learning in enabling predictive and automated process control. By integrating dispersed advances into a single framework, this work addresses the lack of a unified perspective on yoghurt fermentation monitoring and provides a structured foundation for selecting, combining, and deploying sensing technologies in research, pilot, and industrial dairy processing environments. Overall, this review serves as a practical reference to rationally select and integrate advanced sensing and data-driven tools for yoghurt fermentation across laboratory and industrial scales.
The increasing global demand for sustainable food processing practices, coupled with the need to address global energy issues and environmental pollution, has underscored the importance of optimizing drying technologies. While conventional drying methods, such as hot air drying, remain prevalent, they are highly energy-intensive and often compromise product quality. In response, hybrid drying systems (HDS), particularly those combining microwave, heat pump, or dehumidifier and renewable energy sources, have emerged as promising alternatives. However, their broader adoption is constrained by complex interactions between multiple heat and mass transfer mechanisms, which complicate process control, limit scalability, and hinder systematic optimization. This review critically evaluates existing optimization strategies for HDS, highlighting key limitations in current approaches that rely heavily on empirical, univariate, or fragmented methodologies. Relevant studies were identified through a structured literature search using the Scopus database, limited to articles published between 2020 and 2025. The review explores a spectrum of optimization tools, including statistical methods, AI-driven approaches, nature-inspired algorithms, and physics-based models, assessing their applicability, scalability, and integration potential. Emphasis is placed on aligning optimization strategies with specific decision-making parameters such as drying efficiency, energy use, cost, and product quality. The review further proposes a structured framework for selecting hybrid dryer configurations and optimization techniques based on defined objectives. By addressing current research gaps, this work provides insights that support the development of clean, efficient, and resilient food drying systems, with potential relevance to broader sustainability objectives, including the United Nations Sustainable Development Goals (SDGs).
Drying is an energy-intensive and time-consuming operation in food processing and preservation. To address such challenges, ohmic heating (OH) is used as a pretreatment before drying to improve drying efficiency, reduce drying time, and ensure retention of product quality. This comprehensive review explores the mechanistic understanding of OH technology and its application, assisted with other novel techniques prior to the drying process. It also focuses on the impact of OH solely and synergistic approaches on drying efficiency, energy consumption, quality attributes, and product stability. Moreover, technological advancements, safety aspects, challenges, and future research prospects are compiled. The novel pretreatment technology generates internal volumetric heating for efficient drying performance, achieving a high rate of moisture removal. OH is applied as a hurdle pretreatment strategy to enhance the mass transfer, thereby reducing processing time and specific energy consumption. This novel pretreatment technique is frequently used in combination with other non-thermal or thermal approaches for efficient drying performance, as well as the retention of quality attributes, such as color, flavor, and texture, through synergistic and resistance-heating effects. Furthermore, it retained the quality stability of dried food products during storage. Further research is required to explore design aspects, process optimization, material selection, safety concerns, and continuous process development. Overall, OH is a promising approach to address challenges in food drying, and these technological developments can be implemented for sustainable food processing and preservation for scaled-up processes.
Natural Deep Eutectic Solvents (NADES) are emerging as sustainable alternatives to conventional solvents owing to their low toxicity and biodegradability, offering multifunctional benefits for biopolymer-based packaging applications. This study reviews the various roles of NADES as solvents, processing media, plasticizers, and active components in film-forming and coating systems. The findings suggest that NADES effectively modify polymer chain mobility, enhancing mechanical performance and film homogeneity; thus, their use as plasticizers and functional carriers is highly promising. However, several barriers to industrial adoption remain, including limited research on scale-up processes, formulation dependency, high viscosity, and hygroscopic behaviors. Furthermore, the lack of regulatory recognition and insufficient data on the long-term exposure effects continue to hinder commercial implementation. Future efforts must transition from laboratory-scale empirical formulations to predictive, engineering-oriented designs, with priority given to the development of structure-property-function models that link NADES composition directly to polymer performance. Addressing these challenges will facilitate the development of high-performance, eco-friendly packaging materials that align with the principles of a circular economy.
