Search Results (115)

With increasing concerns over food safety, fraud, and information asymmetry in global food supply chains, improving transparency has become a key challenge for modern agricultural systems. Traditional supply chains often lack visibility, leading to fragmented data, reduced consumer trust, and difficulties in verifying food product information. Blockchain technology has emerged as a promising solution for enabling decentralised and tamper-resistant data sharing across supply networks. However, despite its theoretical potential, empirical evidence remains limited regarding how blockchain adoption enhances transparency through specific operational mechanisms, such as traceability. Therefore, this study investigates the role of blockchain adoption in improving traceability and strengthening supply chain transparency within agri-food systems. Data were collected from Generation Z university students in Malaysia, selected due to their familiarity with digital technologies and heightened awareness of food sourcing issues. The study is based on a total of 253 usable responses analysed using IBM SPSS Statistics 27, employing reliability analysis, Exploratory Factor Analysis, Pearson correlation, multiple regression analysis, and the PROCESS Macro Model 4 with 5000 bootstrap resamples for mediation testing. The findings indicate that blockchain adoption is significantly and positively associated with traceability, which in turn is positively associated with overall supply chain transparency. Furthermore, traceability is found to act as a mediating factor, serving as the key mechanism through which blockchain generates transparency outcomes, via an indirect-only mediation pathway. Thus, based on this premise, it is evident that blockchain alone is not sufficient unless integrated with effective traceability systems, highlighting the importance of aligning digital infrastructure with physical supply chain processes to strengthen trust and resilience in agricultural systems. Full article

Poultry supply chains need to comply with Shariah requirements when supplying halal meat, which requires continuous quality improvement and multi-stakeholder inspection. However, current traceability systems have centralized opaque characteristics, fragmented records, and slow detection of anomalies, leading to food safety vulnerabilities and impractical recall protocols. To overcome these challenges, this paper presents an intelligent blockchain and Internet of Things (IoT)-based traceability system with a permissioned Hyperledger Fabric consortium network. A hybrid off-chain storage architecture supports scalable monitoring without ledger congestion: TimescaleDB stores high-frequency IoT sensor data (e.g., temperature and GPS), MinIO warehouses compliance documentation, while only immutable cryptographic hashes are stored on-chain to guarantee the integrity of the data. Halal governance is digitalized using role-based smart contracts that trigger real-time alerts, batch blocking, and automated recall upon environmental threshold breaches. Performance evaluation via Hyperledger Caliper indicates the system achieves 275 write transactions per second, a 300 TPS read throughput, an optimized read latency of 5 ms, and a write latency of less than 36 ms. Validated as a laboratory concept, this decentralized framework demonstrates proactive quality assurance, mitigates ledger bloat, and enhances halal-integrity trust. Full article

Food-safety systems increasingly need to manage large detection files and externally generated analytical results across multiple organizations while linking these records to batch-level control actions. This study proposes a blockchain-based dual-track mechanism for trusted circulation of food-safety detection data and batch-level risk control, using aflatoxin B1 (AFB1) as the empirical case. High-dimensional files are stored in InterPlanetary File System (IPFS) and anchored on-chain by content identifiers (CIDs); three authorized oracles use two-of-three matching of detection values and evidence hashes before contract-based state determination. A stage–device–operation inverted index identifies associated batches, while signed second-track confirmations drive GREEN/YELLOW/RED state transitions. Experiments on a four-node Quorum Byzantine Fault Tolerance (QBFT) network used 57 independent HyperPistachio samples with 86.625-mebibyte (MiB) band-interleaved-by-line (BIL) files. Real-file access was successfully completed, single-oracle failures were tolerated when two consistent oracle reports remained, and associated-batch query latency increased only from 13.06 to 16.78 ms as fanout rose from 1 to 40. A 115.15 min sustained run maintained consistent states across all four nodes. The study manages externally supplied AFB1 results rather than evaluating analytical AFB1 detection accuracy, and its experimental validation is limited to the AFB1 case. Full article

