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High-frequency ohmic heating (HFOH) represents a significant advancement over conventional ohmic heating (OH) by reducing limitations related to electrode–food interactions. Although conventional OH provides rapid and volumetric heating, its performance is often limited by electrode fouling and corrosion. A key finding of the present review is that the existing literature lacks a unified frequency-based classification of OH, making it difficult to compare processing conditions, interpret frequency-dependent mechanisms, and identify appropriate operating regimes for different food applications. To address this gap, this review proposes a novel functional classification based on the dominant electrical behavior, electrochemical phenomena, and processing characteristics observed across different frequency ranges. Accordingly, this review classifies OH systems into four functional frequency ranges: low-frequency range (<1 kHz); medium-frequency range (1–30 kHz–<1 MHz). HFOH operates within these upper ranges to minimize electrochemical reactions at the electrode interface, resulting in faster and more homogeneous temperature distributions. These advantages lead to lower cooking losses and improved retention of nutritional, sensory, and structural attributes, particularly in heterogeneous and multiphase food systems where conventional methods often fail. However, these effects are matrix-dependent, and low-frequency OH can sometimes achieve stronger microbial inactivation via electroporation. Recent research highlights the importance of computational modeling and simulation for process optimization, as these tools enable accurate prediction of thermal profiles and help reduce temperature irregularities. This review analyzes published studies on HFOH and compares its performance with other OH systems. Applications of HFOH across various food processes are evaluated, including microbial and enzyme inactivation, cooking, heating extraction, thawing, tempering and drying/dehydration. Furthermore, this study discusses the benefits and challenges of HFOH and provides strategic recommendations to address these technical limitations, offering insights that may support its future practical implementation and scalability within the modern food processing industry.

Despite the growing relevance of Agriculture 4.0 technologies for enhancing productivity, decision-making, and sustainability in agri-food systems, their adoption remains uneven in developing-country contexts. This study aims to analyze the perceived severity and co-occurrence structure of barriers to Agriculture 4.0 adoption in the agri-food system of Rio Grande do Sul (RS), Brazil, using an exploratory quantitative design grounded in a barrier co-occurrence perspective rather than a causal or actor-centered network interpretation. An online survey conducted in 2024 with farmers in RS evaluated 25 literature-validated barriers spanning technological, economic, political, social, and environmental dimensions. The analysis combined a Barrier Severity Index (BSI), reliability testing, Principal Component Analysis (PCA), K-means clustering, ANOVA by farm size, and proximity-based co-occurrence networks constructed from highly rated barriers. The results show that economic barriers remain the most severe overall, particularly the lack of affordable solutions, high maintenance costs, and limited infrastructure. At the same time, farm-size-stratified networks reveal distinct association structures: small farms display a more segmented pattern linking affordability and technical access to institutional and capability constraints; medium farms show the most globally integrated co-occurrence structure; and large farms exhibit a dense but more differentiated configuration combining cost, interoperability, skills, and governance-related barriers. These findings are interpreted descriptively, as the networks capture patterns of co-reporting rather than causal interdependence. The study contributes a network-analytic representation of perceived barrier configurations and highlights the need for scale-sensitive policy mixes that address bundles of constraints rather than isolated obstacles.

This chapter describes the mechanistic approach used to translate nutrigenomic data into clinically meaningful nutritional formulations through artificial intelligence (AI) and precision fermentation (PF). This highlights the central challenge of personalized nutrition: converting complex genomic and metabolic signals into consistent, actionable nutrient specifications. This chapter introduces an AI-driven inverse-design system that generates digital nutrient specifications (DNSs) that define the molecular composition and production parameters. PF is a biomanufacturing platform capable of executing these specifications through engineered microbial systems and real-time process control to produce genotype-aligned peptides, vitamins, and metabolic cofactors. The chapter also outlines the analytical pipeline linking multiomic interpretation to fermentation optimization and provides examples relevant to metabolic and neurodevelopmental applications, concluding with key regulatory and ethical considerations for implementing AI-based personalized nutrition.

