The demand for protein-based liquid foods is increasing due to growing awareness of the impact of diet on human health. This trend has prompted the food industry to explore minimal processing technologies that ensure both safety and clean-label appeal. This review presents a comprehensive assessment of selected innovative nonthermal technologies—based on high pressure, electromagnetic, acoustic, plasma fields, and membrane filtration principles—to process protein-based liquid foods. Key engineering considerations for designing process conditions suitable for protein systems are discussed. The review also examines the effects of these technologies on microbiological safety and quality attributes, including structural (particle size and microstructure), functional (solubility, rheology, emulsification, and foaming properties), and nutritional aspects (digestibility and allergenicity), along with possible underlying mechanisms. Findings highlight the importance of uniform application of the lethal agent (e.g., pressure, temperature, and electrical field) and thermal effects within the processed volume to validate microbial safety. Product-specific factors such as composition including fat and protein, pH, and water activity must also be carefully considered. Evidence suggests that nonthermal technologies can induce diverse structural and conformational changes in proteins, thereby altering their interactions with other food components and leading to variable impacts on quality attributes such as viscosity and emulsion stability. Increasing thermal intensity in combination with nonthermal agents generally degrade product quality. Future research should aim to optimize nonthermal processing parameters for a variety of protein-based foods by integrating both process and product factors to ensure microbial safety and enhanced product quality. The strategic application of nonthermal technologies—alone or in combination with mild thermal treatments—offers significant potential for developing sustainable, high-quality, and tailor-made protein-based food products.

Sugar kelp (Saccharina latissima) cultivation is rapidly expanding globally, creating an abundant and geographically diverse feedstock whose processing has not kept up with cultivation. This review addresses that gap with a carbohydrate-centric scope around alginate, fucoidan, laminarin, and cellulose, integrating the latest literature with practice-oriented assessments of green biorefinery routes, including ultrasound, microwave, super- and sub-critical fluids, and biocatalysis. In addition, we add comparative insights from our own laboratory work on selectivity, polymer integrity, contaminant control, and process robustness. Unlike broad algal overviews, this work establishes a process–structure–function framework that directly links extraction conditions to molecular attributes such as molecular weight, sulfate content, and M/G ratio, and their subsequent functional performance in food systems. We include a novel overview of biocatalysis in seaweed processing, moving beyond enzymatic hydrolysis to cover underexplored whole-cell fermentation for upgrading biomass toward synbiotic products. Finally, these technical insights are mapped to emerging food applications, including gut-promoting ingredients, encapsulation systems, edible films and coatings, fat and meat analogs, and clean-label ingredients. The result is a sugar-kelp-focused, implementation-minded guide for the sugar kelp industry around the globe to build scalable, food-grade biorefineries for high-value polysaccharide ingredients.

Advances in pathogen detection that incorporate artificial intelligence (AI) may capture microbial signals under challenging environmental conditions that traditional methods miss. This systematic review evaluates the application, performance, and methodological characteristics of AI-enabled imaging for pathogen detection, including its impact on speed, accuracy, and modeling under stress conditions. Studies were systematically identified from five electronic databases using search terms related to AI, pathogen, detection, and imaging. Inclusion criteria, defined using the Population, Intervention, Comparators, Outcome, Study design (PICOS) framework, focused on microscopy-based pathogen detection enhanced by AI. Data extraction followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and captured biological sample preparation, imaging modalities, AI-enabled data analyses, comparator methods, and performance metrics. Of 2736 citations retrieved, 120 were reviewed in full and 28 studies met the inclusion criteria. These represented more than 40 pathogens, most commonly Salmonella spp. and Escherichia coli. Only three studies explicitly evaluated signals from stress or inactivated states. Comparator methods (e.g., culture-based or molecular assays) were infrequently reported, limiting benchmarking against established workflows. Reporting inconsistencies in laboratory protocols and computational pipeline further complicated reproducibility and precluded meta-analysis. Overall, this review offers a comprehensive overview of current AI-enabled imaging approaches from both biological and computational perspectives and highlights the need for standardized benchmarks and reporting practices to support reproducible, transferable pathogen detection.