Background: Global food supply chains have become increasingly complex, sourcing ingredients from multiple countries and intermediaries, creating opportunities for fraud, adulteration, and mislabeling that may compromise consumer safety and market confidence. Digital traceability technologies have been suggested as potential countermeasures, but there is little concrete evidence of their impact in practice. This research presents an assessment of the maturity and effectiveness of these technologies, identifies implementation barriers and security/privacy concerns, and maps research gaps/future directions. Methods: A scoping review of 64 studies from 2023 to 2026 with data extracted from the Scopus and IEEE databases was carried out according to the PRISMA-ScR guidelines and a structured pre-specified data extraction framework. Results: The field is empirically immature, with none of the reviewed solutions offering a provably correct, adversarially tested solution to the oracle problem. There is a lack of alignment between on-chain immutability and GDPR right to erasure and an unequal burden of implementation costs imposed on smallholder producers. Conclusions: A gradual implementation of traceability regulations, along with cost-of-ownership models and harmonized certification measures that do not disadvantage smaller producers are proposed. Finally, field trials, adversarial testing and reporting results in a standardized format, capturing detection performance and implementation costs, are essential. Full article

This systematic review summarises advances in artificial intelligence (AI) and machine learning (ML) for foodborne pathogen detection, covering applications in various technologies (AI-assisted microscopy, spectroscopy, biosensors and sensor-based systems), food supply chains, analytical performance, operational metrics and regulatory developments, addressing gaps in previous reviews limited to individual technologies or lacking regulatory analysis. Following PRISMA 2020 guidelines, Scopus, PubMed, and Web of Science were searched from 1 January 2010 to 25 June 2026 using a validated string. Inclusion criteria were explicit detection of a pathogen, clearly described AI/ML algorithm, study evaluation on food or supply chains, and quantitative validation metrics. Exclusion criteria were chemical-only studies, human-diagnostic studies, or purely theoretical studies. Given heterogeneity in the evidence, qualitative quality indicators were favoured over formal quantitative risk-of-bias tools, in distinction to internal cross-validation versus independent external validation. Key data were extracted using a standardised matrix, and after screening and snowballing, the final corpus consisted of 152 studies. CNN (Convolutional Neural Network)-based microscopy provides >99% accuracy in bacterial identification, SERS (Surface-Enhanced Raman Spectroscopy) and CNN 98.68% for pathogens and 99.85% for resistant strains. ML-driven biosensors show 80–100% prediction accuracy in the presence of environmental noise. Yet, performance drops dramatically on external validation, with models falling from 95% internal to 78–82% on independent test sets. Supply chain applications cover meat, dairy, seafood and produce, but most are still at pilot scale. The main constraints are data heterogeneity, lack of public benchmarks, matrix interference, non-standard validation protocols, and regulatory dissonance. However, the integration of AI with Internet of Things (IoT), blockchain and edge computing improves sensitivity, reduces false results and enables real-time monitoring despite the challenges. AI is a powerful decision-support tool that complements existing food safety controls rather than replacing them. To translate these technologies reliably into routine practice, effective implementation requires rigorous external validation and regulatory harmonisation. Full article

Traditional petroleum-based packaging suffers from pollution and functional limits, making it unsuitable for next-generation food systems. In contrast, bio-based smart packaging—combining renewable substrates with responsive components—transforms packaging from a passive shell into an active quality monitor and supply chain information node through three interconnected pillars: renewability, real-time responsiveness to freshness markers, and digital traceability. Market figures confirm this shift, with the smart food packaging sector projected to reach USD 48.97 billion by 2028 (CAGR 4.49% from 2023). This review covers recent progress in natural polymers (cellulose, chitosan, alginate, gelatin) and bio-based polyesters (PLA, PHA). Their multiscale structures enable tunable mechanical and barrier properties while serving as hosts for intelligent functions. Two functional directions stand out: active preservation (antimicrobial, antioxidant, gas-regulating, stimulus-controlled release) and intelligent sensing (colorimetric indicators, bio-based sensors, nano-amplified signals for real-time freshness monitoring). Beyond material functions, digital tools such as IoT and blockchain turn packaging into interactive data nodes, linking material intelligence with full traceability to enhance food safety and supply chain efficiency. Key challenges remain with long-term operational stability, production costs, scalable manufacturing, and life cycle assessments. Nevertheless, bio-based smart packaging is expected to evolve through biomimetic design, process innovation, and system-level integration toward adaptability, multifunctionality, and intelligence, ultimately supporting safer, more transparent, efficient, and sustainable food systems. Full article