This study investigates the predictive modeling of total Polycyclic Aromatic Hydrocarbon (PAH) concentrations in smoked fish products based on various smoking parameters using machine learning techniques in the Waikato Environment for Knowledge Analysis (WEKA) software environment. Key input variables included fish fat content, smoking temperature, and wood type, all of which were statistically significant predictors of PAH levels (p < 0.05). A multiple linear regression analysis conducted in SPSS revealed a strong correlation between predictors and PAH concentration (r = 0.801), with an explained variance of 64.1% (R² = 0.641) and a standard error of 3.52. Among the evaluated machine learning algorithms—Linear Regression, SMOreg, Multilayer Perceptron, M5P, Random Forest, and IBk—performance was assessed using five criteria: Correlation Coefficient, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), and Root Relative Squared Error (RRSE). All models were validated using 10-fold cross-validation. For classification tasks based on fish species, Logistic Regression outperformed the Random Forest and J48 algorithms, indicating superior predictive capability. This integrated analytical framework demonstrates the effectiveness of machine learning in food safety monitoring and provides a scientific basis for optimizing smoking processes to mitigate PAH contamination. Overall, the findings underscore the practical value of machine learning tools in the predictive modeling of PAH contamination in smoked fish. The approach not only offers high predictive accuracy but also serves as a scientific framework for improving food safety by optimizing smoking conditions to minimize PAH formation. This integrated model can aid food technologists and manufacturers in establishing safer processing parameters while maintaining product quality.

The increasing demand for transparency, safety, and sustainability in the food industry necessitates the adoption of advanced digital technologies. Thus, this study aimed to examine the synergistic integration of the blockchain, artificial intelligence (AI), and the Internet of Things (IoT) as a transformative framework to enhance hygiene, safety, and operational efficiency throughout food supply chains. Hence, by ensuring end-to-end traceability, these technologies collectively mitigate food fraud, improve regulatory compliance, and foster environmentally responsible practices in fisheries and aquaculture. The paper highlights the capacity of the IoT for real-time data acquisition, the immutable record-keeping of the blockchain, and AI-driven predictive analytics in decision support. Furthermore, this study evaluates various mathematical and analytical models, including Mixed-Integer Linear Programming (MILP), Branch and Efficiency (B&E), Simultaneous Data Envelopment Analysis (SDEA), Karush–Kuhn–Tucker (KKT) optimization, and Fuzzy DEMATEL, as practical tools for designing IoT-integrated blockchain systems. By addressing adoption barriers and future opportunities, this study highlights the critical role of regulatory alignment, cross-sector collaboration, and standardization in fostering a safer, more transparent, and sustainable food industry.

The transformation of alternative protein raw materials into processed food products represents a critical advancement in addressing global food security, environmental sustainability, and economic efficiency. Compared to traditional livestock systems, alternative proteins—derived from microalgae, insects, microbial biomass, and plant-based sources—offer high yields with minimal resource input and reduced ecological impact. These raw materials can be cultivated under controlled conditions, allowing for consistent, scalable production independent of seasonal or climatic constraints. Modern food processing technologies, including enzymatic hydrolysis, extrusion, fermentation, and spray-drying, enable the effective extraction, enhancement, and stabilization of protein content, improving functionality and shelf life. Economically, the use of agricultural waste streams and low-input biomass sources significantly lowers production costs, promoting resource-efficient circular systems. Furthermore, incorporating these proteins into consumer-ready products such as meat analogs, fortified beverages, and protein-enriched foods improves nutritional accessibility while meeting evolving consumer preferences. Environmentally, this transition supports reductions in greenhousegas emissions, land degradation, and water usage. Overally, using sustainable ingredients in processed foods helps create a food system that's resilient, nutritious, and economically viable for our growing global population.

