Search Results (264)

Horse meat is characterized by high nutritional value, but due to the specific physiology and long lifespan of horses, it represents a significant pathway for the bioaccumulation of toxic elements. The aim of this study was to examine the presence of cadmium (Cd) and lead (Pb) in muscle, liver and kidney samples of horses slaughtered in Serbia during 2023 and 2024. The toxic elements were determined by flame atomic absorption spectrometry (FAAS). The mean concentrations of cadmium and lead were 0.19 and 0.51 mg/kg in horse muscle; 2.31 and 0.74 mg/kg in horse liver; and 7.70 and 0.68 mg/kg in horse kidneys. Statistically significant differences in mean concentrations were observed between horse tissues, seasons and different age categories (p < 0.001), but there was no difference between sexes (male and female) (p > 0.05). Cadmium levels were above the maximum permitted limits in 93.2% of liver samples, 97.7% of kidney samples, and 31.1% of muscle samples tested. The data obtained indicate the need for continuous monitoring and strict control of animal traceability, especially those raised near ecological hotspots. Full article

Terpinen-4-ol (TP4O) is a key monoterpene alcohol commonly used as a quality and authenticity marker in essential oils, cosmetics, herbal products, and pharmaceutical formulations. However, reliable and comparable quantification of TP4O across laboratories is challenged by variability in natural matrices and the limited availability of well-characterized, traceable reference materials. In this study, a high-purity certified reference material (CRM) of TP4O was developed and characterized by the National Institute of Metrology (Thailand). The material’s purity was determined using two independent and complementary approaches: a mass balance method (MB) method based on gas chromatography with flame ionization detection (GC-FID), Karl Fischer coulometric titration (KFT), and thermogravimetric analysis (TGA), and a quantitative ¹H NMR (qNMR) method employing DSS-d₆ as an internal standard. The purity values obtained using the MB (98.41 ± 0.09%) and qNMR (99.13 ± 0.94%) methods were statistically equivalent (p > 0.05). Based on the combined evaluation, a certified purity value of 98.77% with an expanded uncertainty of 3.05% (k = 2) was assigned. Homogeneity and short- and long-term stability assessments confirmed the suitability of the material for its intended use. This TP4O CRM provides an SI-traceable, high-purity reference to support calibration, method validation, and quality assurance in analytical applications involving essential oil components. Full article

In the context of the energy transition and the increasing deployment of low-carbon gases (hydrogen, biomethane), reliable analytical monitoring is required to support integrity assessment and traceability of gas infrastructures under diverse on-site conditions while limiting analytical costs through standardized sampling and a single analytical system. We developed and validated integrated workflows combining sampling and laboratory analysis for chemical and compound-specific isotope analysis (CSIA) of natural gas and associated gaseous effluents in underground storage. An original quantification approach was implemented, linking sampling pressure to the amount of each compound collected in vials, and coupled with δ¹³C and δ²H measurements of alkanes (C₁–C₃), CO₂ and H₂. Two complementary sampling modes were optimized and compared: conventional high-pressure cylinders and direct collection into vacuum-sealed vials suitable for a broad range of pressures and field conditions. Using reference gas mixtures and operational samples, both approaches showed good reproducibility and isotopic accuracy during laboratory validation and over two years of monitoring. In particular, δ²H determinations for alkanes and H₂ remained robust under low-pressure sampling typical of annular spaces (~1–2 bar), despite gas-composition fluctuations. These validated methodologies provide a flexible basis for routine, standardized monitoring of stored and circulating gases, including emerging low-carbon components. Full article

This study proposes a multi-objective, multi-class explainable modeling framework to explain country performance profiles in PISA Mathematics (PISAM), Reading (PISAR), and Science (PISAS). Instead of treating PISA as a simple ranking, the study models each country’s Low/Medium/High-achieving class and asks which structural signals the model relies on when assigning a country to this class. To this end, the study combines governance quality (e.g., accountability, control of corruption, and political stability, etc.), economic and administrative capacity, and regional/institutional location in a single prediction pipeline and explains the resulting classifications with SHAP contributions conditional on class. While the findings do not point to a single, universal determinant, in mathematics, high-level profiles cluster around political stability, economic scale barriers, and regional location, along with governance indicators; in reading, economic capacity is explicitly integrated into this institutional core; and in science, in addition to these two dimensions, the shared institutional dynamics of regional blocs come into play. Furthermore, the study not only produces explanations but also quantitatively reports their reliability. The fit with the model output (Fidelity) and the traceability of the decision logic (Faithfulness) are 0.95/0.85 for PISAM, 0.89/0.92 for PISAR, and 0.89/0.89 for PISAS, which demonstrates high internal consistency and traceability of the decision process. Overall, the study reframes the PISA results not as isolated test scores but as structural profiles generated by the combination of governance, capacity, and region, revealing the policy-relevant levers behind “high performance” as a transparent and reproducible decision-making pipeline. This provides policymakers with an important roadmap for creating a sustainable education policy. Full article