Crustaceans serve as a crucial source of high-quality protein, but their muscle quality and flavor are highly sensitive to environmental stress during aquaculture, processing, and transportation. Most existing studies have primarily focused on stress responses in gills, hepatopancreas, and intestines. In recent years, increasing attention has been directed toward stress-induced deterioration in crustacean muscle. The physiological regulation of crustaceans under stress involves complex processes such as signal transduction, gene expression, and energy metabolism, which are closely associated with muscle characteristics and form the mechanistic basis for subsequent quality and alterations. This review then systematically discusses the effects of major stressors, including temperature, salinity, hypoxia, ammonia, and dissolved oxygen, on muscle nutrition, texture, flavor, and structural integrity. In addition, chemical or nutritional stress, such as exposure to metals, pesticides, microplastics, and nutritional imbalance, can lead to pathological changes in muscle tissue, which can lead to irreversible damage and may reduce consumer experience and acceptance to a certain extent. The review further highlights recent progress in understanding how the interplay of energy metabolism disorders, oxidative imbalance, and gene regulation mediates these quality. Finally, strategies for mitigating stress and improving muscle quality, including environmental optimization, nutritional regulation, and postharvest management, are evaluated. Emerging biotechnologies offer new avenues for mitigating stress and improving muscle quality, yet their application in crustacean aquaculture demands continued research. Despite these advances, quantifying the interactive effects among multiple stressors and developing sustainable stress management systems remain major challenges for ensuring high-quality crustacean products.

Artificial intelligence (AI) has been increasingly applied to address challenges in food packaging, including food waste, sustainability, and real-time quality assurance. However, existing studies are often confined to specific applications, with limited integration across different stages of the packaging life cycle and insufficient linkage between material performance, functionality, and system-level outcomes. This review systematically analyzes peer-reviewed studies retrieved from the Web of Science Core Collection (2021–2025), selected based on their relevance to AI applications in food packaging, including material performance, safety, and life cycle management. A life cycle–oriented framework is proposed, linking major AI paradigms (supervised, unsupervised, reinforcement, deep learning, and hybrid models) to six key domains: material design, production optimization, food quality prediction, safety assurance, smart labeling and traceability, and recycling. Within this framework, AI supports data-driven prediction, monitoring, and decision-making, whereas hybrid models improve robustness in complex, multifactor systems. Despite challenges related to data quality, model generalization, and regulatory acceptance, AI-driven packaging systems may support a transition from passive containment toward more adaptive and data-informed solutions that improve efficiency, sustainability, and consumer trust.

The demand for protein-based liquid foods is increasing due to growing awareness of the impact of diet on human health. This trend has prompted the food industry to explore minimal processing technologies that ensure both safety and clean-label appeal. This review presents a comprehensive assessment of selected innovative nonthermal technologies—based on high pressure, electromagnetic, acoustic, plasma fields, and membrane filtration principles—to process protein-based liquid foods. Key engineering considerations for designing process conditions suitable for protein systems are discussed. The review also examines the effects of these technologies on microbiological safety and quality attributes, including structural (particle size and microstructure), functional (solubility, rheology, emulsification, and foaming properties), and nutritional aspects (digestibility and allergenicity), along with possible underlying mechanisms. Findings highlight the importance of uniform application of the lethal agent (e.g., pressure, temperature, and electrical field) and thermal effects within the processed volume to validate microbial safety. Product-specific factors such as composition including fat and protein, pH, and water activity must also be carefully considered. Evidence suggests that nonthermal technologies can induce diverse structural and conformational changes in proteins, thereby altering their interactions with other food components and leading to variable impacts on quality attributes such as viscosity and emulsion stability. Increasing thermal intensity in combination with nonthermal agents generally degrade product quality. Future research should aim to optimize nonthermal processing parameters for a variety of protein-based foods by integrating both process and product factors to ensure microbial safety and enhanced product quality. The strategic application of nonthermal technologies—alone or in combination with mild thermal treatments—offers significant potential for developing sustainable, high-quality, and tailor-made protein-based food products.

Sugar kelp (Saccharina latissima) cultivation is rapidly expanding globally, creating an abundant and geographically diverse feedstock whose processing has not kept up with cultivation. This review addresses that gap with a carbohydrate-centric scope around alginate, fucoidan, laminarin, and cellulose, integrating the latest literature with practice-oriented assessments of green biorefinery routes, including ultrasound, microwave, super- and sub-critical fluids, and biocatalysis. In addition, we add comparative insights from our own laboratory work on selectivity, polymer integrity, contaminant control, and process robustness. Unlike broad algal overviews, this work establishes a process–structure–function framework that directly links extraction conditions to molecular attributes such as molecular weight, sulfate content, and M/G ratio, and their subsequent functional performance in food systems. We include a novel overview of biocatalysis in seaweed processing, moving beyond enzymatic hydrolysis to cover underexplored whole-cell fermentation for upgrading biomass toward synbiotic products. Finally, these technical insights are mapped to emerging food applications, including gut-promoting ingredients, encapsulation systems, edible films and coatings, fat and meat analogs, and clean-label ingredients. The result is a sugar-kelp-focused, implementation-minded guide for the sugar kelp industry around the globe to build scalable, food-grade biorefineries for high-value polysaccharide ingredients.

