The safety of the food we consume has a direct impact on individual and population health and affects the economic growth of the region where food safety is practised and enhanced. The central goal of the European Commission’s Food Safety policy is to ensure a high level of protection of human health covering the whole supply chain. In recent years, great attention has been paid to food testing and the application of metrological tools to support food safety. The global food market and national and international food safety regulations have created a huge demand for the measurement traceability and comparability of analytical results that are independent of time or space boundaries. This review provides an overview of the European food safety policy and regulation, with a focus on the measurement-related elements of the European Union (EU) food law. It also highlights how the application of analytical techniques, with particular reference to separation approaches, and metrological tools can ensure the control of certain contaminants that nowadays represent the main challenges for food safety (e.g., mycotoxins, nanoparticles, emerging and process contaminants). METROFOOD-RI-Infrastructure for promoting metrology in food and nutrition is therefore described in this context. This European research infrastructure has been developed and is being implemented in the frame of the European Strategy Forum on Research Infrastructures (ESFRI) to support metrology in food and nutrition and establish a strategy allowing reliable and comparable analytical measurements in food across the entire process line, from primary producers to consumers, and making data findable, accessible, interoperable, and reusable (FAIR).

Olives and olive products are particularly important for the national agroindustrial sector, for the aspects related to the production territory (authenticity), and for the link with the Mediterranean Diet. Several studies indicate that the elemental profile of olive and olive products depends on the production area in which the olive trees were grown, and the elemental content of the olives can be used as a marker of the production area. In order to confirm this hypothesis, the multi-elemental profile of olive drupes and olive leaves of eleven cultivars arising from two different production areas was evaluated through ICP-MS and ICP-AES techniques. In addition, some leaf samples were analysed by LPAS in order to evaluate the applicability of this new analytical technique for determining the geographic origin. The obtained results, combined with chemometric tools, showed the possibility of discriminating samples according to the production area on the basis of the elemental content, as well as by LPAS.

In recent years, the digital revolution has involved the agrifood sector. However, the use of the most recent technologies is still limited due to poor data management. The integration, organisation and optimised use of smart data provides the basis for intelligent systems, services, solutions and applications for food chain management. With the purpose of integrating data on food quality, safety, traceability, transparency and authenticity, an EOSC-compatible (European Open Science Cloud) traceability search engine concept for data standardisation, interoperability, knowledge extraction, and data reuse, was developed within the framework of the FNS-Cloud project (GA No. 863059). For the developed model, three specific food supply chains were examined (olive oil, milk, and fishery products) in order to collect, integrate, organise and make available data relating to each step of each chain. For every step of each chain, parameters of interest and parameters of influence—related to nutritional quality, food safety, transparency and authenticity—were identified together with their monitoring systems. The developed model can be very useful for all actors involved in the food supply chain, both to have a quick graphical visualisation of the entire supply chain and for searching, finding and re-using available food data and information.

Summary: Soil contamination due to toxic elements (TEs) and potentially toxic elements (PTEs) is a global environmental concern due to their toxicity in ecosystems and the potential risk of food chain contamination. This study aimed to examine the differences in the multielemental profile of two experimental fields in Central Italy and to assess the uptake and translocation of TEs (As, Cd, Cr, Ni, Pb, Ti) and PTEs (Co, Cu, V, Zn) in three durum wheat, Triticum turgidum L. subsp. durum varieties grown in organic (OFS) and conventional (CFS) farming systems. The biological absorption coefficient (BAC) and the translocation factor (TF) were evaluated to determine the absorption and translocation of elements from the soil to the aerial parts of the plants. The content of essential mineral nutrients such as Cu and Zn was higher in organically grown plants, whereas TEs and PTEs, such as Cd, Cr, Ni, and V, were more concentrated in conventionally cultivated plants. Among the three varieties, Simeto showed a greater ability to translocate Cd to the edible part (TF = 0.0341 for OFS and 0.7900 for CFS). Soil type, cultivation method, and durum wheat variety all affect the uptake and distribution of elements within the soil-plant system. Future research focusing on the edible parts could improve sustainable agricultural practices, optimise nutrient management, enhance crop quality, and reduce food security risks, ultimately supporting the development of safer farming systems that are resilient to climate and environmental changes.

The agrifood supply chain is undergoing a profound digital transformation driven by the adoption of advanced sensing technologies, data analytics, and interconnected information systems. While these innovations create unprecedented opportunities to improve food safety, traceability, authenticity, and sustainability, they also introduce major challenges related to data accuracy, data availability, and data privacy and ethical governance. In this evolving landscape, metrology provides the scientific framework needed to ensure that digital data are reliable, comparable, and fit for decision-making. This review examines the principal data challenges affecting digital agrifood systems and discusses how metrological principles-including traceability, calibration, validation, uncertainty evaluation, and standardised measurement protocols-can be extended beyond traditional laboratory measurements to emerging digital technologies such as IoT, remote sensing, blockchain, Digital Twins, and artificial intelligence. By integrating these principles with FAIR data practices, digital agrifood ecosystems can improve data interoperability, transparency, and long-term reusability while supporting trustworthy data sharing and responsible governance. The review also highlights how research infrastructures such as METROFOOD-RI and governance initiatives such as DRG4FOOD provide complementary examples of implementing these principles in practice by combining metrological rigor with ethical and privacy-aware data governance. Overall, this review proposes a metrological perspective for addressing emerging data challenges and demonstrates how the integration of measurement science, digital technologies, and responsible governance can support trustworthy digital agrifood ecosystems, evidence-based decision-making, and more sustainable and resilient food systems.

The digital transformation of agrifood systems demands an integrated infrastructure to ensure traceability, trust, and intelligent decision-making across complex and heterogeneous value chains. METROFOOD-IT, a large-scale national research infrastructure in food metrology aligned with the ESFRI METROFOOD-RI, addresses these challenges by combining advanced experimental facilities with a comprehensive digital ecosystem. This paper focuses on the IT kernel of METROFOOD-IT and presents an integrated architectural model that brings together four key technological paradigms: data acquisition through Internet of Things (IoT) and laboratory infrastructures, an Open Data Platform for interoperability and sharing, blockchain-based notarization for integrity and provenance, and Artificial Intelligence (AI) for knowledge extraction and decision support. Rather than describing these components in isolation, the paper abstracts from their implementation within the Italian National Recovery and Resilience Plan (NRRP) project METROFOOD-IT to distill a coherent and reusable architectural pattern in which data management, trust enforcement, and intelligent analytics are tightly coupled. Five explicit design principles are identified and articulated: federated data with centralized metadata, selective on-chain anchoring, user-unobtrusive trust infrastructure, explainability as a first-class architectural concern, and machine learning as the backbone of decision-making. Two empirical case studies—one centered on explainable AI for hyperspectral crop nitrogen assessment and the other on IoT-driven sustainable agriculture monitoring secured by distributed ledger technology—serve a dual role: they motivate and shape the architectural pattern, and they exemplify the operational regimes the resulting design supports. A reference deployment on the Ethereum Sepolia public test network, grounded on an IBM Power E1050 and IBM Storage Scale enterprise substrate, provides quantitative evidence for the proposed hybrid on-chain/off-chain pattern with streaming hash-only notarization. The architecture illustrates how research infrastructures can evolve into integrated digital platforms that enable transparent, verifiable, and scalable agrifood systems, and offers a foundation for generalizable design principles in data-intensive and trust-sensitive settings.