This study investigates the determinants of consumer adoption of blockchain technology (BCT) for traceability in the packaged food industry. Grounded in the Technology Acceptance Model (TAM), the research model incorporates perceived trust as a crucial factor influencing consumer attitudes and behavioral intentions. A survey instrument was developed based on an extensive literature review, measuring five key constructs: perceived usefulness, perceived ease of use, perceived trust, attitude, and behavioral intention. Data collected from a sample of Indian consumers were analyzed using confirmatory factor Analysis (CFA) and structural equation modeling (SEM) in AMOS. Furthermore, perceived trust has a non-significant influence on both consumer attitude and behavioral intention toward using BCT, whereas the traditional TAM constructs of perceived usefulness and ease of use exhibit mixed effects. This research contributes to the theoretical extension of technology adoption models by assessing the paramount role of trust in the context of BCT for food traceability. For practitioners, it provides actionable insights for stakeholders in the packaged food supply chain. It suggests that emphasizing the trust feature does not have a significant effect on blockchain technology adoption in the packaged food case. Full article

In the face of mounting complexity in container terminal operations, the selection of an effective information system is paramount. The TOS (Terminal Operating System) is the most significant of all the information systems in existence for terminals. The objective of this study is to establish a set of criteria for selecting container TOS, determine the priority weights of these criteria and investigate their interactions. To the author’s knowledge, this is the first study to address this topic in such a detailed context. The hybrid FAHP (Fuzzy Analytic Hierarchy Process) and F-DEMATEL (Fuzzy Decision-Making Trial and Evaluation Laboratory) methodology was employed for the 18 criteria that were identified through the academic literature and expert views. The findings demonstrated that container terminal operators have expressed an expectation for a TOS structure that integrates complex business processes, provides effective decision support, increases traceability, works in harmony with advanced technologies, supports smart port transformation processes, enhances digital maturity and enables rapid intervention in bottlenecks. Furthermore, the fact that TOSs should support integration with external stakeholders is also critical in terms of collaboration and transparency, which are of great importance in supply chain management. It is hoped that the present study will contribute to the relevant literature and also provide a structural framework for terminal operators to select the most suitable TOS and for providers to design the most effective product. Full article

The reliable quantification of urea in agricultural systems requires methods that combine metrological rigor with low environmental impact. This work develops and validates a micro-Raman method (λ = 532 nm) for the direct determination of urea in aqueous solutions and soils. The method is formally compared with the reference procedure ISO 19746:2017 (HPLC). Calibration, based on the 1000–1200 and 1460–1670 cm⁻¹ windows, showed near-ideal linearity in the 0.25–25% w/w range (r² = 0.9999). LOD and LOQ values were 0.178 and 0.735% w/w, respectively. Intra- and inter-day accuracy proved adequate for routine use (RSD ≤ 5%). A one-way ANOVA (p = 0.983) confirmed no statistically significant differences between concentrations obtained by micro-Raman and ISO 19746:2017. In the soil matrix, recoveries ranged between 94 and 101, and the contained biases demonstrate good tolerance to matrix effects. Application to maize plots allowed for monitoring urea disappearance at three depths (0–2 cm, 5–7 cm and 10–15 cm) over 90 days. These differentiated areas of rapid surface hydrolysis from more persistent fractions at depth. The Eco-Scale (96), GAPI (pictogram dominated by green areas), and AGREE (0.88) metrics confirm a significantly lower environmental footprint than that of the chromatographic method. The proposed micro-Raman methodology is emerging as a green, fast, and traceable alternative for monitoring urea in fertilizers and agricultural soils. Full article