Artificial intelligence (AI) is transforming food processing and preservation, offering significant opportunities to improve efficiency, safety, quality, and sustainability. This systematic review examines the application of AI technologies in this field, with particular emphasis on machine learning, deep learning, computer vision, and robotics. Using a PRISMA-based approach, literature published between 2015 and 2025 was analysed to identify major research trends, leading contributors, and emerging thematic areas. The review shows that AI has been widely applied in quality control and inspection, process optimisation, shelf-life prediction, intelligent packaging, predictive maintenance, and cold-chain monitoring. AI-driven systems have demonstrated strong capability in analysing complex datasets, detecting abnormalities, modelling food processes, improving decision-making, and enhancing food safety across the value chain. Bibliometric and thematic evidence further indicates that the field is rapidly expanding and increasingly interdisciplinary. However, most reported applications remain at laboratory or pilot scale, with limited industrial-scale implementation. Key barriers include data quality, model robustness, real-time integration, and limited cross-process deployment. Ultimately, these challenges underscore the need for ongoing research and development in AI technologies to realise their promise within the food business further. Future research directions encompass the amalgamation of AI with biotechnology, advancing more resilient and interpretable AI models, and formulating ethical standards for the appropriate application of AI in food processing and preservation. Therefore, this article serves as a significant resource for scholars, practitioners, and industry experts seeking to leverage the full capabilities of AI to elevate the food processing and preservation industry.
Cyclone separators (CySep) receive special attention due to the collection of powder matter that benefits different industries by preventing loss of product and pollution, where the food industry represents an outstanding case. A tangential-descendent-ascendent flow pattern characterizes the internal flow that suffers a high swirl behavior, secondary flows, and considerable turbulence effects. These elements affect the powder collection efficiency of CySep and depend on their geometric design. Thus, the research of CySep has pretended to predict the flow patterns in order to enhance the powder collection efficiency for a wide range of particle sizes. This study describes the remarkable experimental and theoretical approaches, such as the application of CFD, to improve the efficiency of CySep and motivate its application in foods.
Scraped surface heat exchangers (SSHEs) are widely recognised for their efficiency in handling viscous, crystallisation-prone, and fouling-sensitive materials. Their distinctive design enables continuous heat transfer while limiting fouling, which is essential for product quality and process efficiency in the food industry. This review primarily focuses on the fundamental flow and heat transfer mechanisms that govern their performance under food processing conditions. Based on these mechanisms, recent improvement strategies are discussed, including geometric optimisation, nanofluid use, and coupling with ultrasound or magnetic fields. Typical food applications such as oil and fat crystallisation, dairy processing, and beverage concentration are summarised through practical examples. Despite their advantages, SSHEs still face challenges such as high energy demand, scraper wear, and fouling under complex conditions. Future developments integrating multiphysics modelling, advanced design strategies, and intelligent monitoring are expected to enhance operational reliability, reduce energy consumption, and support scalable and sustainable food processing using SSHEs.
Edible coatings offer a sustainable alternative to synthetic packaging. However, achieving uniform, material-efficient, and reproducible deposition on complex food geometries remains a major barrier to industrial-scale adoption. This review examines a wide range of coating materials including polysaccharides, proteins, lipids, agro-industrial waste derivatives, and emerging biopolymers from seaweed, microbial, and insect sources and summarizes their key physicochemical properties, molecular interactions, and functional performance. Coating application methods are critically evaluated from both material and process perspectives, spanning traditional dip, spin, brush, and flow coating to advanced layer-by-layer assembly, vacuum impregnation, pressure-based spraying, electrostatic deposition, and next-generation ultrasonic and multi-fluid atomization. The analysis highlights the superior scalability of pressure-based spray coating and the enhanced droplet control and surface conformity offered by electrostatic and hybrid atomization technologies. Persistent challenges include inconsistent coverage on curved or recessed surfaces, lack of real-time monitoring tools, limited predictive models for three-dimensional substrates, and minimal pilot-scale validation. The insights presented provide a clear technical foundation for developing edible coating systems capable of delivering uniform, controlled, and industrially robust performance across diverse food products.
