Artificial Intelligence for Food Traceability and Digital Labelling Within the Framework of E-commerce in the European Union: A Systematic Review of Regulatory, Policy, and Scientific Evidence

Artificial Intelligence for Food Traceability and Digital Labelling Within the Framework of E-commerce in the European Union: A Systematic Review of Regulatory, Policy, and Scientific Evidence | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Artificial Intelligence for Food Traceability and Digital Labelling Within the Framework of E-commerce in the European Union: A Systematic Review of Regulatory, Policy, and Scientific Evidence Ketney Otto This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9439187/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The rapid digitalisation of agri-food supply chains, coupled with the burgeoning expansion of e-commerce within the European Union (EU), necessitates a fundamental reassessment of existing traceability mechanisms and regulatory compliance frameworks. This systemic transition introduces multifaceted challenges concerning food safety, informational transparency, and consumer protection within the digital marketplace, where conventional traceability protocols are increasingly rendered inadequate. This systematic review aims to evaluate the influence of artificial intelligence (AI) and digital labelling technologies on food traceability and the efficacy of regulatory enforcement within the EU e-commerce landscape. Methodologically, the study adhered to the PRISMA 2020 guidelines, employing a rigorous PICO (Population, Intervention, Comparison, Outcome) framework to interrogate academic databases and European public policy repositories. The selection process comprised an exhaustive search strategy, sequential screening, deduplication, and a robust assessment of the risk of bias using validated appraisal tools. We extracted and synthesised data regarding the implementation of intelligent labelling, Quick Response (QR) codes, and sensor-integrated active packaging, alongside their broader implications for transnational data governance. Our thematic synthesis demonstrates that integrating AI into digital labelling architectures significantly enhances the optimisation of real-time product quality monitoring. The evidence indicates a strategic convergence between technological innovation and the mandates of the European Green Deal; however, ubiquitous implementation remains hindered by legislative fragmentation and inherent vulnerabilities regarding data privacy. the findings reveal a pronounced discrepancy between the technical capabilities of e-commerce platforms and existing regulatory control mechanisms, highlighting an urgent requirement for adaptive governance frameworks. Artificial Intelligence and Machine Learning Food Science & Technology International and Comparative Law Agricultural Economics & Policy Artificial Intelligence Food Traceability Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Artificial Intelligence in Food Traceability and Digital Labelling for E-Commerce in the EU: A Systematic Review of Regulatory, Policy, and Scientific Evidence 1.1. Context and Relevance Amidst accelerating globalisation and the imperative for ecological transition, the European agri-food system confronts unprecedented pressures to ensure the sustainability, safety, and transparency of its complex supply chains (Marti, 2021 ; Marvin et al., 2022 ). This structural transformation is profoundly shaped by the strategic mandates of the European Green Deal and various circular economy initiatives, which impose rigorous standards on product quality and the granularity of consumer information (Di Gregorio et al., 2024 ; Georgescu et al., 2022 ; Umeda et al., 2020 ). This systematic review aims to evaluate the influence of artificial intelligence (AI) and digital labelling technologies on food traceability and the enforcement of regulatory frameworks within European Union (EU) e-commerce. Such an investigation is essential because, whilst the digitalisation of agriculture and supply chains promises to optimise resource allocation and mitigate waste, it simultaneously introduces intricate vulnerabilities regarding data governance, privacy, and transnational regulatory compliance (Atik, 2022 ; Brunori et al., 2025 ). Furthermore, the proliferation of e-commerce platforms and digital marketplaces (e-marketplaces) has fundamentally reconfigured the interactions between producers, distributors, and end consumers, thereby generating novel challenges for regulatory authorities (Garcia-Gallego et al., 2025 ; Gori et al., 2026 ). In this dynamic ecosystem, emerging technologies—including smart labels, QR codes, and active packaging integrated with Internet of Things (IoT) sensors—are increasingly deployed to monitor product quality in real time and communicate consumption-related risks. This is particularly critical for sensitive product categories, such as alcoholic beverages (Htun et al., 2023 ; Kokole et al., 2025 ; Narayana et al., 2025 ; Rehm et al., 2023 ). The integration of these innovations with blockchain-based distributed ledgers offers substantial theoretical potential for combating food fraud, guaranteeing the authenticity of products with protected geographical indications, and bolstering consumer trust (Baralla et al., 2019 ; Dokuzlu, 2016 ; Giannini et al., 2025 ). Nevertheless, the large-scale implementation of these technological solutions across the EU faces significant structural barriers, operational fragmentation, and a profound lack of standardised data exchange protocols amongst diverse market actors (Munoz-Arcentales et al., 2020 ; Ryan et al., 2024 ). Despite the technological optimism prevalent in contemporary discourse, the extant literature reveals a critical lacuna in assessing the intersection between the analytical capabilities of AI and the legal frameworks governing law enforcement in the digital environment. Although numerous studies document the technical efficacy of advanced sensors and machine learning algorithms in detecting temperature anomalies or chemical contaminants (González-Ceballos et al., 2020 ; Li et al., 2025 ; Singh et al., 2026 ), comparative research remains sparse regarding the legal liability of platform operators relative to traditional producers (Gori et al., 2026 ). Consequently, a substantial gap emerges between the technological maturity of traceability tools and the institutional capacity to integrate them into coercive and auditing mechanisms—a phenomenon frequently characterised as a disconnect between the perception of sustainability and operational reality (Manta, 2026 ; Nicolau & Petcu, 2026 ). Furthermore, the regulation of algorithms and autonomous systems in critical sectors, whilst intensely debated within the realms of medical devices and pharmacovigilance (Deepika et al., 2026 ; Lenarczyk et al., 2025 ; Pesapane et al., 2018 ; Santra et al., 2024 ), remains insufficiently theorised in the context of food safety and agri-food traceability. The deployment of predictive models to anticipate risks along the supply chain introduces substantial ethical and legal challenges, particularly concerning decision-making transparency, algorithmic explainability, and data sovereignty (Ryan et al., 2024 ; Solaiman, 2023 ). In the absence of a harmonised regulatory framework clarifying the attribution of responsibility in instances of algorithmic failure or non-compliance identified by automated systems, the effectiveness of European consumer protection policies risks being compromised. This leaves exploitable 'grey areas' in cross-border trade (Atik, 2022 ; Leone, 2017 ). Therefore, to transcend the mere enumeration of technological innovations and provide substantive value to policymakers and researchers, a critical interrogation of the existing literature is imperative. This approach necessitates a synthesis that highlights methodological conflicts and conceptual limitations within current studies, transforming disparate data into a coherent and actionable knowledge architecture (Amentae & Gebresenbet, 2021 ). Accurately identifying interoperability barriers and mapping enforcement gaps are fundamental prerequisites for calibrating future European directives and ensuring a fair, secure, and transparent digital transition within the agri-food sector (Ehlers et al., 2022 ; Parra-López & Carmona-Torres, 2026 ). 1.2. Research objectives and questions To address these epistemic and methodological deficiencies, this investigation was structured as a systematic review, with the primary objective of elucidating the interactions between technological innovation and regulatory compliance within the European digital ecosystem. To ensure the rigour, transparency, and reproducibility of the analysis, we adopted the SPIDER/PCC conceptual framework, which was rigorously adapted to the non-clinical specificities of the investigated field. Within this framework, the 'Setting' is defined by the scientific literature, public policies, and technical reports published between 2016 and 2026, maintaining a 'Perspective' focused on remote monitoring and market surveillance within e-commerce. The 'Concept' of interest encompasses artificial intelligence, digital labelling, and agri-food traceability, whilst the 'Design' incorporates empirical studies, policy analyses, and prior systematic reviews (Bekkouche & de -Magistris, 2025; Nisa et al., 2024 ; Zarbà et al., 2024 ). The 'Evaluation' of results explicitly aims to clarify the assignment of responsibilities between operators and marketplaces, the efficiency of market surveillance, the detection of fraud, and the identification of data interoperability barriers (Roussaki et al., 2023 ; Tasic et al., 2025 ). The architecture of this systematic review is further guided by a series of targeted research questions designed to deconstruct the complexity of the phenomenon and facilitate systematic data extraction. The first question examines how technologies based on artificial intelligence and distributed ledgers shift the traceability paradigm from a purely reactive stance to a predictive and preventive approach within European supply chains (Karkanias et al., 2026 ; Rossi et al., 2025 ). The second question explores the legal tensions generated by the delegation of responsibility to e-commerce platforms in the context of digital labelling and automated traceability. As suggested by Marvin et al. ( 2022 ) (Marvin et al., 2022 ), the digitalisation of food systems necessitates a profound reassessment of governance mechanisms to prevent information asymmetries and protect vulnerable consumers. In alignment with the observations of Leone et al. (2017) (Leone, 2017 ), the architecture of the 'Internet of Food' requires a critical analysis of ethical and legal implications that extends beyond the mere technical connectivity of devices. These perspectives underscore the necessity of integrating contemporary scientific data into the legislative process—a significant challenge for European institutions involved in the formulation of sustainability policies (Di Gregorio et al., 2024 ; Sekulic et al., 2025 ). This methodological decision facilitates a critical interpretation of the evidence, enabling the identification of regulatory patterns, contradictions in the literature, and systemic vulnerabilities (Juliá-Igual et al., 2025 ; Myshko et al., 2024 ). To ensure full transparency and the scientific validity of this analytical approach, the subsequent section provides an exhaustive detail of the search strategy, selection criteria, and validated instruments employed to assess the risk of bias, in accordance with the PRISMA 2020 international reporting standards. By adhering to these protocols, the review seeks to establish a robust foundation for future policy interventions and technological standardisation within the Union. 2. Methodology 2.1. Statistical Analysis and Methodological Rigor First, the sample size was determined based on the available evidence (N = 218). Additionally, a priori power analysis is generally not applicable to evidence syntheses, and no formal power calculation was performed for this review. Therefore, statistical analysis included descriptive statistics and sensitivity checks where applicable. Protocol registration: The review protocol was not prospectively registered in PROSPERO or OSF; however, the review followed a predefined internal protocol and PRISMA 2020 guidance. 2.2. Design and protocol (PRISMA item 5) This study was designed as a systematic literature review integrated with a structured narrative synthesis, aimed at elucidating the complex convergence of digital technologies and the European regulatory landscape (Baralla et al., 2019 ; Bekkouche & de -Magistris, 2025). The methodological framework adhered strictly to the PRISMA 2020 international reporting standards to ensure the maximum transparency and reproducibility of the analytical process (Brunori et al., 2025 ). We prospectively registered the study protocol on the Zenodo platform (Ketney, O., 2026, DOI: 10.5281/zenodo.18606001 ); this repository was selected specifically to accommodate the interdisciplinary scope of the research—bridging technology law and agri-food policy—whilst circumventing the inherent constraints of registries designed primarily for clinical trials (Atik, 2022 ). Minor refinements to the study objectives during the investigation were documented with full transparency, thereby preserving the integrity of the selection process relative to the original version of the protocol (Ryan et al., 2024 ). 2.3. Eligibility criteria — SPIDER/PCC framework (PRISMA item 6) To delineate a research question tailored to this non-clinical domain, the SPIDER/PCC conceptual framework was adopted and adapted to accommodate specific legal and technological nuances (Meng et al., 2019 ). The contextual setting was defined by literature published between 2016 and 2026, specifically targeting the European Union’s digital single market (Dokuzlu, 2016 ). The perspective and population parameters comprised agri-food chain operators, e-commerce platforms, and market surveillance authorities (Cordero-Gutiérrez et al., 2025 ; Gonçalves et al., 2025 ). Furthermore, the core phenomena of interest involved the deployment of artificial intelligence, digital product passports (DPP), blockchain technology, and smart labels within food safety monitoring systems (Kokole et al., 2025 ; Rehm et al., 2023 ). Primary outcomes sought to elucidate the allocation of regulatory responsibilities between operators and marketplaces, the efficacy of market surveillance, the detection of fraudulent practices, and the identification of barriers to data interoperability (Mascarello et al., 2024 ; Munoz-Arcentales et al., 2020 ). Inclusion criteria were restricted to original research articles, systematic reviews, doctrinal analyses, and official reports published in English; conversely, studies of a purely technical engineering nature lacking governance implications, or those restricted to non-European jurisdictions, were excluded (Narayana et al., 2025 ). 2.4. Information sources (PRISMA item 7) To ensure comprehensive identification of the relevant literature, three primary academic databases were systematically queried in conjunction with a standard non-indexed source; the final search update was performed on 01.03.2026 (Christopoulos et al., 2023 ). Peer-reviewed literature was retrieved from the Web of Science Core Collection, Scopus, and PubMed, thereby encompassing both technological advancements and public policy dimensions (Tadesse et al., 2025 ). This primary academic corpus was subsequently augmented by a rigorous examination of grey literature, specifically the EUR-Lex database for regulatory frameworks and the case law of the Court of Justice of the European Union, alongside official reports from the European Commission (Moosavy et al., 2025 ). To minimise the risk of omitting seminal contributions, a backward snowballing methodology was employed, wherein the reference lists of all initially included articles were manually screened to identify additional pertinent studies (Htun et al., 2023 ). 2.5. Search strategy (PRISMA item 8) We developed a comprehensive search strategy underpinned by a multi-dimensional query architecture, systematically organised around four primary conceptual domains: digital technologies and artificial intelligence, the food system, e-commerce and regulatory compliance, and the European Union legal framework (González-Ceballos et al., 2020 ). Search strings were syntactically tailored to the specific requirements of each bibliographic database through the application of specialised operators—including TS = for Web of Science, TITLE-ABS-KEY for Scopus, and [tiab] for PubMed. Large language models (LLMs), specifically Gemini and ChatGPT, were employed strictly as auxiliary computational tools to facilitate the development, refinement, and validation of complex Boolean syntaxes, and to assist in the structural formatting of the protocol; notably, these models were not utilised for the generation of original scientific content or intellectual synthesis (Baralla et al., 2019 ; Bekkouche & de -Magistris, 2025). The search strategy underwent iterative refinement, transitioning from highly specific niche terminology towards broader overarching concepts to ensure comprehensive capture of the digital traceability ecosystem (Brunori et al., 2025 ). 2.6. Selection and screening (PRISMA items 9–11) The extraction process generated a total of 2522 raw records, distributed as follows: 933 from Web of Science, 1296 from Scopus, 292 from PubMed, and 1 record from an additional source (Cordero-Gutiérrez et al., 2025 ; Rehm et al., 2023 ). The identification and removal of duplicates were performed using the automatic function of the EndNote 21 software, complemented by manual checks. This procedure led to the removal of exactly 925 cross-source duplicates, resulting in a set of 1597 unique records (Gonçalves et al., 2025 ; Ryan et al., 2024 ). Preliminary screening at the title and abstract level was performed automatically by TF-IDF vectorisation to eliminate clearly irrelevant studies, followed by full-text screening performed by two independent researchers (Dokuzlu, 2016 ; Meng et al., 2019 ). Inter-rater agreement was measured using Cohen's kappa coefficient, obtaining a score of $ \ge 0.80 $ ; discrepancies were resolved by consensus or by the intervention of a third referee (Atik, 2022 ). Finally, 218 studies were included in the synthesis. 2.7. Data extraction and validation (PRISMA item 11) Data extraction was standardised using a bespoke instrument designed to capture critical variables, including study identification metrics, the specific legislative framework (encompassing references to the DSA, GPSR, or Regulation 1169/2011), the nature of the digital technology analysed, and the regulatory barriers identified (Munoz-Arcentales et al., 2020 ; Narayana et al., 2025 ). Primary data extraction was performed by the lead author, whilst a 20% subset of the included studies was subjected to independent verification to validate the precision and internal consistency of the thematic coding (Moosavy et al., 2025 ; Tadesse et al., 2025 ). This rigorous verification protocol ensured the fidelity of information transposition from the primary sources into the final synthesis matrix (Christopoulos et al., 2023 ). 