Search Results (645)

Rising global demand for safe, high-quality foods has accelerated the development of rapid, sensitive, and cost-effective analytical technologies for detecting harmful substances and quality markers. Electrochemical sensors have emerged as promising tools for food safety monitoring due to their high sensitivity, fast response, portability, and affordability compared with conventional laboratory methods. This review highlights recent advances in nanostructured electrochemical sensors for detecting key food analytes, including antioxidants, mycotoxins, allergens, and flavor compounds in diverse food matrices. It examines advanced nanomaterials such as metal oxides, MXenes, doped carbon nitrides, and noble metal-decorated graphene, which enhance sensor performance through improved surface area, conductivity, and electrocatalytic activity. Integrated with screen-printed or glassy carbon electrodes, these materials achieve ultra-low detection limits, wide linear ranges, and strong selectivity in complex food systems. The review also explores next-generation applications such as NFC-enabled smart packaging for continuous, non-invasive monitoring across the supply chain. Emerging trends in miniaturization, multiplex sensing, and artificial intelligence are discussed, along with key challenges in translating laboratory innovations into practical commercial solutions for global food safety. Full article

Background: Blockchain and distributed ledger technologies are increasingly proposed to strengthen traceability, governance, visibility, and coordination in mining and mineral supply chains, but mining-specific evidence remains fragmented. Methods: We conducted a systematic mapping review of peer-reviewed articles indexed in Scopus and Web of Science to examine application contexts, functional roles, technical architectures, evidence types, and adoption constraints of blockchain-enabled systems in these settings. Results: The review shows that blockchain is used across five functional domains: traceability and provenance; governance and secure data control; operational monitoring and inspection; energy and market coordination; and sustainability and environmental surveillance. Permissioned and consortium-based architectures predominated and were commonly combined with sensors, external storage, identity mechanisms, and smart contracts. Evidence was strongest for technical feasibility under simulated, experimental, comparative, or bounded pilot conditions, whereas durable economic, social, and governance outcomes remained less substantiated. Conclusions: Blockchain is most credible in mining contexts when it supports controlled coordination, auditable recordkeeping, and process integrity. Its practical value depends on reliable physical-to-digital data capture, workable governance arrangements, interoperability, and validation under real institutional and operational conditions. Full article

Background: Global food supply chains increasingly require sustainable and transparent operations; however, empirical evidence linking sustainability practices to firm performance remains inconsistent. This study examines how sustainability practices are translated into measurable business performance outcomes through traceability implementation in Thailand’s export-oriented food-processing sector. Methods: Grounded in Stakeholder Theory, traceability implementation was conceptualized as an accountability-oriented operational mechanism enabling the systematic verification of sustainability-related activities. Data were collected from 362 export-oriented food-processing firms in Thailand and analyzed using covariance-based Structural Equation Modeling (SEM). Results: The findings indicate that sustainability practices significantly influence both traceability implementation and business performance, while traceability implementation partially mediates the sustainability–performance relationship. The results further suggest that sustainability practices generate both direct and indirect performance benefits through structured monitoring, documentation, and verification routines. Conclusions: This study demonstrates that sustainability practices become more performance-relevant when institutionalized through traceable and verifiable operational processes. The findings highlight the importance of integrating traceability implementation into sustainability strategies to strengthen transparency, stakeholder confidence, and competitiveness within export-oriented food supply chain contexts. Full article

This paper presents a comprehensive review on near-infrared (NIR) spectroscopy applied in food analysis, systematically elaborating its core principles, widespread industrial applications, advanced chemometric strategies, and cutting-edge technological progress. NIR spectroscopy (760–2500 nm), characterized by rapid, non-destructive detection and minimal sample preparation, has been widely implemented in quality evaluation and safety monitoring of grains, meat, fruits and vegetables, dairy, fermented products, tea, coffee, and other processed foods, realizing quantitative analysis of nutrients, freshness assessment, texture prediction, adulteration identification, origin tracing, and rapid preliminary screening of toxin/pesticide residues. A series of chemometric methods, including spectral preprocessing (SNV, MSC, S-G smoothing), feature extraction, and variable selection (CARS, PSO-CMW, ICPA), as well as linear/nonlinear modeling algorithms (PLS, SVM, BP-ANN, fuzzy clustering) significantly boost the accuracy and robustness of spectral analysis. Meanwhile, portable NIR devices and online monitoring systems promote on-site and real-time detection in food supply chains. Despite existing challenges such as calibration transfer, matrix interference, and model generalization, innovations like multimodal data fusion, deep learning integration, and intelligent algorithm optimization offer effective solutions. This review not only summarizes the latest research advances of NIR technology in the food field but also emphasizes its significant advantages as a rapid, non-destructive complementary tool to traditional destructive detection methods, providing theoretical support and technical reference for accelerating the industrial translation and standardized application of NIR spectroscopy, and ultimately safeguarding global food quality and safety. Full article

