ABSTRACT Despite transformative analytical capabilities, foodomics technologies remain confined to research laboratories, with traditional methods dominating 87% of routine food testing. Critical evaluation of 127 studies (2020–2024) quantifies the persistent adoption gap. Published accuracies (mean 93.4%, 95% CI: 91.8–95.0%) systematically overestimate real-world performance, declining under independent validation (mean 81.2%, 95% CI: 78.5–83.9%) due to inadequate training sets, matrix effects, and statistical overfitting. Comprehensive economic analysis reveals true implementation costs ($400–800/sample for multi-omics) exceed reported expenses by 72% (95% CI: 68–76%), creating substantial economic barriers. Multi-omics integration provides marginal accuracy gains (mean 4.2 percentage points, 95% CI: 3.1–5.3) at 245% cost increases (95% CI: 205–285%), justifying deployment only when single-omics fails. Critical barriers include validation deficits (6% achieve multi-laboratory validation), insufficient reference materials (<30% of applications), and systematic underreporting of failures. Analysis of 75 documented failures reveals primary mechanisms: inadequate training sets (43%), matrix effects (28%), statistical overfitting (18%), insufficient sensitivity (15%), and uncharacterized confounding (12%). Strategic investments in processing-stable markers ($5–10M), reference material libraries ($20–40M), and cost-reduction technologies ($10–15M) could enable 60–75% expense reduction, expanding viable applications threefold by 2030.

ABSTRACT Vegetables are rich sources of bioactive compounds with emerging significance in neuroprotection and the management of central nervous system (CNS) disorders. This review compiles ethnomedicinal knowledge and contemporary evidence on commonly consumed vegetables with neuroprotective potential, including Mucuna pruriens, Allium sativum, Brassica oleracea, Solanum lycopersicum, and Lactuca sativa. These vegetables are traditionally used for conditions such as anxiety, epilepsy, Parkinson’s disease, and cognitive decline. Mechanistically, their effects involve antioxidant activity, anti-neuroinflammatory actions, modulation of neurotransmitter systems (e.g. dopaminergic and GABAergic pathways), acetylcholinesterase inhibition, and attenuation of oxidative stress – induced neuronal damage. Despite promising findings, limitations include the scarcity of well-designed clinical trials, variability in phytochemical composition, and challenges related to bioavailability and effective dietary dosing. Additionally, heterogeneity in study designs and lack of standardization hinder clinical translation. Future research should focus on rigorous clinical validation, dose – response relationships, and strategies to enhance bioavailability, alongside integrating these vegetables into evidence-based dietary frameworks. These findings further highlight the relevance of vegetable-based dietary diversification in global public health strategies targeting cognitive aging and neurodegenerative disease prevention

ABSTRACT The adulteration of edible oils is a major global issue that impacts food safety and public health, particularly in premium oils such as groundnut, sesame, coconut, sunflower, and olive oil. Traditional detection techniques, such as chromatography and spectroscopy, offer high precision but are often lengthy and destructive. Recent developments in Artificial Intelligence (AI) have facilitated quick, non-destructive, and scalable methods for detecting adulteration. This review explores the shift from conventional techniques to AI-based approaches, which include machine learning, computer vision, and the analysis of spectroscopic data (like Near-Infrared and hyperspectral imaging). It underscores the importance of AI in evaluating sensory and physicochemical properties, such as aroma, density, and color, to enhance accuracy in detection. Additionally, the review assesses the effectiveness of various AI models, outlines the health risks associated with adulterants, and points out key challenges related to data quality, model generalization, and real-world application. Ultimately, it suggests future research avenues to develop robust, integrated, and deployable systems that ensure the authenticity and safety of edible oils.

ABSTRACT Formula foods for special medical purposes (FSMPs) are specialized nutritional foods specially processed and configured to meet the special needs of people with specific disease states for nutrients or diets. Food-medicine homogeneity (FMH) is derived from the theory of Herbal Medicine, emphasizing substances that have both nutritional and therapeutic effects. Although the research on FMH is increasing, the integration of FMH into FSMPs for disease management has not been fully explored. This review synthesizes the evolutionary trajectory of FMH substances, integrating relevant literature published from 2020 to 2025. The focus is on revolutionary new processing technologies to enhance bioavailability, stability, and personalization. This review is pioneering in its expansion of FSMP raw materials to FMH, emphasizing its dual nutritional and therapeutic attributes. FMH employs multi-target synergy to transition FSMPs to multi-pathway mechanisms, thereby ensuring superior safety and efficacy in comparison to traditional therapies. Technological advances in delivery and extraction address application challenges, yet global regulations and personalization remain significant obstacles. FMH-FSMP integration has been shown to converge food and medicine, thereby transcending the concept of “food as medicine” within culturally resonant systems. This integration has the potential to unlock personalized disease management via innovation, trials, and reforms.

ABSTRACT Three-dimensional (3D) food printing enables customized food production, but it faces challenges in creating emulsion-based 3D printed foods with optimal printability, stability, and structural integrity. Xanthan gum (XG) is widely used to address these issues due to its high viscosity at low concentrations, pronounced shear-thinning behavior, and stability under varying pH and temperature. This review critically examines the role of XG in emulsion-based 3D food printing, evaluating its performance in protein-stabilized emulsions, Pickering emulsions, bigels, and starch-based emulsions. A comparison with other natural gums (e.g. guar gum, locust bean gum) highlights the strengths and limitations of XG in printing precision, shape retention, nutrient release, and thermal stability. While XG enhances key printing parameters (viscosity, shear-thinning, and shape fidelity), its effectiveness depends on concentration, pH, and biopolymer interactions. Although it improves stability and printability, high XG levels may reduce nutrient bioaccessibility and alter sensory properties. The review also explores commercial prospects in personalized and dysphagia-friendly nutrition, formulation challenges, and future directions such as synergistic gum blends and advanced processing methods. With ongoing advancements, XG is positioned to play a pivotal role in the future of tailored and sustainable food manufacturing, supporting innovative applications in personalized nutrition, texture-modified foods, and culinary design.