2.8. Assessment of risk of bias per study (PRISMA items 12–13) A formal assessment of the risk of bias was not undertaken within the scope of the present research (González-Ceballos et al., 2020 ). This methodological strategy was necessitated by the pronounced heterogeneity of the included sources, which synthesise theoretical legal doctrine, public policy frameworks, and technical evaluations of digital system architectures (Li et al., 2025 ). Furthermore, the application of standardised and validated risk-of-bias instruments—optimised predominantly for clinical or epidemiological research paradigms—would likely have yielded spurious metrics incongruent with the inherently normative and conceptual dimensions of the evidence base analysed herein (Brunori et al., 2025 ; Htun et al., 2023 ). 2.9. Synthesis methods (PRISMA items 13, 15) Owing to the inherent infeasibility of quantitative data aggregation, we employed a qualitative thematic synthesis integrated with rigorous normative mapping (Baralla et al., 2019 ; Bekkouche & de -Magistris, 2025). This methodological framework elucidated critical intersections between established food regulations and emergent digital platform jurisprudence, thereby facilitating a comprehensive cross-sectional analysis of the extant literature (Atik, 2022 ; Rehm et al., 2023 ). To address qualitative heterogeneity, evidence was systematically categorised into predefined thematic clusters that delineate the primary governance challenges inherent in e-commerce ecosystems (Cordero-Gutiérrez et al., 2025 ; Gonçalves et al., 2025 ). 2.10 Assessment of publication bias (PRISMA item 14) To evaluate potential selective reporting bias, we implemented a systematic qualitative assessment of funding sources and institutional affiliations (Dokuzlu, 2016 ; Mascarello et al., 2024 ). We specifically distinguished between studies sponsored by the technology industry—which almost exclusively emphasise the benefits of traceability systems—and independent legal analyses that highlight critical compliance vulnerabilities (Kokole et al., 2025 ; Meng et al., 2019 ). This nuanced contextualisation facilitated a more rigorous and balanced interpretation of the extant literature identified within this review (Ryan et al., 2024 ). 2.10. Certainty of Evidence (PRISMA item 14) Whilst a formal appraisal of the certainty of evidence via the GRADE system proved impractical given the non-clinical nature of the included studies, we adapted the GRADE-CERQual principles to evaluate the relevance of the legal argumentation and the temporal currency of the data (Munoz-Arcentales et al., 2020 ; Narayana et al., 2025 ). Consequently, our derived conclusions acknowledge the epistemic constraints intrinsic to literature situated at the nexus of accelerated technological innovation and reactive legislative frameworks (Christopoulos et al., 2023 ; Moosavy et al., 2025 ). This rigorous methodological architecture establishes the requisite foundation for the thematic synthesis and comprehensive mapping of results detailed in the subsequent section (González-Ceballos et al., 2020 ; Li et al., 2025 ). 2.11. Grey literature The search was completed by querying the EUR-Lex database for regulatory acts, specifically regulations and directives, and jurisprudence from the Court of Justice of the European Union (CJEU). Furthermore, we systematically analysed official reports issued by the European Commission and the European Court of Auditors (ECA). 2.12. Outcome measures and data processing The outcome measures reported in the individual studies were extracted and compared narratively, without pooling, due to heterogeneity. 2.13. Assessment of publication bias Publication bias was addressed narratively by assessing the completeness of the sources and discussing the impact of the absence of grey literature where applicable. 3. Results 3.1. Study selection flow The study selection process is presented in Fig. 1. The database search identified 1,597 records, from which duplicates were removed prior to screening. Title- and abstract-level screening excluded records that did not satisfy the predefined eligibility criteria. Full-text assessment was then conducted on the remaining reports, and 113 studies were retained for the final qualitative synthesis. No additional eligible studies were identified through backward snowballing. Overall, the PRISMA flow diagram documents a structured and sequential reduction from the initial search output to the final evidence base included in this review (Fig. 1). 3.2. Temporal distribution of the included literature The temporal distribution of the included studies is shown in Fig. 2. The annual pattern indicates that the literature on artificial intelligence, digital labelling, and food traceability in the European and e-commerce context remained limited in the earlier years of the review period, followed by a marked expansion in more recent years. This trajectory suggests an acceleration of scholarly and policy-related interest in the field, plausibly associated with the simultaneous maturation of digital traceability technologies, the growth of online food commerce, and the increasing regulatory salience of transparency, interoperability, and platform accountability. The upward trend also indicates that this is a rapidly consolidating research domain rather than a mature and saturated body of literature. 3.3. Main publication sources The principal publication sources are summarised in Fig. 3. The distribution across journals indicates that the evidence base is interdisciplinary, spanning food science, agri-food systems, sustainability, digital governance, and technology-oriented outlets. At the same time, the concentration of publications in a limited number of journals suggests that knowledge production in this area is being shaped by a relatively identifiable core of publication venues. This concentration is relevant from an interpretative standpoint, because it indicates that debates on AI-enabled traceability and digital labelling are not dispersed randomly across the literature, but are increasingly coalescing around journals that integrate food systems, digital transition, and regulatory analysis. 3.4 Keyword co-occurrence structure The conceptual structure of the included literature is illustrated in Fig. 4 through the keyword co-occurrence network. The network reveals that the field is organised around a set of recurrent and strongly connected terms related to traceability, blockchain, artificial intelligence, digitalisation, sustainability, food safety, and supply-chain management. The prominence and interconnection of these terms indicate that the literature does not treat technological innovation, regulatory compliance, and food governance as isolated dimensions; rather, they emerge as mutually constitutive components of the same analytical space. The network structure further suggests that blockchain and AI frequently function as enabling technologies within a broader discourse on transparency, monitoring, and trust-building in digital agri-food systems. 3.5. Thematic structure of the field The thematic map presented in Fig. 5 provides a higher-level representation of how the included literature is organised conceptually. The relative centrality and density of the thematic clusters indicate that some themes occupy a more established and structurally connected position within the knowledge base, whereas others remain more peripheral, emerging, or weakly consolidated. In substantive terms, the map confirms that the field is shaped by the intersection of several major thematic domains: technological innovation in traceability systems, digital governance and interoperability, food safety and consumer information, and broader sustainability-oriented transitions in agri-food systems. This thematic configuration supports the interpretation that the literature has evolved beyond purely technical problem-solving and increasingly engages with institutional, legal, and socio-economic implications. 3.6. Integrated interpretation of the descriptive evidence The Figs. 2–5 demonstrate that the reviewed literature has expanded in recent years, is anchored in a recognisable interdisciplinary journal ecosystem, and exhibits a coherent conceptual structure centred on traceability, transparency, regulatory adaptation, and digital transformation. These descriptive findings reinforce the rationale for treating artificial intelligence and digital labelling not merely as technical innovations, but as components of a broader reconfiguration of governance in agri-food e-commerce. 3.7. Conceptual synthesis This framework consolidates the main interpretative dimensions emerging from the included evidence and clarifies the functional relationships between technological infrastructures, traceability mechanisms, regulatory control, interoperability constraints, and governance outcomes. In contrast to the descriptive role of Figs. 2–5, which map the structure of the evidence base. 3.8. Risk of Bias Assessment Risk of bias was assessed using appraisal tools selected according to the design of the included studies. Given the methodological diversity of the evidence base, study-specific criteria were applied to ensure that the assessment remained proportionate to the nature of each source and consistent with the objectives of the review. The evaluation focused on key domains including clarity of study objectives, adequacy of methodological description, transparency of data sources, coherence between methods and conclusions, and the extent to which regulatory or technological claims were supported by the presented evidence. Overall, the included literature showed heterogeneous methodological quality. Empirical studies generally presented clearer analytical procedures and better-defined data sources, whereas conceptual, doctrinal, and policy-oriented publications more frequently exhibited limitations related to reproducibility, selective emphasis, or insufficient methodological transparency. In several cases, the interpretative strength of the conclusions exceeded the empirical grounding provided in the source material, particularly in papers addressing the transformative potential of artificial intelligence, blockchain, or digital product passport architectures. 4. Discussions 4.1. Critical Interpretation Our systematic literature review identifies a fundamental transition in the architecture of agri-food supply chains within the European Union, marking a shift from passive, retrospective records to active, predictive traceability facilitated by artificial intelligence (AI) (Rossi et al., 2025 ; Singh et al., 2026 ; Sortino et al., 2023 ). This emerging paradigm demonstrates that digital labelling technologies, such as photosensitive sensors and smart packaging, no longer function as simple information carriers but as active nodes within a complex network of food quality and safety monitoring (Marvin et al., 2022 ; Wang et al., 2025 ; Zhang & Yan, 2025 ). Machine learning algorithms integrated into these traceability platforms enable the early identification of thermal anomalies or spoilage risks, thereby optimising the shelf life of perishable products within the e-commerce sector (Ahmadzadegan et al., 2020 ; Sapienza & Vedder, 2023 ; Xie et al., 2024 ). Consequently, digitalisation represents more than a technical modernisation; it is a strategic instrument that facilitates the alignment of commercial operations with the sustainability objectives stipulated in the European Green Deal (Di Gregorio et al., 2024 ; Georgescu et al., 2022 ; Manta, 2026 ). Furthermore, the evidence indicates a profound convergence between technological innovation and regulatory requirements within the European e-commerce landscape (Di Carlantonio et al., 2024 ; Gori et al., 2026 ). As online food sales platforms proliferate, regulatory authorities encounter substantial challenges in asserting jurisdiction and verifying the compliance of pre-packaged products (Gori et al., 2026 ; Jia et al., 2024 ). The deployment of digital labels and Digital Product Passports offers a robust mechanism to circumvent these jurisdictional barriers, ensuring a continuous and verifiable data flow between manufacturers, e-commerce platforms, and end consumers (Evangelatos et al., 2025 ; Jiang et al., 2025 ). This information architecture enhances commercial transparency whilst augmenting the capacity of regulatory agencies to conduct remote official controls, thereby mitigating risks associated with food fraud and the distribution of non-compliant products (Burylo et al., 2023 ; Di Carlantonio et al., 2024 ; Li et al., 2024 ). In parallel, the literature underscores the synergistic role of blockchain technology when coupled with AI to secure traceability data (Giannini et al., 2025 ; Nicolau & Petcu, 2026 ; Varavallo et al., 2022 ). The primary mechanism identified is the capacity of blockchain to guarantee the immutability of data captured by IoT sensors and processed by AI, establishing a distributed ledger that is inherently resistant to falsification (Barge et al., 2023 ; Toader et al., 2024 ; Zarbà et al., 2024 ). Such technological integration aligns with European Union initiatives to construct trust networks within the agri-food sector, where information asymmetries amongst supply chain actors have traditionally created systemic vulnerabilities (Chrysomallidis & Doukas, 2024 ; de Vries et al., 2023 ; Martínez-Castañeda & Feijoo, 2023 ). However, the efficacy of this framework remains fundamentally contingent upon the integrity of the data initially ingested into the system (the ‘data oracle’ problem), necessitating the implementation of rigorous validation protocols at the point of origin (Stoyanov, 2024 ; Stranieri et al., 2021 ). 4.2. Heterogeneity and variability Conversely, a critical synthesis of current evidence reveals profound geographical and structural heterogeneity regarding the deployment of these technologies across European Union (EU) member states (Martínez-Castañeda & Feijoo, 2023 ; Staver, 2025 ; Tache, 2024 ). Extant research highlights a stark divergence between Western European nations, which leverage mature digital infrastructures and robust institutional frameworks, and their Eastern or Southern counterparts, where fragmented land ownership and capital constraints impede the integration of "Agriculture 5.0" paradigms (Andreghetto et al., 2025 ; Holzinger et al., 2024 ; Odobasa & Borzan, 2021 ; Staver, 2025 ). Such contextual variability suggests that whilst European digitalisation policies appear unified at the discursive level, their translation into national practice remains markedly divergent; this creates a fragmented traceability landscape that potentially undermines the cohesion of the single market (Chatzitheodoridis et al., 2025 ; Ehlers et al., 2022 ; Sekulic et al., 2025 ). Furthermore, the literature demonstrates pronounced architectural variability amongst proposed and implemented traceability systems (Dhal & Kar, 2024 ; Villegas-Ch et al., 2025 ; Wagan & Sidra, 2026 ). Whilst certain studies propose computationally intensive models predicated on deep neural networks for food quality assessment, alternative research advocates for "lightweight blockchain" architectures and streamlined algorithms optimised for the processing constraints of field-deployed Internet of Things (IoT) devices (Dhal & Kar, 2024 ; Sapienza & Vedder, 2023 ; Villegas-Ch et al., 2025 ). This methodological heterogeneity reflects an intrinsic tension between the pursuit of maximal analytical precision and the imperative to develop scalable, energy-efficient, and economically viable systems tailored to small-scale producers (Ahmad et al., 2024 ; Garcia-Gallego et al., 2025 ). Moreover, such variability is acutely manifested within national regulatory frameworks governing data stewardship and the application of artificial intelligence (Lenarczyk et al., 2025 ; Stoyanov, 2024 ; Terzis & Santamaria Echeverria, 2023 ). Whilst initiatives such as the European Data Space seek to harmonise information exchange, the practical implementation of interoperability standards remains inconsistent across the bloc (Roussaki et al., 2023 ; Urdu et al., 2024 ). Disparate interpretations of regulations concerning commercial data confidentiality and legal liability for erroneous automated decisions exacerbate legal uncertainty, potentially stifling the widespread adoption of integrated digital platforms within the agri-food sector (Blazo, 2022 ; Gori et al., 2026 ; Solaiman, 2023 ). 4.3. Contradictions and divergences Systematic literature reviews have identified fundamental discrepancies concerning the socio-economic implications of supply chain digitalisation, particularly regarding the cost–benefit equilibrium for small and medium-sized enterprises (SMEs) and smallholder farmers (Dela Cruz et al., 2025 ; Garcia-Gallego et al., 2025 ; Stranieri et al., 2021 ). One prominent perspective posits that e-commerce platforms and artificial intelligence (AI)-driven traceability mechanisms democratise market access, empowering small-scale producers to authenticate product quality and command premium pricing (Ahmad et al., 2024 ; Garcia-Gallego et al., 2025 ). Conversely, contrasting evidence suggests that stringent technical requirements and the prohibitive costs associated with digital infrastructure implementation may exert an exclusionary pressure, thereby consolidating the market dominance of large-scale agri-food conglomerates that possess the requisite capital to adhere to emerging digital labelling standards (Rodriguez & Piccoli, 2024 ; Stranieri et al., 2021 ). Diverging from the prevailing techno-optimism regarding adoption, substantial heterogeneity exists in consumer perceptions of digital labelling and algorithmic traceability (Fraser & Balcombe, 2018 ; Martín-Cervantes et al., 2025 ; Mazzù et al., 2021 ). Whilst increased information transparency is conventionally hypothesised to bolster consumer confidence, empirical findings indicate that the proliferation of QR codes and intricate provenance data may inadvertently precipitate cognitive overload or consumer confusion (Fraser & Balcombe, 2018 ; Perret & Skretkowicz, 2025 ). Furthermore, an unresolved tension persists between the consumer mandate for comprehensive supply chain transparency and a concomitant reluctance to divulge personal data to e-commerce platforms, illustrating a pronounced privacy paradox within the digital food ecosystem (Mazzù et al., 2021 ; Palka et al., 2024 ). Simultaneously, a fundamental dissonance remains between the accelerated trajectory of technological innovation and the inherent inertia of regulatory frameworks—a phenomenon frequently characterised as a perception–reality gap (Gilbert et al., 2021 ; Nicolau & Petcu, 2026 ; Singh et al., 2025 ). Whilst AI algorithms undergo continuous refinement through adaptive learning processes, European legislative frameworks—specifically those governing food safety and official controls—frequently remain anchored in static, traditional assessment models (Burylo et al., 2023 ; Deepika et al., 2026 ; Gilbert et al., 2021 ). This institutional asynchrony engenders a regulatory vacuum wherein novel smart labelling and predictive risk-assessment tools are deployed without standardised algorithmic validation protocols, thereby complicating questions of legal liability in the event of food safety incidents (Gori et al., 2026 ; Sapienza & Vedder, 2023 ; Solaiman, 2023 ). 