Early and non-destructive identification of fungal contamination in cereals is essential to support post-harvest management, reduce economic losses, and mitigate food safety risks along the wheat supply chain. Among filamentous fungi, Alternaria spp. are widespread contaminants of durum wheat and producers of toxic secondary metabolites such as alternariol (AOH), whose early detection remains analytically challenging. The aim of this study was to evaluate the potential of near-infrared transmittance (NIT) spectroscopy as a rapid, non-destructive pre-screening tool for the early identification of Alternaria-contaminated durum wheat lots and associated AOH risk. Samples from three durum wheat cultivars were artificially inoculated with Alternaria spp. and monitored over time. NIT spectra (570–1100 nm) were acquired in transmittance mode and analyzed using partial least squares (PLS) regression, focusing on the 870–1100 nm spectral region. Clear and time-dependent spectral differences were observed between inoculated and control samples, with the strongest discriminative features at 834 and 966 nm. Classification performance was high, with area under the curve (AUC) values between 0.96 and 0.97. ELISA analysis confirmed progressive AOH accumulation in inoculated kernels, consistent with the observed spectral changes, while control experiments excluded autoclaving and visual grain damage as confounding factors. From an applied perspective, the results indicate that NIT spectroscopy can support post-harvest decision-making as a rapid pre-screening approach, enabling the prioritization of suspect wheat lots for confirmatory analytical testing. Multivariate analysis further confirmed the consistency of spectral differences across datasets. Full article

Carbon Information Disclosure (CID) has emerged as an essential tool for achieving global sustainable development. While existing literature has extensively examined firm-level and institutional drivers of CID, the impact of supply chain network structure remains underexplored, particularly in developing economies. To bridge this gap, this study investigates the impact of supply chain network centrality on CID using a sample of Chinese A-share listed companies from 2008 to 2023. Our empirical results reveal a negative relationship between centrality and CID, suggesting that central firms tend to reduce carbon information disclosure levels to avoid proprietary costs, rather than signaling their environmental legitimacy. Mechanism analysis indicates that centrality inhibits CID through two suggested pathways: by crowding out green technology innovation and by reducing the participation of green investors. However, we find that strong external supervision, such as government environmental attention and media attention, can effectively weaken this inhibitory effect. This effect is also mitigated when firms are subject to heightened regulatory monitoring through China’s Carbon Emissions Trading Scheme pilot. Furthermore, heterogeneity analysis shows that this negative impact is more pronounced in non-heavily polluting sectors where regulatory constraints are softer, while market concentration does not yield a significant heterogeneous impact. Full article

This research presents an integrated framework for supply chain finance in which digital twin, blockchain, and multi-objective fuzzy optimization are used in synergy to improve financial decision-making in dynamic and uncertain environments. In this framework, the digital twin acts as a real-time monitoring and forecasting layer, blockchain acts as a trust and transparency infrastructure, and the optimization model acts as the decision-making core. To evaluate the proposed framework, a scenario-based mathematical model was developed and analyzed using a combination of real-world and simulated data. The results showed that the proposed framework was able to reduce the total cost by 18.6% and increase the return on investment to 12.4%. Also, the use of the digital twin framework significantly reduced financial risks and delays, while the integration of blockchain improved the transparency, traceability, and reliability of transactions and reduced operational errors. Overall, the findings show that this framework has high potential for developing smart, transparent, and resilient financial systems in the supply chain context. 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

The global network of pipelines constitutes a strategic backbone for the world economy, enabling safe and efficient transportation of energy products. These pipelines serve distinct functions in the energy supply chain: gas pipelines support emerging cleaner energy carriers; multi-product pipelines provide versatility in transporting refined liquid fuels; and oil pipelines remain dominant for crude oil delivery. Energy management across the pipeline value chain emphasizes efficiency optimization, cost reduction, and sustainability through real-time monitoring, data analytics, integrated systems, and technological innovations spanning operations, maintenance, and emission control. Despite their critical role, petroleum depots remain relatively understudied, particularly in developing and Sub-Saharan African contexts. This review synthesizes insights from over 100 studies on energy-efficient pumping, predictive control, digitalization, and socio-technical energy management in depots. Analysis of these studies highlights recurring operational and infrastructural issues that constrain energy efficiency in depots. The challenges include irregular truck-loading schedules, frequent pump cycling, aging equipment, power-supply instability, manual operator interventions, and policy-driven constraints. The reviewed studies demonstrate that anticipatory, multi-layer control strategies integrating short-horizon flow forecasting, hybrid model predictive control, and cyber–physical–human–institutional system representations outperform reactive approaches in mitigating energy losses and operational variability. Site-specific calibration and phased deployment emerge as pragmatic pathways for implementing advanced energy optimization under the constrained conditions typical of real-world petroleum depots. Full article