Osmosonication (OS) has been researched immensely as an alternative to conventional osmotic dehydration (OD) by overcoming its demerits in order to improve quality attributes. Strawberries, kiwi, plum, papaya, broccoli, potato, carrot and many other commodities subjected to OS had ameliorated nutritional profile. Microchannel formation augments mass transfer process along with moisture diffusivity thereby decreasing drying time and improving energy efficiency. The relationship between frequency, intensity, power, processing time, osmotic solution and its concentration determines physiochemical, phytochemical, thermal and textural properties of food. This review article aims to deliver critical discussion on impact of OS on various attributes of fruits and vegetables laying emphasis on engineering and structural properties. OS generally lowers the moisture content, enzymatic activity, titratable acidity and respiration rate whereas increases total sugars and enhance colour of dried fruits and vegetables. Fruits and vegetables treated with OS exhibited good bioactive retention. OS as a pre-drying treatment improved structural and engineering properties comprising of water loss (WL) and solid gain (SG), mass diffusivity, weight reduction, dielectric properties, thermal properties, textural properties and energy efficiency. The current study is the first one to propose a conceptual design of pilot/semi-industrial scale osmosonicator along with its processing parameters (power, frequency, agitation, amplitude, time etc.), working design and impact of the input parameters. The proposed osmosonicator attempts to overcome various shortcomings of OS related to scale up, process efficiency, energy savings, nutritional attributes, quality characteristics, and engineering aspects of food product. Factors influencing industrial scalability of osmosonicator are discussed along with its potential for industrial application.
Bigel-based 3D printing is emerging as a transformative strategy in food science. Their tunable rheological properties, capacity for high shape fidelity printing, and stability make bigels a promising class of food bio-inks for many applications. Despite rapid growth, systematic evaluation of the formulation strategies, printability metrics, and application-specific performances remains limited. This review systematically analyses peer-reviewed experimental studies that focus exclusively on edible bigels fabricated through 3D printing. A bibliometric survey highlights the rapid expansion of this field, which is dominated by contributions from China and supported by interdisciplinary inputs from food science, chemistry, and engineering. The literature is organised around formulation levers, including gelator selection, oleogel-to-hydrogel ratio, homogenisation techniques, and functional additives. These parameters are evaluated within a rheology-to-printability framework, with an emphasis on yield stress, shear thinning, and thixotropic recovery as determinants of printability. Application-specific performances are also summarised for fat analogues, dysphagia-oriented structures, bioactive carriers, and intelligent packaging. The review highlights recent advances in modifying bigel structures for 3D printing and points out several gaps, such as inconsistent reporting of formulations, limited knowledge on underlying mechanisms, and the lack of predictive models that connect rheology with printing results. The key research priorities involve creating mechanistic and predictive rheological models to support rational formulation design and standardising formulation and printability metrics for better cross-study comparisons. This review outlines a roadmap to promote bigel 3D printing as an innovative platform for custom, sustainable, and health-oriented food design.
