Search Results (15,032)

The utilization of insects as a source of essential nutrients holds considerable promise, with the potential to serve as both feed and food. Consequently, there is a necessity to develop control systems, as the undeclared addition of insects to food products and/or non-compliance with labelling regulations may pose health risks and result in financial losses for consumers. This review describes methods for identifying and detecting insect species by targeting biomolecules such as DNA, proteins, saccharides, and metabolites, with a particular focus on DNA-based approaches. This review provides a detailed overview of the application of polymerase chain reaction (PCR) and DNA sequencing methods that are suitable for the analysis of edible and forage insects. The main focus is on identifying species that are approved for use as novel foods or insect feeds within the European Union (e.g., house cricket (Acheta domesticus), common mealworm (Tenebrio molitor), migratory locust (Locusta migratoria), lesser mealworm (Alphitobius diaperinus), black soldier fly (Hermetia illucens), banded cricket (Gryllodes sigillatus), field cricket (Gryllus assimilis), silkworm (Bombyx mori)). However, insect species of global relevance are also discussed. The suitability of DNA analysis methods for accurate species identification, detection of (un)labeled contaminants, and monitoring of genetic diversity has been demonstrated. Full article

The wine industry faces increasing challenges related to authenticity, safety, and sustainability due to recurrent fraud, shifting consumer preferences, and environmental concerns. In this study, as part of the B.I.O.C.E.R.T.O project, we integrated blockchain technology with ultrasonic spectroscopy and soil quality data by using the arthropod-based Soil Biological Quality Index (QBS-ar) to enhance traceability, ensure wine quality, and certify sustainable vineyard practices. Four representative wines from the Marche region (Sangiovese, Maceratino, and two Verdicchio PDO varieties) were analyzed across two vintages (2021 and 2022). Ultrasound spectroscopy demonstrated high sensitivity in distinguishing wines based on ethanol and sugar content, comparably to conventional viscosity-based methods. The QBS-ar index was applied to investigate the soil biodiversity status according to the agricultural management practices applied in each vineyard, reinforcing consumer confidence in environmentally responsible viticulture. By recording these data on a public blockchain, we developed a secure, transparent, and immutable certification system to verify the geographical origin of wines along with their unique characteristics. This is the first study to integrate advanced analytical techniques with blockchain technology for wine traceability, simultaneously addressing counterfeiting, consumer demand for transparency, and biodiversity preservation. Our findings support the applicability of this model to other agri-food sectors, with potential for expansion through additional analytical techniques, such as isotopic analysis and further agroecosystem sustainability indicators. Full article

Food waste poses critical environmental, economic, and social challenges, with consumer behavior recognized as a key leverage point for intervention. Packaging plays a vital role in preserving food quality and reducing waste, yet its behavioral influence on household food waste (HFW) remains underexplored. This review systematically examines 52 studies investigating the impact of food packaging—excluding storage/date labeling—on consumer food waste (CFW) behaviors. Using a structured methodology, we classified studies by methodological design, geographic coverage, food types, and focal packaging features. The analysis reveals a dominant reliance on consumer surveys and short-duration diaries, with limited application of rigorous experimental methods. Geographically, the English-language literature is skewed toward high-income countries, particularly Australia and Europe, with notable gaps in regions such as Asia and Africa. Moreover, despite U.S. households discarding approximately 40% of their food, research coverage remains limited. The findings also expose a misalignment between research focus and consumer-perceived importance of packaging features; attributes such as transparency, grip/shape, and dispensing mechanisms are frequently rated as important by consumers but are under-represented in the literature. This review contributes by identifying these gaps, synthesizing behavioral evidence, and offering a roadmap for future research and design innovation. By better aligning packaging functionalities with real-world behaviors, this work supports the development of consumer-informed solutions to mitigate HFW and promote sustainable food systems. Full article

Background: Major Depressive Disorder represents a prevalent and critical mental health issue that highlights the pressing need for innovative therapeutic solutions. Recent research has identified dysfunction within the glutamate system as a crucial element influencing both the onset and management of depressive symptoms. Although TAK-653 is a new positive allosteric modulator of AMPA receptors, its effects have not been rigorously examined in models of depression in primates. Methods: To assess its potential antidepressant properties, a chronic unpredictable mild stress protocol was implemented over 12 weeks to create a monkey model of depression, followed by a two-week treatment period with TAK-653. Results: Behavioral evaluations showed that following stress exposure, the monkeys exhibited reduced motivation for food, increased huddling, diminished movement, and a tendency to remain at the lower levels of their enclosure. They also displayed heightened anxiety in response to external stimuli. Plasma analyses indicated higher levels of cortisol, IL-6, and IL-8 in the stressed monkeys compared to baseline readings, confirming the efficacy of the stress-inducing protocol. Post-treatment with TAK-653 resulted in significant improvements, such as enhanced motivation for food, less huddling behavior, greater activity, and a move towards the upper areas of the enclosure. Additionally, the plasma analysis revealed a marked decrease in cortisol and IL-6 levels, along with an increased expression of BDNF. Conclusions: These findings indicate that TAK-653 effectively alleviates depression-like behaviors in nonhuman primate models, thereby paving the way for a promising new strategy in the treatment of depression. Full article