ABSTRACT Peanut (Arachis hypogaea L.) is globally recognized for its nutritional and economic importance; however, substantial fractions, including the skin and hull, remain underutilized, generating significant agro-industrial residues. This comprehensive review critically synthesizes scientific literature, emphasizing studies published between 2020 and 2025 that address the chemical composition, bioactive properties, safety aspects, and sustainable applications of the main peanut fractions: seed, skin, and hull. Results indicate that the skin exhibits the highest phenolic content and antioxidant activity, supporting its potential use as a natural preservative, functional ingredient, and component of biodegradable and active packaging systems. The hull, rich in lignocellulosic compounds, presents promising opportunities for bioadsorbents, bioplastics, composite materials, and renewable energy generation. The seed, beyond its established nutritional value, demonstrates potential for protein isolates, oil optimization, and nutraceutical development. Integrating these fractions into circular economy strategies can enhance resource efficiency, reduce environmental impacts, and promote sustainable production models across food, cosmetic, and environmental sectors. Nevertheless, methodological heterogeneity, limited toxicological validation, and regulatory uncertainties constrain broader industrial adoption. Overall, this review provides an integrated, critical comparison of peanut fractions and highlights the need for standardized extraction protocols, robust safety assessments, life-cycle evaluations, and techno-economic analyses to enable scalable, regulatory-compliant valorization strategies worldwide.

ABSTRACT The escalating global population and pressing environmental urgent have intensified the need for sustainable alternatives to traditional animal-based proteins. This review provides a comprehensive examination of diverse alternative protein sources and their applications in the meat industry. A systematic evaluation is conducted on plant proteins (e.g. from legumes and oilseeds), proteins from animal by-products (e.g. keratin and insects), and microbial proteins (e.g. from yeasts, mushrooms, mycelia, and algae), with a focus on their nutritional profile, functional properties, and specific applications in developing meat analogues or enhancing traditional meat products. The pivotal role of these alternative proteins in fostering a more sustainable, secure, and health-oriented food system is underscored. Additionally, current challenges are addressed, future research directions are outlined, and strategic recommendations to expedite the adoption of alternative proteins in the global food industry are provided. This review serves as a critical resource for researchers, industry stakeholders, and policymakers aiming to navigate the transition toward sustainable protein systems.

ABSTRACT The growing demand for minimally processed and high-quality food is driving innovation in sterilization technologies. While traditional pasteurization and physical field sterilization methods can effectively inactivate microorganisms, they often compromise food nutrition and flavor. Artificial intelligence (AI) offers a transformative approach to overcoming these limitations. This review summarizes the important research contents of scholars on physical field sterilization in the past decade (2015–2025). It presents a critical analysis of the strengths and limitations of common physical field sterilization technologies, alongside the pressing need for their integration with AI. The distinct advantages of machine learning, deep learning, and computer vision in modeling and predicting outcomes for sterilization applications are detailed. AI has been shown to enhance the safety, efficiency, and sustainability of sterilization processes. Finally, future challenges and application prospects are identified.

ABSTRACT The aging global population is increasingly associated with a rise in age-related diseases, leading to heightened healthcare costs and a diminished quality of life for older adults, which poses significant challenges for families and society. In light of these issues, this study investigates the potential of Pyrroloquinoline quinone (PQQ), a novel food supplement recognized for its strong antioxidant properties and regulatory effects on mitochondrial function, as a therapeutic agent for age-related disorders. Recent research highlights PQQ’s anti-aging effects observed in various tissues, including skin, bones, ovaries, and the brain, through research conducted in model organisms and progressing to human subjects. Importantly, PQQ has demonstrated an established safety profile, showing no toxicity or genotoxicity, thus qualifying it as a viable nutritional supplement. Nevertheless, the precise mechanisms underlying PQQ’s anti-aging effects remain to be clarified. This review aims to elucidate these mechanisms while considering tissue-specific aging characteristics and underscores the translational potential of PQQ as a natural therapeutic agent against age-related diseases. Future research should prioritize developing application strategies for PQQ while addressing existing challenges in the aging research domain, to fully exploit its capacity to promote healthy aging and enhance the quality of life for older individuals.

ABSTRACT As food supply chains grow increasingly complex and consumer demands evolve, AI and ML have emerged as vital technologies for enhancing food safety, quality, traceability, and nutrition. Despite their potential, practical applications require further exploration to unlock their benefits fully. This review provides a modern analysis of the advancements of AI and ML in food safety and quality control, focusing on areas such as microbiological detection, contamination prediction, traceability, and real-time monitoring across the food supply chain. The review examines how AI and ML can enhance food safety regulations by leveraging predictive modelling, risk assessment, and decision-support systems to identify and mitigate hazards proactively. It also demonstrates how AI and ML may enhance nutritional assessment and individualized dietary interventions using predictive modelling, nutrient profiling, and clinical decision support. Integrating AI and ML with food safety and nutrition methodologies enhances conventional risk management strategies by identifying issues, forecasting potential problems, and improving monitoring efficiency. These advances enhance food quality, reduce waste, and foster customer trust. Challenges remain, including insufficient data, the need for model interpretability, and concerns over regulatory compliance. However, AI and ML are constantly improving, offering innovative methods to enhance the intelligence and resilience of food safety systems.