Monitoring fruit quality is vital in modern agriculture and postharvest systems due to its impact on food security, economic sustainability, and consumer health. Traditional destructive sampling and manual inspection are time-consuming and inefficient. Integrating artificial intelligence (AI) with non-destructive sensing technologies offers a rapid, high-throughput alternative for quality assessment. This review explores the application of AI across pre- and postharvest stages using spectroscopic (e.g., NIR, THz), imaging (e.g., hyperspectral, X-ray), and sensor-based systems (e.g., electronic nose). It evaluates traditional machine learning (e.g., SVM, RF), deep learning (e.g., CNNs, GANs), and hybrid models, with a focus on real-time deployment, edge AI, and multimodal data fusion. AI-driven methods have achieved over 95
Liquid nitrogen spray freezing (LNSF) has emerged as a promising technology for cryopreservation in food and biomedical applications, offering ultra-fast cooling rates and excellent heat transfer efficiency that preserve structural integrity. Despite its potential, challenges such as unstable cavitation flow, difficulty controlling atomized particle size, non-uniform heat transfer, and material heterogeneity limit its broader application. This review systematically analyzes the core physical mechanisms of LNSF — cavitation, atomization, heat transfer, and ice crystal dynamics — highlighting key controlling parameters including cavitation number (σ), Weber number (We), Ohnesorge number (Oh), and Reynolds number (Re). The effects of flow properties, nozzle geometry, ambient conditions, and surface characteristics are examined in detail. We also explore digital innovations that enable a shift from rapid to precision freezing, including multimodal sensing, predictive modeling, and adaptive control. Applications across aquatic products, fruits and vegetables, edible fungi, starch-based products, and biological tissues are discussed, emphasizing how cross-scale coupling between flow dynamics and thermophysical behavior influences freezing efficiency, ice morphology, and product quality. Systematic parameter optimization is shown to enhance freezing uniformity and reduce cryo-damage, charting a clear pathway toward next-generation precision LNSF systems.
Protein-based food 3D printing has emerged as a key technology for developing sustainable, personalized, and functional products. This review critically analyzes the scientific evolution of the field between 2014 and 2025 through a bibliometric and functional analysis of 590 articles indexed in Web of Science. Data science tools (keyword co-occurrence, thematic maps, and multiple correspondence analysis) were applied to identify trends, emerging themes, and conceptual relationships. The analysis revealed two main lines: one focused on complex emulsions and the other on vegetable formulations optimized with hydrocolloids. The rheological properties and the type of protein isolate prove to be fundamental axes in the formulation of food inks. It was identified that the type of ingredient (hydrocolloids, lipids, enzymes) and their interaction with proteins directly influence the viscosity, elasticity, extrudability, and shape retention capacity of the inks. Additionally, post-processing methods (baking, microwaving, and enzyme treatment) have a significant impact on the texture, stability, and overall acceptance of the final product. From experimental and literature data, unsupervised classification models (K-means, GMM, hierarchical clustering) based on rheological variables (G′, G″, tan δ) were developed, which allowed the identification of three groups of inks with differential behavior, associated with printing parameters such as nozzle diameter, speed, and filling percentage. Visualization by PCA showed how these properties impact extrusion and structural fidelity. These findings underscore the importance of an integrated understanding of formulation, rheology, printing conditions, and post-processing to optimize the quality and functionality of printed foods, representing a key step towards their industrial implementation.
Arid regions face severe food insecurity due to harsh climatic conditions, limited water resources, and fragile agricultural systems. Neglected and underutilised vegetables (NUVs) cultivated in arid ecosystems are climate-resilient, highly nutritious, and possess immense potential to combat malnutrition and hidden hunger. However, NUVs poses short shelf life, and the absence of suitable post-harvest management and storage systems leads to 20–40
The proactive drying technology called refractance window drying (RWD) has the ability to convert fragmented slices of fruits and vegetables, liquid food, pulp and other biomaterials into edible leathers, flakes, or powders. The aim of this review is to systematically analyze RWD research using quantitative analysis and qualitative analysis to identify trends and research gaps. This systematic review integrates quantitative and qualitative approaches, wherein the quantitative component was conducted using R Studio and VOSviewer software to perform bibliometric mapping and network visualization while the qualitative component was based on an in-depth true case study to provide practical and contextual insights. For the quantitative analysis, the Scopus database was used to retrieve publications from 2014 to 2024, based on the key search term “Refractance Window Drying” appearing in the article titles. Quantitative analysis was conducted on 141 publications sourced from 63 different sources. Finding of the quantitative analysis revealed that the 28.73