From germination to harvest, cereal crops are constantly exposed to a broad spectrum of abiotic stresses that significantly hinder their growth and productivity, posing a serious threat to global food security. Seed resilience and performance are foundational to sustainable agriculture, making the development of efficient, low-cost, and environmentally friendly strategies to enhance seed vigor and stress tolerance a critical priority. Seed priming has emerged as a promising pre-sowing technique that involves exposing seeds to specific organic or inorganic compounds under controlled conditions to improve their physiological and biochemical traits. Various priming techniques—including halopriming, chemical priming, osmopriming, hormonal priming, hydropriming, biopriming, and nanopriming—have been successfully applied in cereal crops to alleviate the adverse effects of environmental stressors. These treatments trigger a cascade of metabolic and molecular responses, including the modulation of hormonal signaling, enhancement of antioxidant defense systems, stabilization of cellular structures, and upregulation of stress-responsive genes. Together, these changes contribute to enhanced seed germination, improved growth and performance, and greater adaptability to abiotic stress conditions. This review provides a comprehensive overview of seed priming strategies in cereal crops, emphasizing their mechanisms of action and their impact on plant performance in challenging environments. Full article

Automated food safety inspection systems rely heavily on the visual detection of contamination, spoilage, and foreign objects in food products. Current approaches typically require extensive labeled training data for each specific hazard type, limiting generalizability to novel or rare safety issues. We propose a zero-shot detection framework for visual food safety hazards that enables the identification of previously unseen contamination types without requiring explicit training examples. Our approach adapts and extends the Knowledge-Enhanced Feature Synthesizer (KEFS) methodology to the food safety domain by constructing a specialized knowledge graph that encodes visual safety attributes and their correlations with food categories. We introduce a Food Safety Knowledge Graph (FSKG) that models the relationships between 26 food categories and 48 visual safety attributes (e.g., discoloration, mold patterns, foreign material characteristics) extracted from food safety databases and expert knowledge. Using this graph as the prior knowledge, our system synthesizes discriminative visual features for unseen hazard classes through a multi-source graph fusion module and region feature diffusion model. Experiments on our newly constructed Food Safety Visual Hazards (FSVH) dataset demonstrate that our approach achieves 63.7% mAP in zero-shot hazard detection, outperforming state-of-the-art general zero-shot detection methods by 6.9%. Furthermore, our framework demonstrates robust generalization to fine-grained novel hazard categories while maintaining high detection performance (59.8% harmonic mean) in generalized zero-shot scenarios where both seen and unseen hazards may occur simultaneously. This work represents a significant advancement toward automated, generalizable food safety inspection systems capable of adapting to emerging visual hazards without a costly retraining process. Full article

Biomaterials and biomedical devices interact with the human body at different levels. At one end of the spectrum, medical devices in contact with tissue pose risks depending on whether they are deployed on the skin or implanted. On the other hand, food packaging and associated material technologies must also be biocompatible to prevent the transfer of harmful molecules and contamination of food, which could impact human health. These seemingly unlinked domains converge into a shared need for the elaboration of new laboratory evaluation protocols that consider recent advances in biomaterials and biodevices, coupled with increasing legal restrictions on the use of animal models. Here, we aim to select and prescribe physiologically relevant microenvironment conditions for biocompatibility testing of novel biomaterials and biodevices. Our discussion spans (1) the development of testing protocols according to material classes, (2) current legislation and standards, and (3) the preparation of biomimetic setups that replicate the microenvironment, with a focus on the multidisciplinary dimension of such studies. Testing spans several characterization domains, beginning with chemical properties, followed by mechanical integrity and, finally, biological response. Biomimetic testing conditions typically include temperature fluctuations, humidity, mechanical stress and loading, exposure to body fluids, and interaction with multifaceted biological systems. Full article