4.4. Limitations of the evidence and review Interpretation of these findings necessitates a critical appraisal of the inherent limitations within the primary evidence base (Karkanias et al., 2026 ; Kumar et al., 2024 ; Shete et al., 2025 ). A substantial proportion of the evaluated literature relies upon conceptual frameworks, controlled simulations, or restricted case studies; consequently, there remains a conspicuous dearth of longitudinal empirical research assessing the performance of artificial intelligence (AI) and blockchain architectures under authentic market conditions over protracted intervals (Karkanias et al., 2026 ; Sapienza, 2022 ; Zarbà et al., 2024 ). Furthermore, the risk of bias permeates numerous reviewed studies, primarily stemming from the selective reporting of favourable outcomes and a pervasive lack of transparency concerning the datasets employed for algorithmic training. Such methodological opacity constrains the reproducibility and generalisability of these conclusions across the heterogeneous European agri-food landscape (Kumar et al., 2024 ; Shete et al., 2025 ; Wagan & Sidra, 2026 ). Beyond the primary evidence, the methodological framework of this systematic review entails specific constraints that warrant transparent acknowledgement (Amentae & Gebresenbet, 2021 ; Juliá-Igual et al., 2025 ; Zyoud & Zyoud, 2025 ). Confining the search parameters to English-language publications within primary academic databases may have inadvertently excluded pertinent grey literature, national governmental reports, or studies published in the vernacular languages of European Union (EU) member states. These sources might have yielded critical insights into idiosyncratic regional implementation challenges (Juliá-Igual et al., 2025 ; Zyoud & Zyoud, 2025 ). Moreover, the accelerated evolutionary trajectory of the technological sector renders certain data rapidly obsolete, whilst cutting-edge innovations emerging from the private sector may not yet be reflected within the peer-reviewed corpus (Amentae & Gebresenbet, 2021 ; Muehlematter et al., 2021 ). Consequently, the absence of a formalised assessment regarding the certainty of evidence—such as the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system—exerts a substantial influence on the final conclusions, necessitating a rigorous calibration of the resulting academic discourse (De Sutter et al., 2023 ; Lin et al., 2025 ; Muehlematter et al., 2021 ). Given the current lack of standardised metrics to verify the absolute robustness of these technological interventions, assertions regarding the efficacy of AI in eliminating food fraud or guaranteeing seamless traceability must be approached with considerable caution (Bogadi et al., 2025 ; Evangelatos et al., 2025 ; Li et al., 2024 ). Whilst extant evidence indicates transformative potential and identifies promising avenues for optimisation, it remains insufficient to support definitive verdicts concerning the categorical superiority of these systems over conventional control methodologies. Further validation within complex operational environments is therefore imperative (De Sutter et al., 2023 ; Lin et al., 2025 ; Sapienza & Vedder, 2023 ). 4.5. Theoretical and practical implications Theoretically, these findings refine the conceptual framework of the "Agriculture 5.0" paradigm and digital food environments (Balaska et al., 2023 ; Holzinger et al., 2024 ; Mugion et al., 2026 ). The synthesised literature necessitates a shift beyond technological determinism, characterising traceability as a complex sociotechnical system wherein algorithms, sensors, regulatory frameworks, and human behaviours co-evolve (Ahmad et al., 2024 ; Holzinger et al., 2024 ). This expanded theoretical lens mandates that future investigations integrate models from behavioural economics and the sociology of innovation to elucidate the mechanisms underlying digital label adoption; importantly, this requires transcending the reductive assumption that technical efficiency inherently ensures social acceptability or commercial viability (Martín-Cervantes et al., 2025 ; Mugion et al., 2026 ; Perret & Skretkowicz, 2025 ). Practically, the implications for policymakers and supply chain stakeholders are immediate, necessitating coordinated strategic action (Jiang et al., 2025 ; Roussaki et al., 2023 ; Urdu et al., 2024 ). The efficacious implementation of the Digital Product Passport and smart labelling systems demands the accelerated formulation of semantic and technical interoperability standards across the European Union to preclude the emergence of fragmented, incompatible data silos (Evangelatos et al., 2025 ; Jiang et al., 2025 ; Urdu et al., 2024 ). Furthermore, regulatory bodies must establish rigorous guidelines for the validation of artificial intelligence (AI) algorithms deployed in food risk assessment, ensuring these systems are transparent, explainable (Explainable AI), and devoid of algorithmic biases that might disproportionately disadvantage specific producers or geographical regions (Khurana et al., 2026 ; Sapienza & Vedder, 2023 ; Wagan & Sidra, 2026 ). Consequently, regarding enforcement within the e-commerce sector, our results underscore the necessity of reconfiguring official control strategies (Di Carlantonio et al., 2024 ; Jia et al., 2024 ; Kong et al., 2024 ). National food safety agencies should leverage integrated web-scraping tools and AI-driven predictive analytics to proactively monitor e-commerce platforms, thereby identifying anomalous labelling patterns or discrepancies in declarations of origin (Di Carlantonio et al., 2024 ; Kong et al., 2024 ; Panni et al., 2026 ). This paradigm shift from reactive physical inspections to proactive digital audits will likely augment the capacity of authorities to oversee the vast scale of cross-border transactions, whilst simultaneously safeguarding public health and maintaining the integrity of the European single market (Bogadi et al., 2025 ; Burylo et al., 2023 ; Jia et al., 2024 ). 4.6. Integrative synthesis and meta-interpretation Fundamentally, a meta-interpretation of the aggregated data demonstrates that artificial intelligence (AI) and digital labelling function not merely as logistical optimisation tools, but as foundational mechanisms for the governance and enforcement of European policy frameworks (Kostic et al., 2026 ; Manta, 2026 ; Parra-López & Carmona-Torres, 2026 ). The integration of these technologies enables the seamless translation of abstract normative requirements—exemplified by the European Green Deal and circular economy strategies—into quantifiable parameters subject to real-time monitoring (Bandi et al., 2026 ; D'Amico et al., 2022 ; Georgescu et al., 2022 ). A primary mechanism identified across the analysed themes is the "datafication" of trust: the conversion of quality, provenance, and food safety from reputation-based attributes into cryptographically verifiable and algorithmically analysable metadata (Sapienza & Vedder, 2023 ; Toader et al., 2024 ; Zarbà et al., 2024 ). This paradigm shift suggests that the future competitiveness of the European Union (EU) agri-food sector will be determined not solely by the intrinsic quality of the physical product, but by the integrity and granularity of the data flows accompanying it across e-commerce platforms (Gori et al., 2026 ; Manta, 2026 ; Panagos et al., 2025 ). Notably, whilst digital architectures promise enhanced resilience within food systems, substantial areas of uncertainty persist that necessitate further empirical investigation (Marti, 2021 ; Myshko et al., 2024 ; Tausová et al., 2026 ). Specifically, it remains unclear how the extensive computational infrastructure required to sustain blockchain networks and sophisticated AI models can be reconciled with overarching carbon footprint reduction targets; consequently, a risk exists that digital solutions may generate unforeseen negative environmental externalities (Bandi et al., 2026 ; Kiskira et al., 2026 ; Zafeiriou et al., 2025 ). Furthermore, the mechanisms required to safeguard consumer cognitive sovereignty against the influence of recommendation algorithms and hyper-personalised labelling necessitate urgent legal clarification (Branda & Ciccozzi, 2026 ; Rojano et al., 2025 ). These epistemic and normative lacunae provide the impetus for formulating applied conclusions and strategic recommendations designed to steer the digital transition of the European agri-food sector in an equitable and sustainable manner. 4.7. Limitations of the review Principal limitations of this review include the absence of a formal assessment of the risk of bias utilising a validated instrument, which potentially constrains the robustness of the conclusions and the generalisability of the results. 5. Conclusions In summary, this systematic review indicates a profound structural transformation within European Union (EU) agri-food supply chains, characterised by a transition from passive, purely documentary traceability towards an active, dynamic, and predictive paradigm. This evolution, underpinned by the integration of artificial intelligence (AI) and advanced digital labelling technologies, suggests a fundamental redefinition of the ontological framework of food traceability within the burgeoning e-commerce landscape. Unlike conventional methodologies, wherein information accompanied the product in a static and frequently fragmented format, emergent technological architectures transform the label from an inert repository of data into an active agent of regulation, communication, and continuous surveillance. This paradigm shift transcends the mere technical modernisation of logistical infrastructure, emerging instead as a critical strategic instrument for surmounting the jurisdictional barriers inherent in transnational digital markets. The synthesised data suggest that the deployment of machine learning algorithms and digital product passports may significantly facilitate the alignment of commercial practices with the sustainability, transparency, and circular economy objectives articulated in the European Green Deal. Consequently, the convergence of technological innovation and stringent regulatory requirements offers a robust conceptual framework for optimising food governance, although the empirical efficacy of these complex systems remains contingent upon the quality, security, and interoperability of data harvested across the entire agri-food value chain. Furthermore, a critical synthesis of the extant evidence suggests that the successful deployment of these digital instruments in enforcing trade regulations necessitates a systemic approach capable of integrating rigorous food safety requirements with the imperatives of data sovereignty and consumer privacy. Whilst emerging technologies offer extensive real-time monitoring capabilities and the early detection of anomalies or fraudulent activities, their large-scale implementation within the European Union faces substantial governance and standardisation challenges. Our analysis indicates that the absence of unified technical protocols and the fragmentation of national legislative frameworks may significantly attenuate the capacity of artificial intelligence to ensure seamless and automated regulatory compliance within the e-commerce environment. Moreover, whilst digital passports and intelligent labels promise granular visibility throughout the product life cycle, the specific mechanisms through which these tools might effectively harmonise disparate regulations at a global scale—extending beyond the internal borders of the European Union—remain insufficiently elucidated in the current literature. This normative and technical ambiguity underscores the necessity for analytical rigour in assessing the long-term impact of digitalisation on global food security, suggesting that technological innovation must be complemented by sustained normative diplomacy. Nevertheless, this review identifies substantial epistemic and practical constraints, most notably a significant lacuna in assessing the differential impact of these technologies across diverse economic cohorts within the supply chain. Whilst academic and policy discourse frequently emphasises the macro-systemic advantages of digitalisation, current evidence is insufficient to provide a granular understanding of how high implementation costs and technical complexities affect smallholders and local producers relative to large-scale transnational logistical networks. This structural asymmetry suggests an inherent risk of digital marginalisation, whereby entities with constrained financial and technological resources may be excluded from a European e-commerce market increasingly defined by stringent traceability standards. The paucity of rigorous comparative studies and meta-analyses quantifying these economic and operational disparities represents a significant weakness in the current state of knowledge. Therefore, the uncritical adoption of AI-driven solutions, without due consideration for the technological absorption capacity of small-scale producers, could precipitate unintended competitive distortions, thereby undermining the principles of equity, social cohesion, and agricultural diversity that European sustainability policies are designed to safeguard and promote. Consequently, the findings of this systematic review necessitate a strategic recalibration of European public policy, advocating for a transition from the imposition of abstract technological mandates towards the development of inclusive, flexible regulatory frameworks adaptable to empirical realities. It is imperative that future legislative initiatives incorporate specific financial and technical support mechanisms to facilitate the adoption of digital labelling by micro-agricultural enterprises, thereby ensuring an equitable and sustainable digitalisation of the entire sector. Prospective research should prioritise empirical investigations and comparative studies focused on detailed cost-benefit analyses of AI implementation among small-scale producers, alongside assessments of the interoperability of traceability systems within complex extra-EU jurisdictional contexts. Recognising the inherent limitations of the current evidence base and the requirement for continuous validation, it is evident that the maturation of this field demands a deeply multidisciplinary approach capable of reconciling algorithmic innovation with the socio-economic realities of European agriculture. This shift in methodological and policy perspective will provide the essential foundation for future scientific inquiry to translate the theoretical potential of digital traceability into practical, equitable, and globally applicable solutions. Overall, the results suggest patterns aligned with the study objectives. Future research should validate these observations in larger and more diverse settings. Declarations Ethical Considerations Not applicable. This study is based exclusively on published literature. Data Availability Statement All data extracted from the constituent studies are available within the Supplementary Information. Furthermore, detailed search strings and methodological evaluation protocols are available from the corresponding author upon reasonable request. Declaration of author contributions (CRediT) The sole author was responsible for conceptualisation, methodology, investigation, data curation, formal analysis, validation, visualisation, and project administration. Additionally, the author undertook the original draft preparation and the subsequent review and editing of the manuscript. The author assumes full accountability for the scientific content, the interpretation of results, and the final version of the manuscript. AI Use Declaration Artificial intelligence-based tools were employed exclusively for linguistic refinement, encompassing grammatical corrections, orthographic standardisation, and the enhancement of prose clarity. No generative AI systems were utilised to produce scientific content, execute data extraction, perform statistical analyses, or inform the interpretation of findings. The authors independently formulated all methodological frameworks, synthesised the data, and derived all conclusions; consequently, the authors retain full accountability for the accuracy and intellectual integrity of this work. Funding This research was supported by the Lucian Blaga University of Sibiu via research grant LBUS-IRG-2024. Acknowledgments Primarily, the author acknowledges the support of the Google Cloud Research Credits programme. Furthermore, all computational analyses and data processing for this project were executed using the infrastructure provided by Google Cloud. Conflict of Interest The author declares no competing financial, professional, or personal interests that could potentially influence the objectivity and impartiality of the findings reported in this manuscript. References Ahmad A, García JO, Alexopoulos A, Koutras K, Kalogeras A, Stefanidis K, Cassinello PC, Garrido LGD, Núñez VM (2024) Agriculture 4.0: Integrating Blockchain for More Efficient and Sustainable Production. REIDOCREA-REVISTA EECTRONICA DE INVESTIGACION Y DOCENCIA CREATIVA . 13:206–212 Ahmadzadegan MH, Mohammadzadeh MS, Eftekharnejad G, Ghorbani H (2020) Intelligent monitoring systems for transportation of perishable products based internet of things (IoT) technology. Proceedings – 2020 IEEE 9th International Conference on Communication Systems and Network Technologies, CSNT 2020 Amentae TK, Gebresenbet G (2021) Digitalization and Future Agro-Food Supply Chain Management: A Literature-Based Implications. Sustainability 13(21) Article 12181. https://doi.org/10.3390/su132112181 Andreghetto A, Ferreira MR, Kourgiantakis M (2025) Digital Strategies for Promoting PDO and PGI Agricultural Products in Southern Europe: Evaluating Online Presence and Sustainability Communication [Article]. Sustain (Switzerland) 17(11):4958. https://doi.org/10.3390/su17114958 Atik C (2022) the Data Ownership Debate. IIC-INTERNATIONAL REVIEW OF INTELLECTUAL PROPERTY AND COMPETITION LAW . 