Food safety systems are undergoing a profound and urgent transformation, shifting from traditional end-product inspection models toward integrated, preventive, and predictive approaches supported by digital, genomic and data-driven technologies. Conventional frameworks face increasing limitations in the context of globalized supply chains, climate variability, emerging hazards, and growing sustainability demands. This structured narrative review critically examines the technological and governance trends driving the transition toward digital and predictive food safety systems, with particular emphasis on their implications for sustainability. Key enabling technologies (including artificial intelligence (AI), whole-genome sequencing (WGS), Internet of Things (IoT)-based monitoring, blockchain-enabled traceability, and predictive analytics) are analyzed in terms of their capacity to enhance early hazard detection, real-time surveillance, and risk anticipation across the food supply chain. Beyond a descriptive overview, this review integrates technological, regulatory, and governance dimensions to identify convergence points, implementation barriers, and sustainability trade-offs, with particular attention to small and medium-sized enterprises and low- and middle-income countries. Furthermore, a four-level Digital Maturity Framework is proposed to conceptualize progressive stages of technological integration, providing a structured pathway for the evolution from reactive to predictive food safety systems. While digital and predictive approaches offer significant potential to reduce food losses, improve transparency, and strengthen evidence-based decision-making, their effective implementation remains constrained by infrastructure gaps, data governance challenges, regulatory fragmentation, and unequal access to digital capabilities. Achieving resilient and sustainability-oriented food safety systems will therefore require coordinated innovation, regulatory harmonization, and inclusive digital transformation strategies. Full article

Effective supply chain traceability is indispensable for ensuring food safety, which is a significant social issue. Traditional traceability systems are mostly based on centralized databases, relying on a single entity or organization and facing problems such as insufficient transparency and the risk of data tampering. To address these issues, many studies have adopted blockchain technology, which offers advantages such as decentralization and immutability. However, challenges such as data credibility and insufficient protection of private data remain. This study proposes a multi-channel architecture based on Blockchain (Hyperledger Fabric in this study), in which data is partitioned and managed across dedicated channels to strengthen the protection of sensitive information. Furthermore, a trust and incentive design is implemented, featuring a trust-value calculation function and a reward–penalty mechanism that encourage participants to upload more truthful data and improve the reliability of data before it is recorded on the blockchain. In this paper, the design and implementation of the proposed system are explained in detail, and its performance is examined using Hyperledger Caliper, a blockchain performance benchmark framework. Functional evaluations indicate that the proposed system can be correctly implemented and that it correctly supports supply chain traceability, trust- and incentive-related, privacy protecting and other functions as designed, while performance evaluations indicate that it can maintain stable performance under higher workloads, suggesting that the proposed approach is practical and applicable to food supply chain traceability scenarios. Full article

Blockchain is increasingly applied in the agri-food sector to enhance traceability, data integrity, and accountability. However, its broader role in food system sustainability remains insufficiently characterized, particularly when examined against global megatrends shaping future agri-food transitions. This paper investigates how blockchain technology can reinforce sustainable, inclusive, and resilient food systems under the effect of major global megatrends. A structured literature review of peer-reviewed and industry sources was conducted to identify evidence on blockchain-enabled improvements in transparency, certification, and supply chain coordination. Complementary analysis of a curated dataset of European and international pilot implementations evaluated technological architectures, governance models, and demonstrated performance outcomes. Additionally, stakeholder-based foresight activities and scenarios representing alternative blockchain adoption pathways, developed within the TRUSTyFOOD project (GA: 101060534), were used to examine the interconnection between blockchain adoption and megatrends. Evidence from the literature and pilot cases indicates that blockchain can strengthen product-level traceability and improve verification of sustainability and safety claims. Cross-case analysis also reveals persistent constraints, including heterogeneous technical standards, limited interoperability, high deployment costs for smallholders, and governance risks arising from consortium-led platforms. Blockchain can function as an enabling digital layer for sustainable and resilient food systems and should be embedded in wider, participatory strategies that align digital innovation with long-term sustainability and equity goals in the agri-food sector. Full article