The increase in chronic diseases and climate change in recent decades has been driven by food systems that affect both human health and the environment. This study investigated the interrelation between food consumption, obesity, undernutrition, and climate change, aiming to understand how these factors connect within the global syndemic. The methodology used was a scoping review, in which 12 articles were analyzed after an initial search that resulted in 11,208 references. The references were screened using the Rayyan software (Rayyan Systems Inc. (Doha, Qatar), version 1.6.1 and web-based version), removing duplicates and assessing the studies based on eligibility criteria. The articles addressed different aspects, such as the relationship between food consumption, obesity, undernutrition, and climate change, providing data on how food insecurity and socioeconomic conditions influence these conditions. In sequence, we developed a conceptual model to offer a detailed view of the factors affecting the global syndemic, considering the availability of food, its accessibility, stability in supply, and its use in the diet. The model recognizes that climate change affects food consumption both directly and indirectly. Direct effects include the impact of extreme weather events—such as floods and droughts—on the availability, access, quantity, and quality of food. Indirectly, climate change exacerbates socioeconomic vulnerabilities and disrupts food systems in more structural ways, contributing to increased food insecurity. The findings revealed that food insecurity, in turn, can lead to both obesity and undernutrition, particularly among vulnerable populations. There was a scarcity of studies that integrated the relationship between undernutrition, climate change, and food consumption, especially in certain regional contexts such as Latin America. The evidence gathered in the literature and the conceptual model provide a foundation for future research and the development of more effective public policies that integrate food issues, public health, and climate change in a more holistic and interconnected approach. Full article

The Chinese mitten crab (Eriocheir sinensis) is a high-value seafood. Efficient quality-grading methods are needed to meet rapid increases in demand. The current grading system for crabs primarily relies on manual observations and weights; it is thus inefficient, requires large amounts of labor, is costly, and no longer meets the requirements for the market. Here, we employed computer vision techniques combined with deep learning modeling to efficiently quantify key physiological traits, such as sex identification, carapace dimensions (length and width), and fatness assessment for quality classification. To this end, a YOLOv5-seg integrated with an SE attention model was developed using 2282 RGB images and manual measurements of the physiological traits of 300 crabs. The RGB dataset was further augmented by rotating and resizing. The results revealed that the accuracy of sex recognition was 100%, and the mAP for carapace segmentation was 0.995, which was superior to YOLOv8-seg and other variants. In addition, we proposed an improved conditional factor K to evaluate the fatness of crabs and classify their quality based on fatness. The consistency between the grading method proposed in this article and manual grading was 100%. This study could aid in developing precise and non-destructive grading systems for the aquaculture and food industries. Full article

The growing human population has increased the need for food beyond what terrestrial sources can provide. This boosts aquaculture demand for molluscs, fish, and crustaceans. Molluscs are popular for their nutritional benefits, making them a profitable industry. Despite a 3% annual growth in mollusc populations, recent high mortality rates and population losses due to poor feeding practices and water pollution have made them more disease-prone. Limited treatment options exist for mollusc diseases in aquaculture systems. Hence, developing rapid, sensitive, and cost-effective diagnostic tools for field use is essential to identify and prevent infections promptly. Recently developed isothermal nucleic acid amplification technologies, like loop-mediated isothermal amplification (LAMP) and recombinase polymerase amplification (RPA), offer rapid results within an hour. This review examines these isothermal diagnostic techniques for mollusc pathogens and their potential for field application. Full article

Zearalenone (ZEA), a potent mycotoxin commonly found in contaminated grains, presents a serious threat to food safety and public health. Conventional detection methods, including culture-based assays and laboratory-bound analytical tools, are often time-consuming, require specialized infrastructure, and lack portability, limiting their utility for rapid, on-site screening. In response, this study introduces a compact, real-time electrochemical sensing platform for the swift and selective detection of ZEA in corn flour matrices. Utilizing a non-faradaic, label-free approach based on Electrochemical Impedance Spectroscopy (EIS), the sensor leverages ZEA-specific antibodies to achieve rapid detection within 5 min. The platform demonstrates a low detection limit of 0.05 ng/mL, with a broad dynamic range from 0.1 ng/mL to 25.6 ng/mL. Reproducibility tests confirm consistent performance, with both inter- and intra-assay variation remaining under a 20% coefficient of variation (%CV). Comparative evaluation with standard benchtop systems underscores its accuracy and field applicability. This portable and user-friendly device provides a powerful tool for real-time mycotoxin monitoring, offering significant potential for improving food safety practices and enabling point-of-need testing in resource-limited settings. Full article

Agriculture is a key sector for economic growth, food security, and rural livelihoods within the member nations of the Southern African Development Community (SADC). However, the agricultural systems throughout the SADC regions face serious threats from climate change, which is seen through temperature rises, irregular rainfall patterns, and the rising frequency of droughts. The study examines the impacts of climate change on agricultural productivity in four SADC countries: South Africa, Zambia, Zimbabwe, and Malawi. It also assesses the impact of institutional structures, policy initiatives, and technological advancements in enhancing agricultural resilience to climate change. The Panel Autoregressive Distributed Lag (PARDL) model was employed to assess short and long run impact of climate change on agricultural productivity. The findings reveal that precipitation significantly increases agricultural productivity in the long run, but not in the short run. In addition, governance inefficiencies, which are measured by control of corruption index have negative long run impacts on agricultural productivity. The estimated speed of adjustment (ECT: −0.9557) demonstrated a strong long run equilibrium relationship, indicating that agricultural productivity converges to its long run trend regardless of short run fluctuations. In conclusion, the findings of this study provide essential knowledge to assist policymakers, researchers, and development agencies in the creation of evidence-based policies aimed at improving agricultural resilience to climate change across SADC member countries. Full article