53(5):701–742. https://doi.org/10.1007/s40319-022-01191-w . Towards Comprehensive European Agricultural Data Governance: Moving Beyond Balaska V, Adamidou Z, Vryzas Z, Gasteratos A (2023) Sustainable Crop Protection via Robotics and Artificial Intelligence Solutions [Article]. Machines 11(8). Article 774. https://doi.org/10.3390/machines11080774 Bandi E, Bavagnoli F, Tettamanzi P (2026) Circular Economy, Short Food Supply Chain and Digitalisation Drive the Agri-Food Sector Transition Towards Environmental and Social Sustainability: A Literature Review. Corp Soc Responsib Environ Manag 33(2):1700–1715. https://doi.org/10.1002/csr.70261 Baralla G, Pinna A, Corrias G (2019) Ensure traceability in european food supply chain by using a blockchain system. Proceedings – 2019 IEEE/ACM 2nd International Workshop on Emerging Trends in Software Engineering for Blockchain, WETSEB 2019 Barge P, Franchetto MAA, Maritano V, Tortia C, Gay P, Schifanella C, Cena F (2023) Blockchain Technology for Food Supply Chain Traceability and Authentication. Lecture Notes in Civil Engineering Bekkouche S, de -Magistris T (2025) Digitalization in the European agri-food supply chain: a scoping review of traceability, transparency, and sustainability. FRONTIERS IN BLOCKCHAIN , 8 , Article 1701872. https://doi.org/10.3389/fbloc.2025.1701872 Blazo O (2022) Bypassing Competition Law, Bypassing through Competition Law. HOT TOPICS & NEXT STEPS, EU ANTITRUST Bogadi NP, Ursulin-Trstenjak N, Sarkanj B, Sarkanj ID (2025) Food and Agriculture Defense in the Supply Chain: A Critical Review. Appl SCIENCES-BASEL 15(20). Article 11020. https://doi.org/10.3390/app152011020 Branda F, Ciccozzi M (2026) The comfort of automation: why cognitive sovereignty matters in AI-driven life sciences [Article]. Artif Intell Life Sci 9 Article 100158. https://doi.org/10.1016/j.ailsci.2026.100158 Brunori G, Bacco M, Puerta-Piñero C, Borzacchiello MT, Stormer E (2025) Agri-food data spaces: Highlighting the need for a farm-centered strategy. Data Brief 59:111388. https://doi.org/10.1016/j.dib.2025.111388 Burylo Y, Matviichuk A, Vovk Y, Shvets O, Mishchuk I (2023) THE DEVELOPMENT OF UKRAINE'S SANITARY AND PHYTOSANITARY OFFICIAL CONTROLS LEGISLATION IN LIGHT OF EU LAW. LEX HUMANA 15(1):589–603 Chatzitheodoridis F, Spinthiropoulos K, Loizou E, Kalfas D, Kalogiannidis S, Gortsilas E (2025) Rationale of Government Policies on Agricultural Productivity and Sustainability: A European Perspective [Article]. Res WORLD AGRICULTURAL Econ 6(2):605–628. https://doi.org/10.36956/rwae.v6i2.1712 Christopoulos P, Schlenk R, Kazdal D, Blasi M, Lennerz J, Shah R, Budczies J, Malek N, Fröhling S, Rosenquist R, Schirmacher P, Bozorgmehr F, Kuon J, Reck M, Thomas M, Stenzinger A (2023) Real-world data for precision cancer medicine—A European perspective [Article]. Genes Chromosomes Cancer 62(9):557–563. https://doi.org/10.1002/gcc.23135 Chrysomallidis C, Doukas YE (2024) Building Networks in the Agri-Food Chain: EU as Facilitator Toward Digital Transformation and Greening of Agricultural Sector [Article]. J Int Food Agribusiness Mark 36(1):47–67. https://doi.org/10.1080/08974438.2023.2281328 Cordero-Gutiérrez R, Lahuerta-Otero E, Zapatero-González A (2025) Leveraging blockchain for ecosystem service transparency: enhancing consumer value and sustainability in the beef industry. AGRICULTURAL FOOD Econ 13(1). Article 45. https://doi.org/10.1186/s40100-025-00392-4 D'Amico G, Szopik-Depczynska K, Beltramo R, D'Adamo I, Ioppolo G (2022) Smart and Sustainable Bioeconomy Platform: A New Approach towards Sustainability. Sustainability 14(1). Article 466. https://doi.org/10.3390/su14010466 De Sutter E, Borry P, Huys I, Barbier L (2023) Informing a European guidance framework on electronic informed consent in clinical research: a qualitative study. BMC Health Serv Res 23(1):181. https://doi.org/10.1186/s12913-023-09173-5 de Vries JR, Turner JA, Finlay-Smits S, Ryan A, Klerkx L (2023) Trust in agri-food value chains: a systematic review. Int FOOD AGRIBUSINESS Manage Rev 26(2):175–197. https://doi.org/10.22434/IFAMR2022.0032 Deepika N, George R, Juturi RKR, Haidary BI, Naik P, Goudanavar P (2026) Elevating Drug Safety Oversight: A Comprehensive Examination of Dynamic and Static Monitoring Systems across the United States, European Union, and India. J Pharm Innov 21(1). Article 94. https://doi.org/10.1007/s12247-025-10328-6 Dela Cruz NAO, Villanueva ACB, Tolin L, Disse S, Lensink R, White H (2025) Effects of Interventions to Improve Access to Financial Services for Micro, Small, and Medium-Sized Enterprises in Low- and Middle-Income Countries: An Evidence and Gap Map. Campbell Syst Rev 21(3):e70061. https://doi.org/10.1002/cl2.70061 Dhal SB, Kar D (2024) Transforming Agricultural Productivity with AI-Driven Forecasting: Innovations in Food Security and Supply Chain Optimization. FORECASTING 6(4):925–951. https://doi.org/10.3390/forecast6040046 Di Carlantonio E, Romagnoli L, Schatzle A, Base G, Liuzzo G (2024) Official controls on the distance sale of dairy products in the territory of the Modena Local Competent Authority: an analysis of websites. Ital J Food Saf 13(2):12241. https://doi.org/10.4081/ijfs.2024.12241 Di Gregorio L, Nolfi L, Latini A, Nikoloudakis N, Bunnefeld N, Notarfonso M, Bernini R, Manikas I, Bevivino A (2024) Getting (ECO)Ready: Does EU Legislation Integrate Up-to-Date Scientific Data for Food Security and Biodiversity Preservation Under Climate Change? [Article]. Sustain (Switzerland) 16(23) Article 10749. https://doi.org/10.3390/su162310749 Dokuzlu S (2016) Geographical indications, implementation and traceability: Gemlik table olives [Article]. Br Food J 118(9):2074–2085. https://doi.org/10.1108/BFJ-09-2015-0341 Ehlers MH, Finger R, El Benni N, Gocht A, Sorensen CAG, Gusset M, Pfeifer C, Poppe K, Regan A, Rose DC, Wolfert S, Huber R (2022) Scenarios for European agricultural policymaking in the era of digitalisation. Agricultural Systems , 196 , Article 103318. https://doi.org/10.1016/j.agsy.2021.103318 Evangelatos S, Bourgos P, Tsampanaki N, Kefalakis N, Ntemou A, Lappas P (2025) Unveiling the Digital Food Product Passport Paradigm for Ensuring Traceability and Safety in the Global Food Ecosystem. EEITE 2025–6th International Conference in Electronic Engineering and Information Technology Fraser I, Balcombe K (2018) Wrapped in the Flag: Food Choice and Country of Origin Labelling [Article]. EuroChoices 17(3):37–42. https://doi.org/10.1111/1746-692X.12189 Garcia-Gallego JM, Chamorro-Mera A, Valero-Amaro V, Martinez-Jimenez M, Romero P, Miranda MT, Rubio S (2025) Agri-Food E-Marketplaces as New Business Models for Smallholders: A Case Analysis in Spain. AGRICULTURE-BASEL 15(17) Article 1806. https://doi.org/10.3390/agriculture15171806 Georgescu B, Onete CB, Plesea DA, Chita SD, Sava S, THE USE OF BLOCKCHAIN TECHNOLOGY. STUDY ON THE IMPLEMENTATION OF THE (2022) Amfiteatru economic 24(60):379–394. https://doi.org/10.24818/EA/2022/60/379 . CONSUMER ATTITUDE TOWARDS Giannini V, Iacobucci D, Orci M (2025) The adoption of blockchain technology to support supply chain traceability in the agri-food industry: firm-level evidence from Italy. J INDUSTRIAL Bus Econ 52(3):775–804. https://doi.org/10.1007/s40812-025-00358-z Gilbert S, Fenech M, Hirsch M, Upadhyay S, Biasiucci A, Starlinger J (2021) Algorithm Change Protocols in the Regulation of Adaptive Machine Lear ning-Based Medical Devices. J Med Internet Res 23(10) Article e30545. https://doi.org/10.2196/30545 Gonçalves C, Fernandes J, Brites C (2025) Blockchain-Enabled Traceability in the Rice Supply Chain: Insights from the TRACE-RICE Project. Foods 14(21). https://doi.org/10.3390/foods14213711 González-Ceballos L, Melero B, Trigo-López M, Vallejos S, Muñoz A, García FC, Fernandez-Muiño MA, Sancho MT, García JM (2020) Functional aromatic polyamides for the preparation of coated fibres as smart labels for the visual detection of biogenic amine vapours and fish spoilage [Article]. Sensors and Actuators, B: Chemical , 304 , Article 127249. https://doi.org/10.1016/j.snb.2019.127249 Gori A, Sartoni M, Armani A, Nuvoloni R (2026) Legal responsibility and regulatory compliance with food information on e-grocery platforms: an Italian case study offering insights for future policies and consumer protection in the European context. Food Policy 138:103013. https://doi.org/10.1016/j.foodpol.2025.103013 Holzinger A, Fister I Jr., Fister I, Kaul S, H. P., Asseng S (2024) Human-Centered AI in Smart Farming: Toward Agriculture 5.0. IEEE Access 12:62199–62214. https://doi.org/10.1109/ACCESS.2024.3395532 Htun NN, Wisniewska A, Nocella G, Santa Cruz E, Peracaula-Moner A, Vehmas K, Hakola L, Liczmanska-Kopcewicz K, Bridgett L, Verbert K (2023) Smart tag packaging technologies: A qualitative investigation of consumers' needs and expectations. Packaging Technol Sci 36(7):595–613. https://doi.org/10.1002/pts.2731 Jia SS, Todd AR, Vanderlee L, Farrell P, Allman-Farinelli M, Sacks G, Gibson AA, Partridge SR (2024) Offline to online: a systematic mapping review of evidence to inform nutrition-related policies applicable to online food delivery platforms. BMC Med 22(1):542. https://doi.org/10.1186/s12916-024-03747-8 Jiang S, Natvig MK, Rader B, Bourgos T, P., Tsampanaki N (2025) Traceability Data in the form of Digital Food Product Passports for Fish Supply Chains. EEITE 2025–6th International Conference in Electronic Engineering and Information Technology Juliá-Igual JF, López-Becerra EI, Meliá-Marti E (2025) Agri-food cooperatives in the European scientific literature in the period 2005–2024. CIRIEC-ESPANA REVISTA DE ECONOMIA PUBLICA SOCIAL Y COOPERATIVA . 114:163–196. https://doi.org/10.7203/CIRIEC-E.114.30373 Karkanias C, Malamakis A, Banias GF (2026) Blockchain and Megatrends in Agri-Food Systems: A Multi-Source Evidence Approach. Foods 15(3). https://doi.org/10.3390/foods15030447 Khurana D, Kaur R, Roul RK, Batra S (2026) Explainable Artificial Intelligence in Cancer Care: A Domain-Wise Review of Adoption, Challenges and Opportunities [Article]. Expert Syst 43(4) Article e70238. https://doi.org/10.1111/exsy.70238 Kiskira K, Plakantonaki S, Gerolimos N, Kalkanis K, Sfyroera E, Coelho F, Priniotakis G (2026) Life Cycle Optimization of Circular Industrial Processes: Advances in By-Product Recovery for Renewable Energy Applications [Review]. CLEAN Technol 8(1). Article 5. https://doi.org/10.3390/cleantechnol8010005 Kokole D, Neufeld M, Correia D, Rehm J, Pettigrew S, Paradis C, Ferreira-Borges C, Sawaya M, Causio FA, Fernandez-Shaw N, Clemente A, Bermúdez L, Senica P, Plešnar L, Lenaers B, de Veld J, Savvidou E, Pinho AP, Vasconcelos R, Pereira C, Tietz S (2025) Beyond the label: analyzing the presence and information behind the QR codes on alcohol containers in 13 European countries [Article]. International Journal of Drug Policy , 142 , Article 104862. https://doi.org/10.1016/j.drugpo.2025.104862 Kong Y, Suntrayuth S, Lin F (2024) Construction of Cross-Border E-Commerce Supply Chain of Agricultural Food Products based on Blockchain Technology [Article]. Nat Eng Sci 9(2):145–163. https://doi.org/10.28978/nesciences.1569226 Kostic M, Sarac V, Narandzic T, Kovacevic DB (2026) Digital and Green Technological Drivers of Transformation in the Agri-Food Sector. Foods 15(6). Article 1081. https://doi.org/10.3390/foods15061081 Kumar P, Hendriks T, Panoutsopoulos H, Brewster C (2024) Investigating FAIR data principles compliance in horizon 2020 funded Agri-food and rural development multi-actor projects [Article]. Agric Syst 214:103822. https://doi.org/10.1016/j.agsy.2023.103822 Lenarczyk G, Minssen T, Price N, Rai A (2025) The future of AI regulation in drug development: a comparative analysis. J Law Biosci 12(2):lsaf028. https://doi.org/10.1093/jlb/lsaf028 Leone L (2017) Beyond Connectivity: The Internet of Food Architecture Between Ethics and the EU Citizenry [Article]. J Agric Environ Ethics 30(3):423–438. https://doi.org/10.1007/s10806-017-9675-6 Li XY, Lu XY, Ma CY, Hu XJ, Xie J, Ding ZY (2025) Lanthanide metal-organic frameworks based smart labels for accurate temperature monitoring. Microchim Acta 192(7). Article 401. https://doi.org/10.1007/s00604-025-07208-8 Li YC, Elliott CT, Petchkongkaew A, Wu D (2024) The classification, detection and 'SMART' control of the nine sins of tea fraud. Trends Food Sci Technol 149:104565. https://doi.org/10.1016/j.tifs.2024.104565 Lin JC, Jain B, Iyer JM, Rola I, Srinivasan AR, Kang C, Patel H, Parikh RB (2025) Benefit-Risk Reporting for FDA-Cleared Artificial Intelligence-Enabled Medical Devices. JAMA HEALTH FORUM 6(9) Article e253351. https://doi.org/10.1001/jamahealthforum.2025.3351 Manta AG (2026) From Sustainability Narratives to Digital Infrastructures: Mapping the Transformation of Smart Agri-Food Systems. Foods 15(3). https://doi.org/10.3390/foods15030469 Marti AM (2021) Globalisation, climatic and digital transitions and pandemic effects on agri-food systems: implications for public policies. REVISTA ESPANOLA DE ESTUDIOS AGROSOCIALES Y PESQUEROS-REEAP (257), 61–130 Martín-Cervantes PA, Madrazo PD, Ziarati P (2025) Understanding European consumers' acceptance of edible insects through machine learning. Br Food J. https://doi.org/10.1108/BFJ-03-2025-0351 Martínez-Castañeda M, Feijoo C (2023) Use of blockchain in the agri-food value chain: State of the art in Spain and some lessons from the perspective of public support. Telecomm Policy 47(6). Article 102574. https://doi.org/10.1016/j.telpol.2023.102574 Marvin HJP, Bouzembrak Y, van der Fels-Klerx HJ, Kempenaar C, Veerkamp R, Chauhan A, Stroosnijder S, Top J, Simsek-Senel G, Vrolijk H, Knibbe WJ, Zhang L, Boom R, Tekinerdogan B (2022) Digitalisation and Artificial Intelligence for sustainable food systems [Note]. Trends Food Sci Technol 120:344–348. https://doi.org/10.1016/j.tifs.2022.01.020 Mascarello G, Pinto A, Crovato S, Pezzoli BT, Pietropaoli M, Bertola M, Mutinelli F, Formato G (2024) Consumers' Perceptions and Behaviors Regarding Honey Purchases and Expectations on Traceability and Sustainability in Italy. Sustainability 16(20) Article 8846. https://doi.org/10.3390/su16208846 Mazzù MF, Marozzo V, Baccelloni A, Pompeis FD (2021) Measuring the Effect of Blockchain Extrinsic Cues on Consumers' Perceived Flavor and Healthiness: A Cross-Country Analysis. Foods 10(6). Article 1413. https://doi.org/10.3390/foods10061413 Meng ZZ, Wu ZP, Gray J (2019) RFID-Based Object-Centric Data Management Framework for Smart Manufacturing Applications. IEEE Internet Things J 6(2):2706–2716. https://doi.org/10.1109/JIOT.2018.2873426 Moosavy M, Khatibi SA, Faraz AS, Tarighi A (2025) Identification and quantification of permitted and non-permitted synthetic food colorants in meat products [Article]. Food Control 178:111434. https://doi.org/10.1016/j.foodcont.2025.111434 Muehlematter UJ, Daniore P, Vokinger KN (2021) Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015-20): a comparative analysis. Lancet Digit Health 3(3):e195–e203. https://doi.org/10.1016/s2589-7500(20)30292-2 Mugion RG, Ungaro V, Di Pietro L, Amin A, Bisceglia F (2026) Mapping Women's Role in Agriculture 4.0: A Bibliometric Analysis and Conceptual Framework. AGRICULTURE-BASEL 16(2). Article 214. https://doi.org/10.3390/agriculture16020214 Munoz-Arcentales A, López-Pernas S, Pozo A, Alonso A, Salvachúa J, Huecas G (2020) Implement Using FIWARE Sustainability 12(9). Article 3885. https://doi.org/10.3390/su12093885 . Data Usage and Access Control in Industrial Data Spaces Myshko A, Checchinato F, Colapinto C, Finotto V, Mauracher C (2024) Towards the twin transition in the agri-food sector? Framing the current debate on sustainability and digitalisation [Review]. J Clean Prod 452., Article 142063. https://doi.org/10.1016/j.jclepro.2024.142063 Narayana GP, Singh DK, Chudasama MH, Deotale S, Reddy NBP, Perumal T (2025) Intelligent packaging of functional foods: A comprehensive review of its advances, recyclability, and regulatory frameworks. Bioresource Technol Rep 32:102449. https://doi.org/10.1016/j.biteb.2025.102449 Nicolau AM, Petcu P (2026) Beyond Green Labels: Leveraging Blockchain, IoT, and AI for Enhanced Traceability and Verification of Green Marketing Claims in Transnational Agri-Food Supply Chains. Sustainability 18(2). Article 782. https://doi.org/10.3390/su18020782 Nisa SK, Elias H, Usman S, Chuprat S (2024) Discovering the Global Landscape of Agri-Food and Blockchain: A Bibliometric Review. Int J Adv Comput Sci Appl 15(4):556–575 Odobasa R, Borzan Z (2021) CHALLENGES AND IMPACTS OF COVID-19 PANDEMIC AND THE EUROPEAN GREEN PLAN ON THE DEVELOPMENT OF THE CROATIAN AGRICULTURAL AND FOOD SECTOR 10TH INTERNATIONAL SCIENTIFIC SYMPOSIUM REGION ENTREPRENEURSHIP DEVELOPMENT (RED 2021) Palka P, Palosz R, Porebski A, Wisniewska K (2024) A dataset on the contents of 100 terms of service of online platforms, analyzed and evaluated under the EU consumer law. DATA BRIEF 53:110136. https://doi.org/10.1016/j.dib.2024.110136 Panagos P, Orgiazzi A, Lugato E, Vandenesch G, Thannberger L, Neff JC, Reijneveld A, Mathijs E, Chapot P, Guerrini S, Styriaková D, Kremers J, Martin P, van der Tol R, Burgeon V, Kalimeri E, Van Eynde E (2025) Healthy soils as a booster to EU competitiveness. LAND USE POLICY , 158 , Article 107755. https://doi.org/10.1016/j.landusepol.2025.107755 Panni F, Massaro A, Bodenbender L, Zacometti C, Piro R, Savino AF, Weller P, Tata A (2026) HS-GC-IMS authenticates the botanical origin of seven types of Italian monofloral honey, annotating the significant volatile compounds by the novel HS- SPME-GC-IMS-MS prototype [Article]. Microchem J 224 Article 117526. https://doi.org/10.1016/j.microc.2026.117526 Parra-López C, Carmona-Torres C (2026) Enabling the Circular Food Economy Through Industry 4.0: A Review of Applications, Challenges, and Policies in the European Union. Compr Rev Food Sci Food Saf 25(2):e70431. https://doi.org/10.1111/1541-4337.70431 Perret JK, Skretkowicz Y (2025) Nutri-Score - a bibliographic review and analysis of the literature. J FOOD Nutr Res 64(2):91–103 Pesapane F, Volonté C, Codari M, Sardanelli F (2018) Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States. Insights Imaging 9(5):745–753. https://doi.org/10.1007/s13244-018-0645-y Rehm J, Ferreira-Borges C, Kokole D, Neufeld M, Olsen A, Rovira P, Garcia LS, Tran A, Colom J (2023) Assessing the impact of providing digital product information on the health risks of alcoholic beverages to the consumer at point of sale: A pilot study. Drug Alcohol Rev 42(6):1332–1337. https://doi.org/10.1111/dar.13676 Rodriguez J, Piccoli G (2024) Sustaining superior visibility within digital platforms through inside and outside competitive action repertoires [Article]. Electron Commer Res Appl 68 Article 101456. https://doi.org/10.1016/j.elerap.2024.101456 Rojano FJP, García LV, Martínez TF (2025) Disinformation in the food sector. Case studies published in Maldita, Newtral and EFE Verifica. REVISTA Int DE RELACIONES PUBLICAS 30(15):45–66. https://doi.org/10.5783/revrrpp.v15i30.913 Rossi S, Gemma S, Borghini F, Perini M, Butini S, Carullo G, Campiani G (2025) Agri-food traceability today: Advancing innovation towards efficiency, sustainability, ethical sourcing, and safety in food supply chains. Trends Food Sci Technol 163:105154. https://doi.org/10.1016/j.tifs.2025.105154 Roussaki I, Doolin K, Skarmeta A, Routis G, Claffey E, Mora M, Martinez JA (2023) Building an interoperable space for smart agriculture. Digit Commun NETWORKS 9(1):183–193. https://doi.org/10.1016/j.dcan.2022.02.004 Ryan M, Gürtler P, Bogucki A (2024) Will the real data sovereign please stand up? An EU policy response to sovereignty in data spaces. Int J LAW Inform Technol 32(1):eaae006. https://doi.org/10.1093/ijlit/eaae006 Santra S, Kukreja P, Saxena K, Gandhi S, Singh OV (2024) Navigating regulatory and policy challenges for AI enabled combination devices. Front Med Technol 6:1473350. https://doi.org/10.3389/fmedt.2024.1473350 Sapienza S (2022) Smart Solutions in AgriTech: Research Trajectories in the Digital Transition. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Sapienza S, Vedder A (2023) Principle-based recommendations for big data and machine learning in food safety: the P-SAFETY model [Article]. AI Soc 38(1):5–20. https://doi.org/10.1007/s00146-021-01282-1 Sekulic NM, Narancic M, Nikolic M (2025) LEGAL DIMENSIONS OF EU SUSTAINABLE AGRICULTURE POLICIES. Ekon POLJOPRIVREDA-ECONOMICS Agric 72(3):1105–1119. https://doi.org/10.59267/ekoPolj25031105M Shete RP, Bongale AM, Laxmikantha K, Hiremath S, Dharrao D (2025) Comprehensive analysis of aquaculture research trends focusing on Internet of Things, machine learning, water quality monitoring, and cybersecurity over two decades using bibliometric data. RESULTS Eng 28:108266. https://doi.org/10.1016/j.rineng.2025.108266 Singh PK, Agrawal N, Khare AK (2026) Artificial Intelligence: An Advanced Tool to Improve Safety and Quality in the Meat Industry. J Food Process Eng 49(2) Article e70383. https://doi.org/10.1111/jfpe.70383 Singh R, Zhou K, Auclair JR (2025) Reimagining drug regulation in the age of AI: a framework for the AI-enabled Ecosystem for Therapeutics. Front Med (Lausanne) 12:1679611. https://doi.org/10.3389/fmed.2025.1679611 Solaiman B (2023) FROM 'AI TO LAW' IN HEALTHCARE: THE PROLIFERATION OF GLOBAL GUIDELINES IN A VOID OF LEGAL UNCERTAINTY [Article]. Med Law 42(2):391–406 Sortino A, De Leo G, Caruso T, Zinnai A, Beltrame F (2023) Agri-food: which technologies to guarantee our health? XXXI INTERNATIONAL HORTICULTURAL CONGRESS, IHC2022: III INTERNATIONAL SYMPOSIUM ON MECHANIZATION, PRECISION HORTICULTURE, AND ROBOTICS: PRECISION AND DIGITAL HORTICULTURE IN FIELD ENVIRONMENTS Staver L, THE INTEGRATION INTO THE EUROPEAN UNION (2025) OPTIMIZING MOLDOVA'S AGRI-FOOD SUPPLY CHAINS FOR. SCIENTIFIC PAPERS-SERIES MANAGEMENT ECONOMIC ENGINEERING IN AGRICULTURE AND RURAL DEVELOPMENT , 25 (2) Stoyanov K (2024) STRATEGIES FOR POLICY IN SCIENCE AND EDUCATION-STRATEGII NA OBRAZOVATELNATA I NAUCHNATA POLITIKA. 