Anguilla japonica is a catadromous fish, and the Yangtze River Estuary serves as a crucial passage for A. japonica migrating downstream to the sea. A large number of adult A. japonica appear on the market during the peak migration period. Due to the lack of effective discrimination basis, it is difficult to distinguish the source of samples in market supervision. Therefore, there is an urgent need to trace the origin of A. japonica from different water bodies. This study analyzed muscle elemental fingerprints of 21 elements to determine the geographical origin of A. japonica. The results showed that A. japonica from different habitats had distinct elemental compositions in their muscles. Specifically, A. japonica from estuary waters (EW) was characterized by significantly higher levels of V and Hg compared to other water bodies. Na was identified as a key discriminant element among different habitats, with its content significantly increasing in river waters (RW), EW, and offshore waters (OW), respectively. Discriminant analysis selected four discriminant elements (V, Hg, Na and Cu) from 21 elemental compositions, among which V, Hg, and Na were the three key distinguishing elements. Based on the composition of these four discriminant elements in the muscles of A. japonica from different habitats, hierarchical cluster analysis (HCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and linear discriminant analysis (LDA) were applied and validated, successfully achieving rapid and accurate origin tracing and verification for new samples, achieving 100% classification accuracy. Therefore, the application of muscle EFA can achieve the geographical traceability of A. japonica from different habitats. The analytical method and verification process for origin tracing established in this study can be successfully applied to market supervision for tracing the origin of samples with unknown sources. Full article

(1) Background: In hospitals, mattresses are often relocated for cleaning or patient transfer, leading to mismatches between actual and recorded bed locations. Manual updates are time-consuming and error-prone, requiring an automatic localization system that is cost-effective and easy to deploy to ensure traceability and reduce nursing workload. (2) Purpose: This study presents a pragmatic, large-scale implementation and validation of a BLE-based localization system using RSSI measurements. The goal was to achieve reliable room-level identification of smart mattresses by leveraging existing hospital infrastructure. (3) Results: The system showed stable signals in the complex hospital environment, with a 12.04 dBm mean gap between primary and secondary rooms, accurately detecting mattress movements and restoring location confidence. Nurses reported easier operation, reduced manual checks, and improved accuracy, though occasional mismatches occurred when receivers were offline. (4) Conclusions: The RSSI-based system demonstrates a feasible and scalable model for real-world asset tracking. Future upgrades include receiver health monitoring, watchdog restarts, and enhanced user training to improve reliability and usability. (5) Method: RSSI–distance relationships were characterized under different partition conditions to determine parameters for room differentiation. To evaluate real-world scalability, a field validation involving 266 mattresses in 101 rooms over 42 h tested performance, along with relocation tests and nurse feedback. Full article

The cheese microbiome is a complex ecosystem strongly influenced by both technological practices and the processing environment. Moving beyond traditional cultured-based methods, the integration of shotgun metagenomics into cheese microbiology has enabled in-depth resolution of microbial communities at the species and strain levels. The aim of the present study was to review recent applications of shotgun metagenomics in cheese research, underscoring its role in tracking microbial dynamics during production and in discovering genes of technological importance. In addition, the review highlights how shotgun metagenomics enables the identification of key metabolic pathways, including amino acid catabolism, lipid metabolism, and citrate degradation, among others, which are central to flavor formation and ripening. Results of the discussed literature demonstrate how microbial composition, functional traits, and overall quality of cheese are determined by factors such as raw materials, the cheesemaking environment, and artisanal practices. Moreover, it highlights the analytical potentials of shotgun metagenomics, including metagenome-assembled genomes (MAGs) reconstruction, characterization of various genes contributing to flavor-related biosynthetic pathways, bacteriocin production, antimicrobial resistance, and virulence, as well as the identification of phages and CRISPR-Cas systems. These insights obtained are crucial for ensuring product’s authenticity, enabling traceability, and improving the assessment of safety and quality. Despite shotgun metagenomics’ advantages, there are still analytical restrictions concerning data handling and interpretation, which need to be addressed by importing standardization steps and moving towards integrating multi-omics approaches. Such strategies will lead to more accurate and reproducible results across studies and improved resolution of active ecosystems. Ultimately, shotgun metagenomics has shifted the field from descriptive surveys to a more detailed understanding of the underlying mechanisms shaping the overall quality and safety of cheese, thus bringing innovation in modern dairy microbiology. Full article

Blockchain technology has attracted considerable attention in the supply chain literature for its potential to enhance operational traceability, transparency, and trust, as well as to advance greening initiatives. Given current supply chain configurations, exploring barriers to implementation is a consequential agenda, and current studies have devoted substantial effort to identifying and offering guidance to address them. Despite recent findings, insights into how blockchain technology adoption can support green supply chain management are missing, particularly in the maritime sector, which receives limited attention. Thus, this work outlines a methodological approach to examine the suitability of maritime routes for addressing barriers to implementing blockchain technology in green supply chain management. Viewing the evaluation as a multi-criteria decision-making (MCDM) problem, the proposed approach performs the following actions on a case study evaluating four maritime lines. Firstly, from the 13 identified barriers in the literature review and expert interviews, nine relevant barriers were determined after one round of a Delphi process. These barriers eventually comprise the set of evaluation criteria. Secondly, to satisfy the assumption of criterion independence in most MCDM methods, this work proposes a novel extended grey relational analysis (GRA) that allows for the measurement of criterion independence based on the concept of grey relational space. Proposed here for the first time, the extended GRA offers a distribution-free overall independence index for each criterion based on pattern similarity. Finally, an integration of SWARA (Stepwise Weight Assessment Ratio Analysis) and ARAS (Additive Ratio Assessment) methods under Z-information is developed to address the evaluation problem involving expert judgments in a highly uncertain decision-making context. Results show that transaction-level uncertainty is the most critical barrier to blockchain adoption, followed by technology risks and higher sustainability costs. Among the four maritime lines, Line 3 is best prepared for a blockchain-enabled green supply chain. The agreement between these results and those of other MCDM methods is shown in the comparative analysis. Also, ranking remains unchanged even when the criteria weights are adjusted. The proposed approach provides a computationally efficient and tractable framework for maritime managers to make informed decisions about blockchain adoption to promote green supply chains. Full article