Accurate estimation of dynamic environmental phenomena through intelligent sensing systems plays a critical role in enabling reliable monitoring and decision-making in complex real-world scenarios. With the rapid development of artificial intelligence-driven sensing technologies and Internet of Things systems, modern agricultural monitoring is evolving from isolated data acquisition toward intelligent, multimodal perception and decision-making. However, traditional approaches predominantly rely on single data sources, making it difficult to simultaneously capture plant phenotypic variations and environment-driven mechanisms, thereby limiting model applicability in complex field scenarios. To address this issue, a multimodal pest density estimation framework, namely the Pest Density Estimation Framework (PDEF), is proposed, which integrates UAV-based imagery, trap monitoring data, and environmental sensor measurements. In this framework, crop canopy damage features are extracted using convolutional neural networks, while temporal encoding is employed to model dynamic environmental variations. Cross-modal feature alignment and environment-aware enhancement mechanisms are further introduced to achieve deep integration of multi-source information, enabling the construction of a unified feature representation space and improving estimation accuracy. Extensive experiments conducted on a constructed multimodal agricultural dataset demonstrate that the proposed method achieves MAE, RMSE, and MAPE values of $5.47$, $7.62$, and $14.9\%$, respectively, significantly outperforming the Transformer-based fusion model (MAE $6.01$, RMSE $8.16$). Meanwhile, the coefficient of determination reaches $R^2=0.84$, indicating superior fitting capability and stability. In multimodal combination experiments, the three-modality fusion reduces error metrics by more than $20\%$ on average compared with single-modality models, validating the effectiveness of multi-source collaborative modeling. From the perspective of integrating plant phenotypic analysis and environmental perception, this study provides a novel AI-driven intelligent sensing framework for pest monitoring and crop management, contributing to improved pest prediction capability and enhanced intelligence in agricultural production systems. This study further provides practical implications for agricultural economics and supply chain optimization by enabling data-driven decision-making through intelligent sensing systems. Full article

Situated within Saudi Arabia’s Vision 2030 transformation agenda, this study examines the performance implications of sustainable logistics practices (SLPs) and the mediating role of Management Information Systems (MIS). Although achieving a “double bottom line” is a central premise of sustainable supply chain management, its realization in state-driven emerging economies remains unclear. Drawing on the Natural Resource-Based View and Stakeholder Theory, a structural equation model is tested using survey data from 372 logistics and supply chain professionals in Saudi Arabia. The model assesses the effects of Green Transportation, Sustainable Packaging, and Sustainable Waste Management on Environmental Sustainability and Economic Performance. The results reveal a clear “Economic Performance paradox.” While all three practices significantly enhance Environmental Sustainability, only Sustainable Waste Management directly improves Economic Performance. Moreover, Green MIS significantly mediates the relationship between sustainable logistics practices and Environmental Sustainability but shows no direct or mediating effect on Economic Performance. This indicates a prevailing compliance-oriented use of MIS, where firms prioritize environmental monitoring and reporting over operational optimization. This study demonstrates that the double bottom line is not automatic, but contingent on practice type and institutional context. By providing firm-level evidence from Saudi Arabia, the study extends sustainable logistics and information systems research and offers contextually grounded insights for managers and policymakers. Full article

Illegal, unreported, and unregulated (IUU) fishing activities have become one of the most critical challenges facing the global fish industry, particularly in developing countries, with the economic impact of fish fraud reaching billions of dollars annually. A major contributor to this problem is the limitation of conventional fish supply chain systems, which lack secure data sharing among stakeholders, fail to provide trusted product information to consumers, and offer insufficient transparency for regulatory authorities. These shortcomings facilitate fraud and weaken trust and oversight across the supply chain. Blockchain technology has demonstrated strong capability to address key cybersecurity challenges by enhancing traceability, transparency, and tamper-resistant data integrity across distributed supply chain stakeholders. In this paper, we present an enterprise-oriented prototype of a secure, permissioned blockchain-based fish supply chain system designed to enable trusted data sharing and end-to-end traceability across multi-stakeholder environments. Building upon our prior work in Ethereum-based seafood quality monitoring, this study contributes: (1) a modular, consortium-grade architecture implemented using Hyperledger Fabric and containerized via Docker, supporting scalable organizational participation; (2) formal UML-based system modeling of supply chain actors, assets, and lifecycle transitions; and (3) custom chaincode logic that enforces ownership transfer workflows and regulatory compliance policies. In addition, the architecture is designed as agent-ready, exposing standardized APIs that enable future integration of autonomous AI-driven client applications for proactive supply chain orchestration. By leveraging a private, permissioned network model, the functional prototype demonstrates the feasibility of improving data veracity and providing a practical foundation for mitigating fraud and enhancing regulatory oversight in the global fish industry. Full article