32(6):127–137. https://doi.org/10.53656/str2024-6s-10-int . INTEGRATION OF BLOCKCHAIN TECHNOLOGIES IN THE FOOD SUPPLY CHAIN - REGULATORY FRAMEWORKS IN THE EUROPEAN UNION Stranieri S, Riccardi F, Meuwissen MPM, Soregaroli C (2021) Exploring the impact of blockchain on the performance of agri-food supply chains. Food Control 119., Article 107495. https://doi.org/10.1016/j.foodcont.2020.107495 Tache I, THE LEADING INDUSTRIES IN THE NEW EU MEMBER STATES AND THEIR CONTRIBUTION TO INCREASING THE EU’S EXPORT POTENTIAL (2024). In Contemporary Studies in Economic and Financial Analysis (Vol. 115, pp. 141–157). https://doi.org/10.1108/S1569-375920240000115009 Tadesse T, Ramirez-Rodriguez S, Rahmani D, Gezahegn TW, Verherbrugghen H, Vermeulen A, Skourletis N, Roig JMG (2025) Healthy but also traceable: consumer choices for traits of food waste-reducing innovations. J Clean Prod 521:146299. https://doi.org/10.1016/j.jclepro.2025.146299 Tasic I, Benaissa RR, Gómez PA, Fernandez JA, Cano MD (2025) Revisiting Traceability of Vegetable Fresh-Products in the EU: Why Blockchain Does (Not) Work? [Letter]. INTERNET Technol Lett 8(6) Article e647. https://doi.org/10.1002/itl2.647 Tausová M, Culková K, Mykhei M, Taus P, Domaracká L, Vrastiaková A (2026) Sectoral Analysis of Food Waste in EU Countries: Implications for Pro-Environmental Orientation and Policy. Foods 15(6):972. https://doi.org/10.3390/foods15060972 Terzis P, Santamaria Echeverria OE (2023) Interoperability and governance in the European Health Data Space regulation [Note]. Med Law Int 23(4):368–376. https://doi.org/10.1177/09685332231165692 Toader DC, Radulescu CM, Toader C (2024) Investigating the Adoption of Blockchain Technology in Agri-Food Supply Chains: Analysis of an Extended UTAUT Model. AGRICULTURE-BASEL 14(4):614. https://doi.org/10.3390/agriculture14040614 Umeda Y, Kitagawa K, Hirose Y, Akaho K, Sakai Y, Ohta M (2020) Potential impacts of the European Union’s circular economy policy on Japanese manufacturers [Article]. Int J Autom Technol 14(6):857–866. https://doi.org/10.20965/ijat.2020.p0857 Urdu D, Berre AJ, Sundmaeker H, Rilling S, Roussaki I, Marguglio A, Doolin K, Zaborowski P, Atkinson R, Palma R, Faraldi M, Wolfert S (2024) Aligning interoperability architectures for digital agri-food platforms [Article]. Computers and Electronics in Agriculture , 224 , Article 109194. https://doi.org/10.1016/j.compag.2024.109194 Varavallo G, Caragnano G, Bertone F, Vernetti-Prot L, Terzo O (2022) Traceability Platform Based on Green Blockchain: An Application Case Study in Dairy Supply Chain. Sustainability 14(6):3321. https://doi.org/10.3390/su14063321 Villegas-Ch W, Govea J, Moncayo-Moncayo PS, Navarro AM, Chavez-Pirca C (2025) Optimization of the Traceability of Perishable Products Through Light Blockchain and IoT in the Food Industry. IEEE Access 13:67599–67615. https://doi.org/10.1109/ACCESS.2025.3561596 Wagan SM, Sidra S (2026) Interpreting Irrigation Decisions: Explainable AI Using SHAP and LIME in Agricultural Water Management Models. Water Resour Manage 40(1) Article 52. https://doi.org/10.1007/s11269-025-04468-0 Wang Y, Wu W, Xu J, Gao M, Wu Z, Wang R, Liu H (2025) Changes in Quality and Safety Indexes During Rice Harvest and Discussion on Drying Technology. Foods 14(7). https://doi.org/10.3390/foods14071225 Xie X, Fu B, Zhang M, Zhang W, Xu Z, Liu S, Wang J (2024) Eugenol-infused alginate nanoemulsion: A fluorescent label for direct food shelf-life extension and non-contact detection of food spoilage through biogenic amine sensing [Article]. Food Control 163:110525. https://doi.org/10.1016/j.foodcont.2024.110525 Zafeiriou E, Galatsidas S, Tsantopoulos G, Sofios S, Siakou E, Arabatzis G (2025) Assessing environmental and economic dynamics in the EU agri-food sector: The impact of imports through a BVAR analysis. NEW MEDIT 24(2):37–57. https://doi.org/10.30682/nm2502c Zarbà C, Chinnici G, Matarazzo A, Privitera D, Scuderi A (2024) The innovative role of blockchain in agri-food systems: A literature analysis. Food Control 164., Article 110603. https://doi.org/10.1016/j.foodcont.2024.110603 Zhang Z, Yan B (2025) Smart Photoresponsive Label with Red Fluorescence and Blue Phosphorescence Promoted by Hydrogen-Bonded Organic Frameworks for Food Additives Detection. ACS Appl Mater Interfaces 17(20):29941–29951. https://doi.org/10.1021/acsami.5c06337 Zyoud S, Zyoud AH (2025) Revealing global trends on nature-based solutions: Mapping and visualizing research landscapes [Article]. Nature-Based Solutions 7., Article 100229. https://doi.org/10.1016/j.nbsj.2025.100229 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9439187","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":624379381,"identity":"86efea3d-4698-45fb-aca1-e9297dd9bf18","order_by":0,"name":"Ketney Otto","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYNCCA2CS8QFDwQEGNgnGBvyq2ZjBWiSAJLMBgwGJWtgkQFrATHyAX77/4AOGMzZ15uw9ZtU8Bnfs+aSb2yQY99jg1CLZxgx0z400CcueM2a3eQyeJbbJHGyTYHiWhlOLwTFmoHs+HJYwuJED0nI4gU0iEajlwGFCWv5LGNx/Y1YM1GJPpJYbB4C28JgxA7UwthHSItmWbGyQcCZZcsOZtGLJORC/NFskHMDtF37mgw8ffDhmx29w/PDGD28q7tjLz25/eOPDAdwhBgYJYJLDAF2EIGB/QJy6UTAKRsEoGHEAACxhUlPzM2KiAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-1638-1154","institution":"\"Lucian Blaga\" University of Sibiu","correspondingAuthor":true,"prefix":"","firstName":"Ketney","middleName":"","lastName":"Otto","suffix":""}],"badges":[],"createdAt":"2026-04-16 14:07:20","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9439187/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9439187/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107135743,"identity":"90be9dc4-6323-43e4-a1ee-f02da0cd7677","added_by":"auto","created_at":"2026-04-17 07:58:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":76310,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA 2020 flow diagram of the study selection process. The diagram summarizes identification, screening, eligibility assessment, and final inclusion of studies retrieved from Web of Science, Scopus, PubMed, and additional sources. After duplicate removal and title/abstract screening, 140 studies met the eligibility criteria and were included in the review.\u003c/p\u003e","description":"","filename":"figure1flow.png","url":"https://assets-eu.researchsquare.com/files/rs-9439187/v1/251ec1ac451a5b691e4f33ca.png"},{"id":107135747,"identity":"d9afb624-6082-4a41-b4d0-3eb967190212","added_by":"auto","created_at":"2026-04-17 07:58:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":54465,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual distribution of included studies published between 2016 and 2026. The figure shows the temporal evolution of the literature, with a marked increase in publication output in the most recent years and a peak in 2025, indicating growing research interest in the topic.\u003c/p\u003e","description":"","filename":"figure2yeardistribution.png","url":"https://assets-eu.researchsquare.com/files/rs-9439187/v1/73324e317af333d86a4b997f.png"},{"id":107135721,"identity":"2c3db76c-7910-4850-be35-0044cb894e30","added_by":"auto","created_at":"2026-04-17 07:58:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":231713,"visible":true,"origin":"","legend":"\u003cp\u003eMost productive journals publishing studies included in the review. The bar chart presents the leading source titles by number of included articles, highlighting Sustainability, Foods, and Food Control as the most frequent publication outlets within the analyzed corpus.\u003c/p\u003e","description":"","filename":"figure3topjournals.png","url":"https://assets-eu.researchsquare.com/files/rs-9439187/v1/4e8ae1d02d0874d929dbf888.png"},{"id":107135742,"identity":"7568ef1b-f8dc-4c9c-b17f-d00b39e3fd35","added_by":"auto","created_at":"2026-04-17 07:58:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":118567,"visible":true,"origin":"","legend":"\u003cp\u003eMost frequent author keywords identified in the included studies. The chart presents the dominant terms used across the literature as a proxy for keyword co-occurrence patterns, emphasizing recurring research topics such as wearable devices, remote patient monitoring, digital health, diagnostic accuracy, cardiovascular monitoring, artificial intelligence, and arrhythmia detection.\u003c/p\u003e","description":"","filename":"figure4keywordfrequency.png","url":"https://assets-eu.researchsquare.com/files/rs-9439187/v1/e1cbcfe3abcb620b4ccf5093.png"},{"id":107135768,"identity":"37238783-ba79-4735-ba17-3f216ec2128b","added_by":"auto","created_at":"2026-04-17 07:58:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":129880,"visible":true,"origin":"","legend":"\u003cp\u003eThematic map of the conceptual structure of the included literature. Bubble size reflects the relative prominence of each theme, while the position on the centrality and density axes indicates its degree of relevance within the field and its level of internal development. Themes located toward the upper-right region represent well-developed and influential topics, whereas themes with lower centrality or density indicate more isolated or emerging research directions.\u003c/p\u003e","description":"","filename":"figure5thematicmap.png","url":"https://assets-eu.researchsquare.com/files/rs-9439187/v1/8b6ff1821b97d448af0da5b6.png"},{"id":108976533,"identity":"e6a62605-586b-4f37-bcea-948c27d2dcc3","added_by":"auto","created_at":"2026-05-11 11:24:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":970955,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9439187/v1/26630ef9-7c15-4d84-ab5c-a6c751342ba5.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eArtificial Intelligence for Food Traceability and Digital Labelling Within the Framework of E-commerce in the European Union: A Systematic Review of Regulatory, Policy, and Scientific Evidence\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eArtificial Intelligence in Food Traceability and Digital Labelling for E-Commerce in the EU: A Systematic Review of Regulatory, Policy, and Scientific Evidence\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1. Context and Relevance\u003c/h2\u003e \u003cp\u003eAmidst accelerating globalisation and the imperative for ecological transition, the European agri-food system confronts unprecedented pressures to ensure the sustainability, safety, and transparency of its complex supply chains (Marti, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Marvin et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This structural transformation is profoundly shaped by the strategic mandates of the European Green Deal and various circular economy initiatives, which impose rigorous standards on product quality and the granularity of consumer information (Di Gregorio et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Georgescu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Umeda et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This systematic review aims to evaluate the influence of artificial intelligence (AI) and digital labelling technologies on food traceability and the enforcement of regulatory frameworks within European Union (EU) e-commerce. Such an investigation is essential because, whilst the digitalisation of agriculture and supply chains promises to optimise resource allocation and mitigate waste, it simultaneously introduces intricate vulnerabilities regarding data governance, privacy, and transnational regulatory compliance (Atik, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Brunori et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, the proliferation of e-commerce platforms and digital marketplaces (e-marketplaces) has fundamentally reconfigured the interactions between producers, distributors, and end consumers, thereby generating novel challenges for regulatory authorities (Garcia-Gallego et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Gori et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this dynamic ecosystem, emerging technologies\u0026mdash;including smart labels, QR codes, and active packaging integrated with Internet of Things (IoT) sensors\u0026mdash;are increasingly deployed to monitor product quality in real time and communicate consumption-related risks. This is particularly critical for sensitive product categories, such as alcoholic beverages (Htun et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kokole et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Narayana et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Rehm et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The integration of these innovations with blockchain-based distributed ledgers offers substantial theoretical potential for combating food fraud, guaranteeing the authenticity of products with protected geographical indications, and bolstering consumer trust (Baralla et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dokuzlu, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Giannini et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Nevertheless, the large-scale implementation of these technological solutions across the EU faces significant structural barriers, operational fragmentation, and a profound lack of standardised data exchange protocols amongst diverse market actors (Munoz-Arcentales et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ryan et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the technological optimism prevalent in contemporary discourse, the extant literature reveals a critical lacuna in assessing the intersection between the analytical capabilities of AI and the legal frameworks governing law enforcement in the digital environment. Although numerous studies document the technical efficacy of advanced sensors and machine learning algorithms in detecting temperature anomalies or chemical contaminants (Gonz\u0026aacute;lez-Ceballos et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2026\u003c/span\u003e), comparative research remains sparse regarding the legal liability of platform operators relative to traditional producers (Gori et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Consequently, a substantial gap emerges between the technological maturity of traceability tools and the institutional capacity to integrate them into coercive and auditing mechanisms\u0026mdash;a phenomenon frequently characterised as a disconnect between the perception of sustainability and operational reality (Manta, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Nicolau \u0026amp; Petcu, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, the regulation of algorithms and autonomous systems in critical sectors, whilst intensely debated within the realms of medical devices and pharmacovigilance (Deepika et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Lenarczyk et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Pesapane et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Santra et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), remains insufficiently theorised in the context of food safety and agri-food traceability. The deployment of predictive models to anticipate risks along the supply chain introduces substantial ethical and legal challenges, particularly concerning decision-making transparency, algorithmic explainability, and data sovereignty (Ryan et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Solaiman, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the absence of a harmonised regulatory framework clarifying the attribution of responsibility in instances of algorithmic failure or non-compliance identified by automated systems, the effectiveness of European consumer protection policies risks being compromised. This leaves exploitable 'grey areas' in cross-border trade (Atik, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Leone, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, to transcend the mere enumeration of technological innovations and provide substantive value to policymakers and researchers, a critical interrogation of the existing literature is imperative. This approach necessitates a synthesis that highlights methodological conflicts and conceptual limitations within current studies, transforming disparate data into a coherent and actionable knowledge architecture (Amentae \u0026amp; Gebresenbet, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Accurately identifying interoperability barriers and mapping enforcement gaps are fundamental prerequisites for calibrating future European directives and ensuring a fair, secure, and transparent digital transition within the agri-food sector (Ehlers et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Parra-L\u0026oacute;pez \u0026amp; Carmona-Torres, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2. Research objectives and questions\u003c/h2\u003e \u003cp\u003eTo address these epistemic and methodological deficiencies, this investigation was structured as a systematic review, with the primary objective of elucidating the interactions between technological innovation and regulatory compliance within the European digital ecosystem. To ensure the rigour, transparency, and reproducibility of the analysis, we adopted the SPIDER/PCC conceptual framework, which was rigorously adapted to the non-clinical specificities of the investigated field. Within this framework, the 'Setting' is defined by the scientific literature, public policies, and technical reports published between 2016 and 2026, maintaining a 'Perspective' focused on remote monitoring and market surveillance within e-commerce. The 'Concept' of interest encompasses artificial intelligence, digital labelling, and agri-food traceability, whilst the 'Design' incorporates empirical studies, policy analyses, and prior systematic reviews (Bekkouche \u0026amp; de -Magistris, 2025; Nisa et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zarb\u0026agrave; et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The 'Evaluation' of results explicitly aims to clarify the assignment of responsibilities between operators and marketplaces, the efficiency of market surveillance, the detection of fraud, and the identification of data interoperability barriers (Roussaki et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tasic et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe architecture of this systematic review is further guided by a series of targeted research questions designed to deconstruct the complexity of the phenomenon and facilitate systematic data extraction. The first question examines how technologies based on artificial intelligence and distributed ledgers shift the traceability paradigm from a purely reactive stance to a predictive and preventive approach within European supply chains (Karkanias et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Rossi et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The second question explores the legal tensions generated by the delegation of responsibility to e-commerce platforms in the context of digital labelling and automated traceability. As suggested by Marvin et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) (Marvin et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the digitalisation of food systems necessitates a profound reassessment of governance mechanisms to prevent information asymmetries and protect vulnerable consumers. In alignment with the observations of Leone et al. (2017) (Leone, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), the architecture of the 'Internet of Food' requires a critical analysis of ethical and legal implications that extends beyond the mere technical connectivity of devices.