Indoor farming presents a sustainable response to urbanization and climate change, yet optimizing light use efficiency (LUE) remains vital for maximizing crop yield and minimizing energy use. This study introduces an IoT-based framework for adaptive light management in controlled environments, using lettuce (Lactuca sativa L.) as a model crop due to its rapid growth and sensitivity to light spectra. The system integrates advanced LED lighting, real-time sensors, and cloud-based analytics to enhance light distribution and automate adjustments based on growth stages. The key findings indicate a 20% increase in energy efficiency and a 15% improvement in lettuce growth compared to traditional static models. Novel metrics—Light Use Efficiency at Growth stage Canopy Level (LUEP) and Lamp Level (LUEL)—were developed to assess system performance comprehensively. Simulations identified optimal growth conditions, including a light intensity of 350–400 µmol/m²/s and photoperiods of 16–17 h/day. Spectral optimization showed that a balanced blue-red light mix benefits vegetative growth, while higher red content supports flowering. The framework’s feedback control ensures rapid (<2 s) and accurate (>97%) adjustments to environmental deviations, maintaining ideal conditions throughout growth stages. Comparative analysis confirms the adaptive system’s superiority over static models in responding to dynamic environmental conditions and improving performance metrics like LUEP and LUEL. Practical recommendations include stage-specific guidelines for light spectrum, intensity, and duration to enhance both energy efficiency and crop productivity. While tailored to lettuce, the modular system design allows for adaptation to a variety of leafy greens and other crops with species-specific calibration. This research demonstrates the potential of IoT-driven adaptive lighting systems to advance precision agriculture in indoor environments, offering scalable, energy-efficient solutions for sustainable food production. Full article

A factory energy management system, based on information and communication technology, facilitates efficient energy management using the real-time monitoring, analyzing, and controlling of the energy consumption of a factory. However, traditional food processing plants use basic control systems that cannot analyze energy consumption for each phase of processing. This makes it difficult to identify usage patterns for individual operations. This study identifies steam energy consumption patterns across four stages of food processing. Additionally, it proposes a customized predictive model employing four machine learning algorithms—linear regression, decision tree, random forest, and k-nearest neighbor—as well as two deep learning algorithms: long short-term memory and multi-layer perceptron. The enhanced multi-layer perceptron model achieved a high performance, with a coefficient of determination (R²) of 0.9418, a coefficient of variation of root mean square error (CVRMSE) of 9.49%, and a relative accuracy of 93.28%. The results of this study demonstrate that straightforward data and models can accurately predict steam energy consumption for individual processes. These findings suggest that a customized predictive model, tailored to the energy consumption characteristics of each process, can offer precise energy operation guidance for food manufacturers, thereby improving energy efficiency and reducing consumption. Full article

Antioxidant peptides derived from woody oil resource by-products exhibit strong free radical scavenging abilities and offer potential applications in functional foods, nutraceuticals, and cosmetics. This review summarizes the latest advances in preparation technologies, including enzymatic hydrolysis, microbial fermentation, chemical synthesis, recombinant expression, and molecular imprinting, each with distinct advantages in yield, selectivity, and scalability. The structure–activity relationships of antioxidant peptides are explored with respect to amino acid composition, molecular weight, and 3D conformation, which collectively determine their bioactivity and stability. Additionally, emerging delivery systems—such as nanoliposomes, microencapsulation, and cell-penetrating peptides—are discussed for their role in enhancing peptide stability, absorption, and targeted release. Mechanistic studies reveal that antioxidant peptides from woody oil resources act through network pharmacology, engaging core signaling pathways, including Nrf2/ARE, PI3K/Akt, AMPK, and JAK/STAT, to regulate oxidative stress, mitochondrial health, and inflammation. Preliminary safety data from in vitro, animal, and early clinical studies suggest low toxicity and favorable tolerability. The integration of omics technologies, molecular docking, and bioinformatics is accelerating the mechanism-driven design and functional validation of peptides. In conclusion, antioxidant peptides derived from woody oil resources represent a sustainable, multifunctional, and scalable solution for improving human health and promoting a circular bioeconomy. Future research should focus on structural optimization, delivery enhancement, and clinical validation to facilitate their industrial translation. Full article