Background/Objectives: Rapid identification of foodborne pathogens is of high practical significance because it enables prompt epidemiological response, timely patient management, and effective sanitary control of food products. In this study, we developed an integrated molecular platform combining recombinase polymerase amplification (RPA) with CRISPR/Cas12a technology for rapid, sensitive, and specific detection of Salmonella enterica. Methods: Four virulence genes (sirA, stn, siiD, and pagN) were selected as targets to ensure reliable pathogen identification. Reaction conditions were optimized using the Moraxella bovoculi Cas12a (MbCas12a) nuclease. The study focused on integrating isothermal amplification with a custom-engineered hardware solution for visual fluorescence detection. Results: The developed method demonstrated sensitive and specific detection, with no cross-reactivity to non-target microorganisms. Optimization allowed for a substantially reduced assay time of approximately 30 min. As a result, a portable fluorescence visualization approach was developed, featuring a 3D-printed housing and an integrated ultraviolet light source for direct visual fluorescence detection. This allows rapid differentiation of samples without specialized laboratory equipment, making it suitable for field applications. Conclusions: The combination of isothermal amplification, MbCas12a-based detection, and the portable fluorescence visualization approach provides a versatile platform for rapid diagnostics and food safety monitoring. This approach has strong potential to improve public health outcomes and enhance the resilience of food supply chains by enabling accessible, field-deployable pathogen detection. Full article

Ensuring the quality, reliability, and efficiency of cold chain logistics for thermolabile pharmaceutical products, particularly vaccines, remains a critical challenge in global health supply chains. These biologics require stringent temperature control throughout storage, transport, and distribution to preserve their efficacy. Persistent issues such as maintaining product integrity, accurately forecasting vaccine demand, and fostering trust among stakeholders often result in inefficiencies, waste, and public mistrust. This study proposes an intelligent digital management framework specifically designed for vaccine cold chains, integrating blockchain, the Internet of Things (IoT), and machine learning (ML) to address these challenges in a holistic and sustainable manner. The main innovation of the study lies in combining secure traceability, real-time cold chain monitoring, and predictive decision support within a unified vaccine cold chain management framework rather than treating these functions as isolated technological solutions. Using WHO immunization coverage data and vaccine-related review data, the framework supports vaccine demand forecasting through the Informer model and stakeholder trust assessment through BERT-based sentiment analysis. In the sentiment analysis task, the BERT model achieved ~80% accuracy on dominant sentiment classes, with a weighted F1-score of 0.6974, demonstrating strong performance on imbalanced datasets. By minimizing vaccine spoilage and enabling more accurate demand planning, the system reduces excess production and distribution, thus lowering resource consumption, carbon emissions, and financial waste. Moreover, trust-informed analytics support better alignment of supply with actual community needs, fostering equity and resilience in vaccine distribution. While this framework has been validated through simulations and experimental evaluation, further real-world testing is needed to assess long-term stability and stakeholder adoption. Nonetheless, it provides a scalable and adaptive foundation for advancing sustainability and transparency in pharmaceutical cold chains. Full article

Disruptions caused by disasters, pandemics, and systemic crises have increased the complexity and vulnerability of global supply chains, highlighting the need for advanced analytical approaches to risk and resilience management. In this context, artificial intelligence (AI) has emerged as a promising analytical capability for improving risk assessment and decision-making in disrupted supply chains. The study follows PRISMA 2020 reporting guidelines adapted for bibliometric research and presents a bibliometric and knowledge-mapping analysis of artificial intelligence applications in disaster supply chain risk and resilience management. Using the Web of Science Core Collection, a dataset of 288 peer-reviewed publications was analyzed through keyword co-occurrence, bibliographic coupling, citation analysis, and collaboration network mapping. The findings indicate a rapidly expanding research field in which AI supports predictive risk assessment, real-time monitoring, and resilience-oriented decision-making in disaster-prone supply networks. The analysis identifies dominant thematic clusters, emerging research directions, and opportunities for integrating AI-enabled analytics into supply chain risk management frameworks. The mapped literature also suggests secondary interpretive implications for financial risk exposure and supply chain finance, rather than indicating a separately operationalized finance-specific bibliometric subfield. To enhance interpretive depth, an AI-assisted analytical layer was applied to refine thematic clusters and detect emerging trends. However, this layer operates as a complementary interpretive tool and is subject to methodological limitations, including sensitivity to keyword semantics, dependence on bibliometric outputs, and potential interpretive bias in AI-assisted thematic labeling. Consequently, the AI-assisted analysis is used to support, rather than replace, bibliometric findings. Overall, this study contributes to the emerging literature on artificial intelligence in disaster supply chain risk management and highlights future research opportunities, including improved methodological integration and enhanced analytical transparency in AI-assisted bibliometric research. Full article