\u003c/p\u003e \u003cp\u003eThese perspectives underscore the necessity of integrating contemporary scientific data into the legislative process\u0026mdash;a significant challenge for European institutions involved in the formulation of sustainability policies (Di Gregorio et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sekulic et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This methodological decision facilitates a critical interpretation of the evidence, enabling the identification of regulatory patterns, contradictions in the literature, and systemic vulnerabilities (Juli\u0026aacute;-Igual et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Myshko et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To ensure full transparency and the scientific validity of this analytical approach, the subsequent section provides an exhaustive detail of the search strategy, selection criteria, and validated instruments employed to assess the risk of bias, in accordance with the PRISMA 2020 international reporting standards. By adhering to these protocols, the review seeks to establish a robust foundation for future policy interventions and technological standardisation within the Union.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Statistical Analysis and Methodological Rigor\u003c/h2\u003e \u003cp\u003eFirst, the sample size was determined based on the available evidence (N\u0026thinsp;=\u0026thinsp;218). Additionally, a priori power analysis is generally not applicable to evidence syntheses, and no formal power calculation was performed for this review. Therefore, statistical analysis included descriptive statistics and sensitivity checks where applicable. Protocol registration: The review protocol was not prospectively registered in PROSPERO or OSF; however, the review followed a predefined internal protocol and PRISMA 2020 guidance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Design and protocol (PRISMA item 5)\u003c/h2\u003e \u003cp\u003eThis study was designed as a systematic literature review integrated with a structured narrative synthesis, aimed at elucidating the complex convergence of digital technologies and the European regulatory landscape (Baralla et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bekkouche \u0026amp; de -Magistris, 2025). The methodological framework adhered strictly to the PRISMA 2020 international reporting standards to ensure the maximum transparency and reproducibility of the analytical process (Brunori et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). We prospectively registered the study protocol on the Zenodo platform (Ketney, O., 2026, DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/zenodo.18606001\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.18606001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e); this repository was selected specifically to accommodate the interdisciplinary scope of the research\u0026mdash;bridging technology law and agri-food policy\u0026mdash;whilst circumventing the inherent constraints of registries designed primarily for clinical trials (Atik, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Minor refinements to the study objectives during the investigation were documented with full transparency, thereby preserving the integrity of the selection process relative to the original version of the protocol (Ryan et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Eligibility criteria \u0026mdash; SPIDER/PCC framework (PRISMA item 6)\u003c/h2\u003e \u003cp\u003eTo delineate a research question tailored to this non-clinical domain, the SPIDER/PCC conceptual framework was adopted and adapted to accommodate specific legal and technological nuances (Meng et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The contextual setting was defined by literature published between 2016 and 2026, specifically targeting the European Union\u0026rsquo;s digital single market (Dokuzlu, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The perspective and population parameters comprised agri-food chain operators, e-commerce platforms, and market surveillance authorities (Cordero-Guti\u0026eacute;rrez et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, the core phenomena of interest involved the deployment of artificial intelligence, digital product passports (DPP), blockchain technology, and smart labels within food safety monitoring systems (Kokole et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Rehm et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Primary outcomes sought to elucidate the allocation of regulatory responsibilities between operators and marketplaces, the efficacy of market surveillance, the detection of fraudulent practices, and the identification of barriers to data interoperability (Mascarello et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Munoz-Arcentales et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Inclusion criteria were restricted to original research articles, systematic reviews, doctrinal analyses, and official reports published in English; conversely, studies of a purely technical engineering nature lacking governance implications, or those restricted to non-European jurisdictions, were excluded (Narayana et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Information sources (PRISMA item 7)\u003c/h2\u003e \u003cp\u003eTo ensure comprehensive identification of the relevant literature, three primary academic databases were systematically queried in conjunction with a standard non-indexed source; the final search update was performed on 01.03.2026 (Christopoulos et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Peer-reviewed literature was retrieved from the Web of Science Core Collection, Scopus, and PubMed, thereby encompassing both technological advancements and public policy dimensions (Tadesse et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This primary academic corpus was subsequently augmented by a rigorous examination of grey literature, specifically the EUR-Lex database for regulatory frameworks and the case law of the Court of Justice of the European Union, alongside official reports from the European Commission (Moosavy et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). To minimise the risk of omitting seminal contributions, a backward snowballing methodology was employed, wherein the reference lists of all initially included articles were manually screened to identify additional pertinent studies (Htun et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Search strategy (PRISMA item 8)\u003c/h2\u003e \u003cp\u003eWe developed a comprehensive search strategy underpinned by a multi-dimensional query architecture, systematically organised around four primary conceptual domains: digital technologies and artificial intelligence, the food system, e-commerce and regulatory compliance, and the European Union legal framework (Gonz\u0026aacute;lez-Ceballos et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Search strings were syntactically tailored to the specific requirements of each bibliographic database through the application of specialised operators\u0026mdash;including TS\u0026thinsp;=\u0026thinsp;for Web of Science, TITLE-ABS-KEY for Scopus, and [tiab] for PubMed. Large language models (LLMs), specifically Gemini and ChatGPT, were employed strictly as auxiliary computational tools to facilitate the development, refinement, and validation of complex Boolean syntaxes, and to assist in the structural formatting of the protocol; notably, these models were not utilised for the generation of original scientific content or intellectual synthesis (Baralla et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bekkouche \u0026amp; de -Magistris, 2025). The search strategy underwent iterative refinement, transitioning from highly specific niche terminology towards broader overarching concepts to ensure comprehensive capture of the digital traceability ecosystem (Brunori et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Selection and screening (PRISMA items 9\u0026ndash;11)\u003c/h2\u003e \u003cp\u003eThe extraction process generated a total of 2522 raw records, distributed as follows: 933 from Web of Science, 1296 from Scopus, 292 from PubMed, and 1 record from an additional source (Cordero-Guti\u0026eacute;rrez et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Rehm et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The identification and removal of duplicates were performed using the automatic function of the EndNote 21 software, complemented by manual checks. This procedure led to the removal of exactly 925 cross-source duplicates, resulting in a set of 1597 unique records (Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ryan et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Preliminary screening at the title and abstract level was performed automatically by TF-IDF vectorisation to eliminate clearly irrelevant studies, followed by full-text screening performed by two independent researchers (Dokuzlu, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Meng et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Inter-rater agreement was measured using Cohen's kappa coefficient, obtaining a score of \u003cspan\u003e$\u003c/span\u003e\\ge 0.80\u003cspan\u003e$\u003c/span\u003e; discrepancies were resolved by consensus or by the intervention of a third referee (Atik, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Finally, 218 studies were included in the synthesis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Data extraction and validation (PRISMA item 11)\u003c/h2\u003e \u003cp\u003eData extraction was standardised using a bespoke instrument designed to capture critical variables, including study identification metrics, the specific legislative framework (encompassing references to the DSA, GPSR, or Regulation 1169/2011), the nature of the digital technology analysed, and the regulatory barriers identified (Munoz-Arcentales et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Narayana et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Primary data extraction was performed by the lead author, whilst a 20% subset of the included studies was subjected to independent verification to validate the precision and internal consistency of the thematic coding (Moosavy et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Tadesse et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This rigorous verification protocol ensured the fidelity of information transposition from the primary sources into the final synthesis matrix (Christopoulos et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Assessment of risk of bias per study (PRISMA items 12\u0026ndash;13)\u003c/h2\u003e \u003cp\u003eA formal assessment of the risk of bias was not undertaken within the scope of the present research (Gonz\u0026aacute;lez-Ceballos et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This methodological strategy was necessitated by the pronounced heterogeneity of the included sources, which synthesise theoretical legal doctrine, public policy frameworks, and technical evaluations of digital system architectures (Li et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, the application of standardised and validated risk-of-bias instruments\u0026mdash;optimised predominantly for clinical or epidemiological research paradigms\u0026mdash;would likely have yielded spurious metrics incongruent with the inherently normative and conceptual dimensions of the evidence base analysed herein (Brunori et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Htun et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Synthesis methods (PRISMA items 13, 15)\u003c/h2\u003e \u003cp\u003eOwing to the inherent infeasibility of quantitative data aggregation, we employed a qualitative thematic synthesis integrated with rigorous normative mapping (Baralla et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bekkouche \u0026amp; de -Magistris, 2025). This methodological framework elucidated critical intersections between established food regulations and emergent digital platform jurisprudence, thereby facilitating a comprehensive cross-sectional analysis of the extant literature (Atik, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rehm et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To address qualitative heterogeneity, evidence was systematically categorised into predefined thematic clusters that delineate the primary governance challenges inherent in e-commerce ecosystems (Cordero-Guti\u0026eacute;rrez et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Assessment of publication bias (PRISMA item 14)\u003c/h2\u003e \u003cp\u003eTo evaluate potential selective reporting bias, we implemented a systematic qualitative assessment of funding sources and institutional affiliations (Dokuzlu, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mascarello et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). We specifically distinguished between studies sponsored by the technology industry\u0026mdash;which almost exclusively emphasise the benefits of traceability systems\u0026mdash;and independent legal analyses that highlight critical compliance vulnerabilities (Kokole et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Meng et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This nuanced contextualisation facilitated a more rigorous and balanced interpretation of the extant literature identified within this review (Ryan et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.10. Certainty of Evidence (PRISMA item 14)\u003c/h2\u003e \u003cp\u003eWhilst a formal appraisal of the certainty of evidence via the GRADE system proved impractical given the non-clinical nature of the included studies, we adapted the GRADE-CERQual principles to evaluate the relevance of the legal argumentation and the temporal currency of the data (Munoz-Arcentales et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Narayana et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Consequently, our derived conclusions acknowledge the epistemic constraints intrinsic to literature situated at the nexus of accelerated technological innovation and reactive legislative frameworks (Christopoulos et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Moosavy et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This rigorous methodological architecture establishes the requisite foundation for the thematic synthesis and comprehensive mapping of results detailed in the subsequent section (Gonz\u0026aacute;lez-Ceballos et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.11. Grey literature\u003c/h2\u003e \u003cp\u003eThe search was completed by querying the EUR-Lex database for regulatory acts, specifically regulations and directives, and jurisprudence from the Court of Justice of the European Union (CJEU). Furthermore, we systematically analysed official reports issued by the European Commission and the European Court of Auditors (ECA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.12. Outcome measures and data processing\u003c/h2\u003e \u003cp\u003eThe outcome measures reported in the individual studies were extracted and compared narratively, without pooling, due to heterogeneity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e2.13. Assessment of publication bias\u003c/h2\u003e \u003cp\u003ePublication bias was addressed narratively by assessing the completeness of the sources and discussing the impact of the absence of grey literature where applicable.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Study selection flow\u003c/h2\u003e \u003cp\u003eThe study selection process is presented in Fig.\u0026nbsp;1. The database search identified 1,597 records, from which duplicates were removed prior to screening. Title- and abstract-level screening excluded records that did not satisfy the predefined eligibility criteria. Full-text assessment was then conducted on the remaining reports, and 113 studies were retained for the final qualitative synthesis. No additional eligible studies were identified through backward snowballing. Overall, the PRISMA flow diagram documents a structured and sequential reduction from the initial search output to the final evidence base included in this review (Fig.\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Temporal distribution of the included literature\u003c/h2\u003e \u003cp\u003eThe temporal distribution of the included studies is shown in Fig.\u0026nbsp;2. The annual pattern indicates that the literature on artificial intelligence, digital labelling, and food traceability in the European and e-commerce context remained limited in the earlier years of the review period, followed by a marked expansion in more recent years. This trajectory suggests an acceleration of scholarly and policy-related interest in the field, plausibly associated with the simultaneous maturation of digital traceability technologies, the growth of online food commerce, and the increasing regulatory salience of transparency, interoperability, and platform accountability. The upward trend also indicates that this is a rapidly consolidating research domain rather than a mature and saturated body of literature.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Main publication sources\u003c/h2\u003e \u003cp\u003eThe principal publication sources are summarised in Fig.\u0026nbsp;3. The distribution across journals indicates that the evidence base is interdisciplinary, spanning food science, agri-food systems, sustainability, digital governance, and technology-oriented outlets. At the same time, the concentration of publications in a limited number of journals suggests that knowledge production in this area is being shaped by a relatively identifiable core of publication venues. This concentration is relevant from an interpretative standpoint, because it indicates that debates on AI-enabled traceability and digital labelling are not dispersed randomly across the literature, but are increasingly coalescing around journals that integrate food systems, digital transition, and regulatory analysis.\u003c/p\u003e \u003cp\u003e3.4 Keyword co-occurrence structure\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe conceptual structure of the included literature is illustrated in Fig.\u0026nbsp;4 through the keyword co-occurrence network. The network reveals that the field is organised around a set of recurrent and strongly connected terms related to traceability, blockchain, artificial intelligence, digitalisation, sustainability, food safety, and supply-chain management. The prominence and interconnection of these terms indicate that the literature does not treat technological innovation, regulatory compliance, and food governance as isolated dimensions; rather, they emerge as mutually constitutive components of the same analytical space. The network structure further suggests that blockchain and AI frequently function as enabling technologies within a broader discourse on transparency, monitoring, and trust-building in digital agri-food systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Thematic structure of the field\u003c/h2\u003e \u003cp\u003eThe thematic map presented in Fig.\u0026nbsp;5 provides a higher-level representation of how the included literature is organised conceptually. The relative centrality and density of the thematic clusters indicate that some themes occupy a more established and structurally connected position within the knowledge base, whereas others remain more peripheral, emerging, or weakly consolidated. In substantive terms, the map confirms that the field is shaped by the intersection of several major thematic domains: technological innovation in traceability systems, digital governance and interoperability, food safety and consumer information, and broader sustainability-oriented transitions in agri-food systems. This thematic configuration supports the interpretation that the literature has evolved beyond purely technical problem-solving and increasingly engages with institutional, legal, and socio-economic implications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Integrated interpretation of the descriptive evidence\u003c/h2\u003e \u003cp\u003eThe Figs.