The quality and safety of grain and oil food are paramount to sustainable societal development and public health. Implementing early warning analysis and risk control is critical for the comprehensive identification and management of grain and oil food safety risks. However, traditional risk prediction models are limited by their inability to accurately analyze complex nonlinear data, while their reliance on centralized storage further undermines prediction credibility and traceability. This study proposes a deep learning risk prediction model integrated with a blockchain-based traceability mechanism. Firstly, a risk prediction model combining Grey Relational Analysis (GRA) and Bayesian-optimized Tabular Neural Network (TabNet-BO) is proposed, enabling precise and rapid fine-grained risk prediction of the data; Secondly, a risk prediction method combining blockchain and deep learning is proposed. This method first completes the prediction interaction with the deep learning model through a smart contract and then records the exceeding data and prediction results on the blockchain to ensure the authenticity and traceability of the data. At the same time, a storage optimization method is employed, where only the exceeding data is uploaded to the blockchain, while the non-exceeding data is encrypted and stored in the local database. Compared with existing models, the proposed model not only effectively enhances the prediction capability for grain and oil food quality and safety but also improves the transparency and credibility of data management. Full article

Food loss and waste (FLW) is a chronic problem across food systems worldwide, with meat being one of the most resource-intensive and perishable categories. The perishable character of meat, combined with complex cold chain requirements and consumer behavior, makes the sector particularly sensitive to inefficiencies and loss across all stages from production to consumption. This review synthesizes the latest advancements in new preservation technologies and supply chain efficiency strategies to minimize meat wastage and also outlines current challenges and future directions. New preservation technologies, such as high-pressure processing, cold plasma, pulsed electric fields, and modified atmosphere packaging, have substantial potential to extend shelf life while preserving nutritional and sensory quality. Active and intelligent packaging, bio-preservatives, and nanomaterials act as complementary solutions to enhance safety and quality control. At the same time, blockchain, IoT sensors, AI, and predictive analytics-driven digitalization of the supply chain are opening new opportunities in traceability, demand forecasting, and cold chain management. Nevertheless, regulatory uncertainty, high capital investment requirements, heterogeneity among meat types, and consumer hesitancy towards novel technologies remain significant barriers. Furthermore, the scalability of advanced solutions is limited in emerging nations due to digital inequalities. Convergent approaches that combine technical innovation with policy harmonization, stakeholder capacity building, and consumer education are essential to address these challenges. System-level strategies based on circular economy principles can further reduce meat loss and waste, while enabling by-product valorization and improving climate resilience. By integrating preservation innovations and digital tools within the framework of UN Sustainable Development Goal 12.3, the meat sector can make meaningful progress towards sustainable food systems, improved food safety, and enhanced environmental outcomes. Full article