Background of Study: Innovative food solutions are essential for low- and middle-income countries facing food insecurity, malnutrition, and environmental degradation. The high consumption of instant noodles in Indonesia, especially among low-income and nutritionally vulnerable populations, presents a strategic opportunity to enhance nutritional quality by utilizing underutilized fish resources and seafood by-products. Aims and Scope of Paper: This study aims to evaluate the development potential of fish-enriched instant noodles within the framework of circular economy and sustainability. It covers the nutritional enhancement, food waste reduction, and environmental impacts of such innovations, particularly focusing on the feasibility of industrial-scale implementation in the Indonesian context. Methods: A multidisciplinary approach was employed, combining nutritional science, food technology, and sustainability principles. The production process involved the selection of fish-based ingredients and by-products, dough formulation, processing through steaming and energy-efficient drying, and the use of biodegradable packaging. The innovation was assessed in terms of nutritional improvement, environmental impact, and industrial scalability. Result: The results demonstrated that fish-enriched instant noodles significantly improve protein content, omega-3 fatty acids, calcium, and micronutrients compared to conventional instant noodles. Moreover, the adoption of renewable energy, water recycling systems, and eco-friendly packaging effectively reduced the carbon footprint and waste associated with production. This innovation aligns with several United Nations Sustainable Development Goals (SDGs), including Goal 2 (Zero Hunger), Goal 3 (Good Health and Well-being), Goal 12 (Responsible Consumption and Production), and Goal 14 (Life Below Water). Conclusion: The development of fish-enriched instant noodles offers a functional food solution that not only enhances community nutrition but also reduces food waste and supports sustainable food systems. Despite its promising potential, challenges remain, including limited regulatory frameworks, technological readiness, and low consumer awareness of the benefits of circular economy-based foods. Cross-sector collaboration is essential to achieve broader implementation of this innovation on an industrial scale.

Emulsions stabilized by solid particles, known as Pickering emulsions, offer a compelling alternative to conventional surfactant-based emulsions, attracting considerable interest within the food sector. This review comprehensively examines the application of solid particles in stabilizing food emulsions, offering a detailed comparison with traditional surfactant-based methods. Additionally, this paper elucidates how Pickering emulsions, stabilized by particles at the oil-water interface, achieve superior stability, preventing coalescence and phase separation, which is crucial for extending the shelf life of food products. It further explores the enhanced functionality these emulsions provide, including improved nutrient delivery through encapsulation, where sensitive compounds are protected and released in a controlled manner. Moreover, the review delves into innovative applications such as the development of low-fat food alternatives, where Pickering emulsions maintain sensory qualities while reducing lipid content, and their role in enabling advanced food technologies like 3D printing, allowing for tailored food structures and nutritional profiles. By elucidating the mechanisms of particle stabilization and exploring diverse applications, this paper underscores the significant potential of Pickering emulsion technology in transforming food processing and product development.

For human consumption, mussels are known as an abundant source of nutrients. As for public health, mussels must be cultivated or harvested in clean regions. Because they obtain their food by filtering water, mussels are an excellent indicator of water contamination. Owing to this, they might include a range of chemical and microbiological contaminants that can be found in contaminated waters, including diclofenac, organochlorine pesticides, dioxins, furans, heavy metals, microplastics, nanomaterials, nanoparticles, persistent organic pollutants, polychlorinated biphenyls, perfluoroalkyl substances, polycyclic aromatic hydrocarbons, and organochlorine compounds. Mussels should also be processed under appropriate conditions after being harvested from clean waters, and their contamination should be prevented. It is thought that it is necessary to carry out controls continuously at every stage, from harvest to consumption, for consumers to reach healthy and reliable mussels. In addition, as this study reported, it is envisaged that information studies on the chemical and microbiological contaminants of mussels and their safety will be important to prevent the formation of disease risks.

Numerous by-products from the wastes of marine items can be obtained and utilized in a wide range of sectors. The by-products obtained from cephalopod wastes are evaluated and used in various industries (such as food, medicine, and agriculture) by evaluating the wastes of the head, skin, intestines, ink, etc., much like all other wastes derived from marine goods. Because these by-products are rich in nutrients and contain biological substances, they are anticipated to be improved in several industries. Additionally, it should be stated that research to produce financial gains will be funded and that the by-products of these marine things will be acquired. In particular, this study investigates the synthesis of antioxidant and antibacterial agents, the extraction of bioactive components and their use as food additives, the synthesis of gelatin and peptides from cephalopod by-products, the impact of cephalopod by-products on extending shelf life, and the health risks and benefits of cephalopod by-products.