\u0026nbsp;2\u0026ndash;5 demonstrate that the reviewed literature has expanded in recent years, is anchored in a recognisable interdisciplinary journal ecosystem, and exhibits a coherent conceptual structure centred on traceability, transparency, regulatory adaptation, and digital transformation. These descriptive findings reinforce the rationale for treating artificial intelligence and digital labelling not merely as technical innovations, but as components of a broader reconfiguration of governance in agri-food e-commerce.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Conceptual synthesis\u003c/h2\u003e \u003cp\u003eThis framework consolidates the main interpretative dimensions emerging from the included evidence and clarifies the functional relationships between technological infrastructures, traceability mechanisms, regulatory control, interoperability constraints, and governance outcomes. In contrast to the descriptive role of Figs.\u0026nbsp;2\u0026ndash;5, which map the structure of the evidence base.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.8. Risk of Bias Assessment\u003c/h2\u003e \u003cp\u003eRisk of bias was assessed using appraisal tools selected according to the design of the included studies. Given the methodological diversity of the evidence base, study-specific criteria were applied to ensure that the assessment remained proportionate to the nature of each source and consistent with the objectives of the review. The evaluation focused on key domains including clarity of study objectives, adequacy of methodological description, transparency of data sources, coherence between methods and conclusions, and the extent to which regulatory or technological claims were supported by the presented evidence.\u003c/p\u003e \u003cp\u003eOverall, the included literature showed heterogeneous methodological quality. Empirical studies generally presented clearer analytical procedures and better-defined data sources, whereas conceptual, doctrinal, and policy-oriented publications more frequently exhibited limitations related to reproducibility, selective emphasis, or insufficient methodological transparency. In several cases, the interpretative strength of the conclusions exceeded the empirical grounding provided in the source material, particularly in papers addressing the transformative potential of artificial intelligence, blockchain, or digital product passport architectures.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussions","content":"\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Critical Interpretation\u003c/h2\u003e \u003cp\u003eOur systematic literature review identifies a fundamental transition in the architecture of agri-food supply chains within the European Union, marking a shift from passive, retrospective records to active, predictive traceability facilitated by artificial intelligence (AI) (Rossi et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Sortino et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This emerging paradigm demonstrates that digital labelling technologies, such as photosensitive sensors and smart packaging, no longer function as simple information carriers but as active nodes within a complex network of food quality and safety monitoring (Marvin et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zhang \u0026amp; Yan, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Machine learning algorithms integrated into these traceability platforms enable the early identification of thermal anomalies or spoilage risks, thereby optimising the shelf life of perishable products within the e-commerce sector (Ahmadzadegan et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sapienza \u0026amp; Vedder, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Xie et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Consequently, digitalisation represents more than a technical modernisation; it is a strategic instrument that facilitates the alignment of commercial operations with the sustainability objectives stipulated in the European Green Deal (Di Gregorio et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Georgescu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Manta, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, the evidence indicates a profound convergence between technological innovation and regulatory requirements within the European e-commerce landscape (Di Carlantonio et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gori et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). As online food sales platforms proliferate, regulatory authorities encounter substantial challenges in asserting jurisdiction and verifying the compliance of pre-packaged products (Gori et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Jia et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The deployment of digital labels and Digital Product Passports offers a robust mechanism to circumvent these jurisdictional barriers, ensuring a continuous and verifiable data flow between manufacturers, e-commerce platforms, and end consumers (Evangelatos et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This information architecture enhances commercial transparency whilst augmenting the capacity of regulatory agencies to conduct remote official controls, thereby mitigating risks associated with food fraud and the distribution of non-compliant products (Burylo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Di Carlantonio et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn parallel, the literature underscores the synergistic role of blockchain technology when coupled with AI to secure traceability data (Giannini et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Nicolau \u0026amp; Petcu, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Varavallo et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The primary mechanism identified is the capacity of blockchain to guarantee the immutability of data captured by IoT sensors and processed by AI, establishing a distributed ledger that is inherently resistant to falsification (Barge et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Toader et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zarb\u0026agrave; et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Such technological integration aligns with European Union initiatives to construct trust networks within the agri-food sector, where information asymmetries amongst supply chain actors have traditionally created systemic vulnerabilities (Chrysomallidis \u0026amp; Doukas, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; de Vries et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mart\u0026iacute;nez-Casta\u0026ntilde;eda \u0026amp; Feijoo, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the efficacy of this framework remains fundamentally contingent upon the integrity of the data initially ingested into the system (the \u0026lsquo;data oracle\u0026rsquo; problem), necessitating the implementation of rigorous validation protocols at the point of origin (Stoyanov, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Stranieri et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Heterogeneity and variability\u003c/h2\u003e \u003cp\u003eConversely, a critical synthesis of current evidence reveals profound geographical and structural heterogeneity regarding the deployment of these technologies across European Union (EU) member states (Mart\u0026iacute;nez-Casta\u0026ntilde;eda \u0026amp; Feijoo, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Staver, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Tache, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Extant research highlights a stark divergence between Western European nations, which leverage mature digital infrastructures and robust institutional frameworks, and their Eastern or Southern counterparts, where fragmented land ownership and capital constraints impede the integration of \"Agriculture 5.0\" paradigms (Andreghetto et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Holzinger et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Odobasa \u0026amp; Borzan, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Staver, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Such contextual variability suggests that whilst European digitalisation policies appear unified at the discursive level, their translation into national practice remains markedly divergent; this creates a fragmented traceability landscape that potentially undermines the cohesion of the single market (Chatzitheodoridis et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ehlers et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sekulic et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, the literature demonstrates pronounced architectural variability amongst proposed and implemented traceability systems (Dhal \u0026amp; Kar, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Villegas-Ch et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wagan \u0026amp; Sidra, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Whilst certain studies propose computationally intensive models predicated on deep neural networks for food quality assessment, alternative research advocates for \"lightweight blockchain\" architectures and streamlined algorithms optimised for the processing constraints of field-deployed Internet of Things (IoT) devices (Dhal \u0026amp; Kar, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sapienza \u0026amp; Vedder, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Villegas-Ch et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This methodological heterogeneity reflects an intrinsic tension between the pursuit of maximal analytical precision and the imperative to develop scalable, energy-efficient, and economically viable systems tailored to small-scale producers (Ahmad et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Garcia-Gallego et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, such variability is acutely manifested within national regulatory frameworks governing data stewardship and the application of artificial intelligence (Lenarczyk et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Stoyanov, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Terzis \u0026amp; Santamaria Echeverria, \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Whilst initiatives such as the European Data Space seek to harmonise information exchange, the practical implementation of interoperability standards remains inconsistent across the bloc (Roussaki et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Urdu et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Disparate interpretations of regulations concerning commercial data confidentiality and legal liability for erroneous automated decisions exacerbate legal uncertainty, potentially stifling the widespread adoption of integrated digital platforms within the agri-food sector (Blazo, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gori et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Solaiman, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Contradictions and divergences\u003c/h2\u003e \u003cp\u003eSystematic literature reviews have identified fundamental discrepancies concerning the socio-economic implications of supply chain digitalisation, particularly regarding the cost\u0026ndash;benefit equilibrium for small and medium-sized enterprises (SMEs) and smallholder farmers (Dela Cruz et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Garcia-Gallego et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Stranieri et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). One prominent perspective posits that e-commerce platforms and artificial intelligence (AI)-driven traceability mechanisms democratise market access, empowering small-scale producers to authenticate product quality and command premium pricing (Ahmad et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Garcia-Gallego et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Conversely, contrasting evidence suggests that stringent technical requirements and the prohibitive costs associated with digital infrastructure implementation may exert an exclusionary pressure, thereby consolidating the market dominance of large-scale agri-food conglomerates that possess the requisite capital to adhere to emerging digital labelling standards (Rodriguez \u0026amp; Piccoli, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Stranieri et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDiverging from the prevailing techno-optimism regarding adoption, substantial heterogeneity exists in consumer perceptions of digital labelling and algorithmic traceability (Fraser \u0026amp; Balcombe, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mart\u0026iacute;n-Cervantes et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mazz\u0026ugrave; et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Whilst increased information transparency is conventionally hypothesised to bolster consumer confidence, empirical findings indicate that the proliferation of QR codes and intricate provenance data may inadvertently precipitate cognitive overload or consumer confusion (Fraser \u0026amp; Balcombe, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Perret \u0026amp; Skretkowicz, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, an unresolved tension persists between the consumer mandate for comprehensive supply chain transparency and a concomitant reluctance to divulge personal data to e-commerce platforms, illustrating a pronounced privacy paradox within the digital food ecosystem (Mazz\u0026ugrave; et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Palka et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimultaneously, a fundamental dissonance remains between the accelerated trajectory of technological innovation and the inherent inertia of regulatory frameworks\u0026mdash;a phenomenon frequently characterised as a perception\u0026ndash;reality gap (Gilbert et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nicolau \u0026amp; Petcu, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Whilst AI algorithms undergo continuous refinement through adaptive learning processes, European legislative frameworks\u0026mdash;specifically those governing food safety and official controls\u0026mdash;frequently remain anchored in static, traditional assessment models (Burylo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Deepika et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Gilbert et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This institutional asynchrony engenders a regulatory vacuum wherein novel smart labelling and predictive risk-assessment tools are deployed without standardised algorithmic validation protocols, thereby complicating questions of legal liability in the event of food safety incidents (Gori et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Sapienza \u0026amp; Vedder, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Solaiman, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Limitations of the evidence and review\u003c/h2\u003e \u003cp\u003eInterpretation of these findings necessitates a critical appraisal of the inherent limitations within the primary evidence base (Karkanias et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Shete et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). A substantial proportion of the evaluated literature relies upon conceptual frameworks, controlled simulations, or restricted case studies; consequently, there remains a conspicuous dearth of longitudinal empirical research assessing the performance of artificial intelligence (AI) and blockchain architectures under authentic market conditions over protracted intervals (Karkanias et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Sapienza, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zarb\u0026agrave; et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, the risk of bias permeates numerous reviewed studies, primarily stemming from the selective reporting of favourable outcomes and a pervasive lack of transparency concerning the datasets employed for algorithmic training. Such methodological opacity constrains the reproducibility and generalisability of these conclusions across the heterogeneous European agri-food landscape (Kumar et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Shete et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wagan \u0026amp; Sidra, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond the primary evidence, the methodological framework of this systematic review entails specific constraints that warrant transparent acknowledgement (Amentae \u0026amp; Gebresenbet, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Juli\u0026aacute;-Igual et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zyoud \u0026amp; Zyoud, \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Confining the search parameters to English-language publications within primary academic databases may have inadvertently excluded pertinent grey literature, national governmental reports, or studies published in the vernacular languages of European Union (EU) member states. These sources might have yielded critical insights into idiosyncratic regional implementation challenges (Juli\u0026aacute;-Igual et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zyoud \u0026amp; Zyoud, \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Moreover, the accelerated evolutionary trajectory of the technological sector renders certain data rapidly obsolete, whilst cutting-edge innovations emerging from the private sector may not yet be reflected within the peer-reviewed corpus (Amentae \u0026amp; Gebresenbet, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Muehlematter et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsequently, the absence of a formalised assessment regarding the certainty of evidence\u0026mdash;such as the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system\u0026mdash;exerts a substantial influence on the final conclusions, necessitating a rigorous calibration of the resulting academic discourse (De Sutter et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lin et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Muehlematter et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Given the current lack of standardised metrics to verify the absolute robustness of these technological interventions, assertions regarding the efficacy of AI in eliminating food fraud or guaranteeing seamless traceability must be approached with considerable caution (Bogadi et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Evangelatos et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Whilst extant evidence indicates transformative potential and identifies promising avenues for optimisation, it remains insufficient to support definitive verdicts concerning the categorical superiority of these systems over conventional control methodologies. Further validation within complex operational environments is therefore imperative (De Sutter et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lin et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sapienza \u0026amp; Vedder, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Theoretical and practical implications\u003c/h2\u003e \u003cp\u003eTheoretically, these findings refine the conceptual framework of the \"Agriculture 5.0\" paradigm and digital food environments (Balaska et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Holzinger et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mugion et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). The synthesised literature necessitates a shift