Advances in pathogen detection that incorporate artificial intelligence (AI) may capture microbial signals under challenging environmental conditions that traditional methods miss. This systematic review evaluates the application, performance, and methodological characteristics of AI-enabled imaging for pathogen detection, including its impact on speed, accuracy, and modeling under stress conditions. Studies were systematically identified from five electronic databases using search terms related to AI, pathogen, detection, and imaging. Inclusion criteria, defined using the Population, Intervention, Comparators, Outcome, Study design (PICOS) framework, focused on microscopy-based pathogen detection enhanced by AI. Data extraction followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and captured biological sample preparation, imaging modalities, AI-enabled data analyses, comparator methods, and performance metrics. Of 2736 citations retrieved, 120 were reviewed in full and 28 studies met the inclusion criteria. These represented more than 40 pathogens, most commonly Salmonella spp. and Escherichia coli. Only three studies explicitly evaluated signals from stress or inactivated states. Comparator methods (e.g., culture-based or molecular assays) were infrequently reported, limiting benchmarking against established workflows. Reporting inconsistencies in laboratory protocols and computational pipeline further complicated reproducibility and precluded meta-analysis. Overall, this review offers a comprehensive overview of current AI-enabled imaging approaches from both biological and computational perspectives and highlights the need for standardized benchmarks and reporting practices to support reproducible, transferable pathogen detection.

Crustaceans serve as a crucial source of high-quality protein, but their muscle quality and flavor are highly sensitive to environmental stress during aquaculture, processing, and transportation. Most existing studies have primarily focused on stress responses in gills, hepatopancreas, and intestines. In recent years, increasing attention has been directed toward stress-induced deterioration in crustacean muscle. The physiological regulation of crustaceans under stress involves complex processes such as signal transduction, gene expression, and energy metabolism, which are closely associated with muscle characteristics and form the mechanistic basis for subsequent quality and alterations. This review then systematically discusses the effects of major stressors, including temperature, salinity, hypoxia, ammonia, and dissolved oxygen, on muscle nutrition, texture, flavor, and structural integrity. In addition, chemical or nutritional stress, such as exposure to metals, pesticides, microplastics, and nutritional imbalance, can lead to pathological changes in muscle tissue, which can lead to irreversible damage and may reduce consumer experience and acceptance to a certain extent. The review further highlights recent progress in understanding how the interplay of energy metabolism disorders, oxidative imbalance, and gene regulation mediates these quality. Finally, strategies for mitigating stress and improving muscle quality, including environmental optimization, nutritional regulation, and postharvest management, are evaluated. Emerging biotechnologies offer new avenues for mitigating stress and improving muscle quality, yet their application in crustacean aquaculture demands continued research. Despite these advances, quantifying the interactive effects among multiple stressors and developing sustainable stress management systems remain major challenges for ensuring high-quality crustacean products.

Artificial intelligence (AI) has been increasingly applied to address challenges in food packaging, including food waste, sustainability, and real-time quality assurance. However, existing studies are often confined to specific applications, with limited integration across different stages of the packaging life cycle and insufficient linkage between material performance, functionality, and system-level outcomes. This review systematically analyzes peer-reviewed studies retrieved from the Web of Science Core Collection (2021–2025), selected based on their relevance to AI applications in food packaging, including material performance, safety, and life cycle management. A life cycle–oriented framework is proposed, linking major AI paradigms (supervised, unsupervised, reinforcement, deep learning, and hybrid models) to six key domains: material design, production optimization, food quality prediction, safety assurance, smart labeling and traceability, and recycling. Within this framework, AI supports data-driven prediction, monitoring, and decision-making, whereas hybrid models improve robustness in complex, multifactor systems. Despite challenges related to data quality, model generalization, and regulatory acceptance, AI-driven packaging systems may support a transition from passive containment toward more adaptive and data-informed solutions that improve efficiency, sustainability, and consumer trust.