This study aimed to determine the degree of diversification in exports of fresh/chilled salmon and the level of international competitiveness of Norway, Sweden, Chile, and the United Kingdom over 2020–2024, using the Herfindahl–Hirschman Index (HHI) and the normalized revealed comparative advantage (NRCA). A quantitative, descriptive approach was adopted, drawing on annual Trade Map data for HS subheading 030214. HHI series were constructed by country–destination and NRCA series by country–market, and both were examined through univariate analysis. The findings showed that Norway exhibited low concentration levels and strong, stable advantages in Saudi Arabia, Türkiye, and Russia, whereas Sweden displayed moderate but rising concentration, supported by high advantages in Belgium, the United Kingdom, Germany, and Italy. In contrast, Chile and the United Kingdom recorded persistently high HHI values, with pronounced advantages concentrated in a limited number of markets (Brazil in Chile’s case; France and Chinese Taipei in the UK’s) and intra-product positions or comparative disadvantages in China, the United States, and Mexico. The study concludes that the combination of geographic diversification and positive NRCA enhances export resilience, while extreme specialization increases vulnerability to demand and regulatory shocks. It is recommended that Chile and the United Kingdom further develop diversification strategies toward markets where NRCA is neutral or negative, and that Norway and Sweden consolidate their advantages through investments in sustainability, traceability, and logistics. Further multivariate research incorporating macroeconomic and firm-level variables is also suggested. 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

Most current second-hand housing sales, contract signing, and other processes require the participation of intermediaries. However, suppose the intermediary refuses to disclose all information to the parties involved in the transactions. In that case, this traditional model can lead to weak supervision and punishment, adverse selection, moral hazards, and weak contract enforcement. Blockchain technology can not only secure the information intermediaries share, encouraging them to disclose information, but can also generate irreversible records of housing transactions for data traceability. Therefore, this study aims to develop a framework based on blockchain technology for the trading of second-hand housing. In this study, a second-hand housing online trading framework (SHHOTF) based on smart contract development is proposed for the second-hand housing business process, aiming to promote second-hand housing transactions. The contributions of this study lie in (1) determining the framework requirements, (2) proposing the functional module of a framework based on the blockchain and designing a complete business process, (3) developing an architecture for integrating blockchain and second-hand housing transaction processes, and developing technical components that support the framework functions, and (4) demonstrating the use case in Britain, analyzing the effectiveness and innovation of the framework. Furthermore, the framework demonstrated a 24% increase in transaction speed compared to the traditional Ethereum public network. The proposed process is highly adaptable within the current second-hand housing domain, and the developed framework can serve as a reference for introducing blockchain technology into other industries or application scenarios. Full article

Actigraphy quantifies human locomotor activity by measuring wrist acceleration via wearable devices at relatively high rates and converting it into lower-temporal-resolution activity values; however, the computational implementations of this data compression differ substantially across manufacturers. Building on our previous work comparing activity determination methods, we have investigated how they (e.g., digital filtering and data compression) influence nonparametric circadian rhythm analysis and sleep–wake scoring. In addition to our generalized actigraphic framework, we have also emulated the use of specific devices commonly employed in such sleep-related studies by applying their methods to raw actigraphic acceleration data we collected to demonstrate, through concrete real-life examples, how methodological choices may shape analytical outcomes. Additionally, we assessed whether nonparametric indicators could be derived directly from acceleration data without compressing them into activity values. Overall, our analysis revealed that all these analytical approaches to the sleep–wake cycle can be substantially affected by the manufacturer-dependent actigraphic methodology employed, with the observed effects traceable to distinct steps of the signal-processing pipeline, underscoring the necessity of cross-manufacturer harmonization from a clinically oriented perspective. Full article