Background: In the era of rapid digital transformation, efficient food logistics (FL) is critical for sustainability and competitiveness. Maintaining food quality, minimizing waste, and optimizing costs are complex challenges that advanced digital technologies aim to address, particularly amid growing e-commerce and last-mile delivery demands. This underscores the need for a structured, quantitative evaluation of technological solutions to ensure operational reliability, efficiency, and sustainability. Methods: This study employs a Multi-Criteria Decision Making (MCDM) model combining Criterion Impact LOSs (CILOS) and Multi-Objective Optimization on the basis of Simple Ratio Analysis (MOOSRA) to evaluate key FL technologies: IoT, blockchain, Big Data analytics, automation and robotics, and cloud/edge computing. Nine evaluation criteria relevant to logistics providers were used, covering operational efficiency, flexibility, sustainability, food safety, data reliability, KPI support, scalability, costs, and implementation speed. CILOS determined criteria weights by considering interdependencies, and MOOSRA ranked technologies by benefits-to-costs ratios. Sensitivity analysis validated result robustness. Results: Automation and robotics ranked highest for enhancing efficiency, reducing errors, and improving handling and safety. Blockchain was second, supporting traceability and data security. Big Data analytics was third, enabling demand prediction and inventory optimization. IoT ranked fourth, providing real-time monitoring, while cloud/edge computing ranked fifth due to indirect operational impact. Conclusions: The CILOS–MOOSRA model enables transparent, structured evaluation, integrating quantitative metrics with logistics providers’ priorities. Results highlight technologies that enhance efficiency, reliability, and sustainability while revealing integration challenges, providing a strategic foundation for digital transformation in FL. Full article

The integrity of certification processes in the agrifood and fishing industries is essential for combating fraud, ensuring food safety, and meeting rising consumer expectations for transparency and sustainability. Yet, current certification systems remain fragmented, and they are vulnerable to tampering and highly dependent on manual or centralized procedures. This study addresses these gaps by providing a comprehensive survey that systematically classifies blockchain-based certification technologies and methodologies applied to the fisheries sector. The survey examines how the blockchain enhances trust through immutable record-keeping, smart contracts, and decentralized verification mechanisms, ensuring authenticity and accountability across the supply chain. Special attention is given to case studies and implementations that focus on ensuring food safety, verifying sustainability claims, and fostering consumer trust through transparent labeling. Furthermore, the paper identifies technological barriers, such as scalability and interoperability, and puts forward a collection of functional and non-functional requirements for holistic blockchain implementation. By providing a detailed overview of current trends and gaps, this study aims to guide researchers, industry stakeholders, and policymakers in adopting and optimizing blockchain technologies for certification. The findings highlight the potential of blockchain to innovate certification systems, easing the way for more resilient, sustainable, and consumer-centric agrifood and fishing industries. Full article

Recent advancements in food packaging have transitioned from passive containment toward innovative smart systems that integrate active and intelligent functionalities to improve product preservation, safety, and consumer interaction. This review examines the evolution of these technologies, focusing on biodegradable polymers and nanomaterial-enhanced substrates that combine environmental sustainability with superior barriers and antimicrobial performance. Developments in embedded sensing systems, including chemical, temperature, and humidity sensors, enable the continuous monitoring of food quality and environmental conditions, supporting extended shelf-life and early contamination detection. Intelligent packaging further incorporates indicators, sensors, and data carriers that enhance transparency and traceability across supply chains. These systems are often connected through blockchain and Internet of Things (IoT) platforms for real-time data analysis. The review also addresses consumer engagement via interactive labels and personalized nutritional feedback, along with the economic, behavioral, and regulatory aspects influencing large-scale adoption. Life cycle assessments are analyzed to evaluate trade-offs between enhanced functionality and environmental impact, emphasizing recyclability and end-of-life strategies within circular economy frameworks. Finally, the article discusses current technical challenges while highlighting emerging trends such as AI-driven predictive analytics and IoT-enabled connectivity as key enablers of sustainable, efficient, and safe food packaging systems. Full article

Blockchain technology plays a critical role in strengthening traceability in food supply chains (FSCs), particularly in relation to transparency, authenticity, food safety, and sustainability. This study conducts a systematic review of 518 journal articles retrieved from Scopus and Web of Science and applies latent Dirichlet allocation (LDA) topic modeling to identify dominant research trends. The analysis reveals eight key themes, including blockchain adoption enablers and challenges, consumer perceptions, supply chain traceability systems, sustainability, and food safety applications. The findings highlight significant growth in academic interest and demonstrate how blockchain improves visibility and efficiency across supply chain actors. The review offers theoretical insights into blockchain’s interdisciplinary role in FSC traceability and provides practical guidance for farmers, food industries, policymakers, and technology developers, while outlining future research opportunities. Full article