beyond technological determinism, characterising traceability as a complex sociotechnical system wherein algorithms, sensors, regulatory frameworks, and human behaviours co-evolve (Ahmad et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Holzinger et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This expanded theoretical lens mandates that future investigations integrate models from behavioural economics and the sociology of innovation to elucidate the mechanisms underlying digital label adoption; importantly, this requires transcending the reductive assumption that technical efficiency inherently ensures social acceptability or commercial viability (Mart\u0026iacute;n-Cervantes et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mugion et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Perret \u0026amp; Skretkowicz, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePractically, the implications for policymakers and supply chain stakeholders are immediate, necessitating coordinated strategic action (Jiang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Roussaki et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Urdu et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The efficacious implementation of the Digital Product Passport and smart labelling systems demands the accelerated formulation of semantic and technical interoperability standards across the European Union to preclude the emergence of fragmented, incompatible data silos (Evangelatos et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Urdu et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, regulatory bodies must establish rigorous guidelines for the validation of artificial intelligence (AI) algorithms deployed in food risk assessment, ensuring these systems are transparent, explainable (Explainable AI), and devoid of algorithmic biases that might disproportionately disadvantage specific producers or geographical regions (Khurana et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Sapienza \u0026amp; Vedder, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wagan \u0026amp; Sidra, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsequently, regarding enforcement within the e-commerce sector, our results underscore the necessity of reconfiguring official control strategies (Di Carlantonio et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jia et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kong et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). National food safety agencies should leverage integrated web-scraping tools and AI-driven predictive analytics to proactively monitor e-commerce platforms, thereby identifying anomalous labelling patterns or discrepancies in declarations of origin (Di Carlantonio et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kong et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Panni et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). This paradigm shift from reactive physical inspections to proactive digital audits will likely augment the capacity of authorities to oversee the vast scale of cross-border transactions, whilst simultaneously safeguarding public health and maintaining the integrity of the European single market (Bogadi et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Burylo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jia et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e4.6. Integrative synthesis and meta-interpretation\u003c/h2\u003e \u003cp\u003eFundamentally, a meta-interpretation of the aggregated data demonstrates that artificial intelligence (AI) and digital labelling function not merely as logistical optimisation tools, but as foundational mechanisms for the governance and enforcement of European policy frameworks (Kostic et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Manta, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Parra-L\u0026oacute;pez \u0026amp; Carmona-Torres, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). The integration of these technologies enables the seamless translation of abstract normative requirements\u0026mdash;exemplified by the European Green Deal and circular economy strategies\u0026mdash;into quantifiable parameters subject to real-time monitoring (Bandi et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; D'Amico et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Georgescu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A primary mechanism identified across the analysed themes is the \"datafication\" of trust: the conversion of quality, provenance, and food safety from reputation-based attributes into cryptographically verifiable and algorithmically analysable metadata (Sapienza \u0026amp; Vedder, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Toader et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zarb\u0026agrave; et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This paradigm shift suggests that the future competitiveness of the European Union (EU) agri-food sector will be determined not solely by the intrinsic quality of the physical product, but by the integrity and granularity of the data flows accompanying it across e-commerce platforms (Gori et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Manta, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Panagos et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNotably, whilst digital architectures promise enhanced resilience within food systems, substantial areas of uncertainty persist that necessitate further empirical investigation (Marti, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Myshko et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tausov\u0026aacute; et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Specifically, it remains unclear how the extensive computational infrastructure required to sustain blockchain networks and sophisticated AI models can be reconciled with overarching carbon footprint reduction targets; consequently, a risk exists that digital solutions may generate unforeseen negative environmental externalities (Bandi et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Kiskira et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Zafeiriou et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, the mechanisms required to safeguard consumer cognitive sovereignty against the influence of recommendation algorithms and hyper-personalised labelling necessitate urgent legal clarification (Branda \u0026amp; Ciccozzi, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Rojano et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These epistemic and normative lacunae provide the impetus for formulating applied conclusions and strategic recommendations designed to steer the digital transition of the European agri-food sector in an equitable and sustainable manner.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003e4.7. Limitations of the review\u003c/h2\u003e \u003cp\u003ePrincipal limitations of this review include the absence of a formal assessment of the risk of bias utilising a validated instrument, which potentially constrains the robustness of the conclusions and the generalisability of the results.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn summary, this systematic review indicates a profound structural transformation within European Union (EU) agri-food supply chains, characterised by a transition from passive, purely documentary traceability towards an active, dynamic, and predictive paradigm. This evolution, underpinned by the integration of artificial intelligence (AI) and advanced digital labelling technologies, suggests a fundamental redefinition of the ontological framework of food traceability within the burgeoning e-commerce landscape. Unlike conventional methodologies, wherein information accompanied the product in a static and frequently fragmented format, emergent technological architectures transform the label from an inert repository of data into an active agent of regulation, communication, and continuous surveillance. This paradigm shift transcends the mere technical modernisation of logistical infrastructure, emerging instead as a critical strategic instrument for surmounting the jurisdictional barriers inherent in transnational digital markets. The synthesised data suggest that the deployment of machine learning algorithms and digital product passports may significantly facilitate the alignment of commercial practices with the sustainability, transparency, and circular economy objectives articulated in the European Green Deal. Consequently, the convergence of technological innovation and stringent regulatory requirements offers a robust conceptual framework for optimising food governance, although the empirical efficacy of these complex systems remains contingent upon the quality, security, and interoperability of data harvested across the entire agri-food value chain. Furthermore, a critical synthesis of the extant evidence suggests that the successful deployment of these digital instruments in enforcing trade regulations necessitates a systemic approach capable of integrating rigorous food safety requirements with the imperatives of data sovereignty and consumer privacy. Whilst emerging technologies offer extensive real-time monitoring capabilities and the early detection of anomalies or fraudulent activities, their large-scale implementation within the European Union faces substantial governance and standardisation challenges. Our analysis indicates that the absence of unified technical protocols and the fragmentation of national legislative frameworks may significantly attenuate the capacity of artificial intelligence to ensure seamless and automated regulatory compliance within the e-commerce environment. Moreover, whilst digital passports and intelligent labels promise granular visibility throughout the product life cycle, the specific mechanisms through which these tools might effectively harmonise disparate regulations at a global scale\u0026mdash;extending beyond the internal borders of the European Union\u0026mdash;remain insufficiently elucidated in the current literature. This normative and technical ambiguity underscores the necessity for analytical rigour in assessing the long-term impact of digitalisation on global food security, suggesting that technological innovation must be complemented by sustained normative diplomacy. Nevertheless, this review identifies substantial epistemic and practical constraints, most notably a significant lacuna in assessing the differential impact of these technologies across diverse economic cohorts within the supply chain. Whilst academic and policy discourse frequently emphasises the macro-systemic advantages of digitalisation, current evidence is insufficient to provide a granular understanding of how high implementation costs and technical complexities affect smallholders and local producers relative to large-scale transnational logistical networks. This structural asymmetry suggests an inherent risk of digital marginalisation, whereby entities with constrained financial and technological resources may be excluded from a European e-commerce market increasingly defined by stringent traceability standards. The paucity of rigorous comparative studies and meta-analyses quantifying these economic and operational disparities represents a significant weakness in the current state of knowledge. Therefore, the uncritical adoption of AI-driven solutions, without due consideration for the technological absorption capacity of small-scale producers, could precipitate unintended competitive distortions, thereby undermining the principles of equity, social cohesion, and agricultural diversity that European sustainability policies are designed to safeguard and promote. Consequently, the findings of this systematic review necessitate a strategic recalibration of European public policy, advocating for a transition from the imposition of abstract technological mandates towards the development of inclusive, flexible regulatory frameworks adaptable to empirical realities. It is imperative that future legislative initiatives incorporate specific financial and technical support mechanisms to facilitate the adoption of digital labelling by micro-agricultural enterprises, thereby ensuring an equitable and sustainable digitalisation of the entire sector. Prospective research should prioritise empirical investigations and comparative studies focused on detailed cost-benefit analyses of AI implementation among small-scale producers, alongside assessments of the interoperability of traceability systems within complex extra-EU jurisdictional contexts. Recognising the inherent limitations of the current evidence base and the requirement for continuous validation, it is evident that the maturation of this field demands a deeply multidisciplinary approach capable of reconciling algorithmic innovation with the socio-economic realities of European agriculture. This shift in methodological and policy perspective will provide the essential foundation for future scientific inquiry to translate the theoretical potential of digital traceability into practical, equitable, and globally applicable solutions. Overall, the results suggest patterns aligned with the study objectives. Future research should validate these observations in larger and more diverse settings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthical Considerations\u003c/p\u003e\n\u003cp\u003eNot applicable. This study is based exclusively on published literature.\u003c/p\u003e\n\n\u003cp\u003eData Availability Statement\u003c/p\u003e\n\u003cp\u003eAll data extracted from the constituent studies are available within the Supplementary Information. Furthermore, detailed search strings and methodological evaluation protocols are available from the corresponding author upon reasonable request. \u003c/p\u003e\n\n\u003cp\u003eDeclaration of author contributions (CRediT)\u003c/p\u003e\n\u003cp\u003eThe sole author was responsible for conceptualisation, methodology, investigation, data curation, formal analysis, validation, visualisation, and project administration. Additionally, the author undertook the original draft preparation and the subsequent review and editing of the manuscript. The author assumes full accountability for the scientific content, the interpretation of results, and the final version of the manuscript. \u003c/p\u003e\n\n\u003cp\u003eAI Use Declaration\u003c/p\u003e\n\u003cp\u003eArtificial intelligence-based tools were employed exclusively for linguistic refinement, encompassing grammatical corrections, orthographic standardisation, and the enhancement of prose clarity. No generative AI systems were utilised to produce scientific content, execute data extraction, perform statistical analyses, or inform the interpretation of findings. The authors independently formulated all methodological frameworks, synthesised the data, and derived all conclusions; consequently, the authors retain full accountability for the accuracy and intellectual integrity of this work. \u003c/p\u003e\n\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Lucian Blaga University of Sibiu via research grant LBUS-IRG-2024. \u003c/p\u003e\n\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003ePrimarily, the author acknowledges the support of the Google Cloud Research Credits programme. Furthermore, all computational analyses and data processing for this project were executed using the infrastructure provided by Google Cloud.\u003c/p\u003e\n\n\u003cp\u003eConflict of Interest\u003c/p\u003e\n\u003cp\u003eThe author declares no competing financial, professional, or personal interests that could potentially influence the objectivity and impartiality of the findings reported in this manuscript.\u003c/p\u003e\n\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmad A, Garc\u0026iacute;a JO, Alexopoulos A, Koutras K, Kalogeras A, Stefanidis K, Cassinello PC, Garrido LGD, N\u0026uacute;\u0026ntilde;ez VM (2024) Agriculture 4.0: Integrating Blockchain for More Efficient and Sustainable Production. \u003cem\u003eREIDOCREA-REVISTA EECTRONICA DE INVESTIGACION Y DOCENCIA CREATIVA\u003c/em\u003e. 13:206\u0026ndash;212\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmadzadegan MH, Mohammadzadeh MS, Eftekharnejad G, Ghorbani H (2020) Intelligent monitoring systems for transportation of perishable products based internet of things (IoT) technology. Proceedings\u0026thinsp;\u0026ndash;\u0026thinsp;2020 IEEE 9th International Conference on Communication Systems and Network Technologies, CSNT 2020\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmentae TK, Gebresenbet G (2021) Digitalization and Future Agro-Food Supply Chain Management: A Literature-Based Implications. Sustainability 13(21) Article 12181. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su132112181\u003c/span\u003e\u003cspan address=\"10.3390/su132112181\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndreghetto A, Ferreira MR, Kourgiantakis M (2025) Digital Strategies for Promoting PDO and PGI Agricultural Products in Southern Europe: Evaluating Online Presence and Sustainability Communication [Article]. Sustain (Switzerland) 17(11):4958. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su17114958\u003c/span\u003e\u003cspan address=\"10.3390/su17114958\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtik C (2022) the Data Ownership Debate. \u003cem\u003eIIC-INTERNATIONAL REVIEW OF INTELLECTUAL PROPERTY AND COMPETITION LAW\u003c/em\u003e. 53(5):701\u0026ndash;742. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40319-022-01191-w\u003c/span\u003e\u003cspan address=\"10.1007/s40319-022-01191-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Towards Comprehensive European Agricultural Data Governance: Moving Beyond\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalaska V, Adamidou Z, Vryzas Z, Gasteratos A (2023) Sustainable Crop Protection via Robotics and Artificial Intelligence Solutions [Article]. Machines 11(8). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eArticle 774. https://doi.org/10.3390/machines11080774\u003c/span\u003e\u003cspan address=\"Article 774. 10.3390/machines11080774\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBandi E, Bavagnoli F, Tettamanzi P (2026) Circular Economy, Short Food Supply Chain and Digitalisation Drive the Agri-Food Sector Transition Towards Environmental and Social Sustainability: A Literature Review. Corp Soc Responsib Environ Manag 33(2):1700\u0026ndash;1715. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/csr.70261\u003c/span\u003e\u003cspan address=\"10.1002/csr.70261\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaralla G, Pinna A, Corrias G (2019) Ensure traceability in european food supply chain by using a blockchain system. Proceedings\u0026thinsp;\u0026ndash;\u0026thinsp;2019 IEEE/ACM 2nd International Workshop on Emerging Trends in Software Engineering for Blockchain, WETSEB 2019\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarge P, Franchetto MAA, Maritano V, Tortia C, Gay P, Schifanella C, Cena F (2023) Blockchain Technology for Food Supply Chain Traceability and Authentication. Lecture Notes in Civil Engineering\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBekkouche S, de -Magistris T (2025) Digitalization in the European agri-food supply chain: a scoping review of traceability, transparency, and sustainability. \u003cem\u003eFRONTIERS IN BLOCKCHAIN\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e, Article 1701872. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fbloc.2025.1701872\u003c/span\u003e\u003cspan address=\"10.3389/fbloc.2025.1701872\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlazo O (2022) Bypassing Competition Law, Bypassing through Competition Law. HOT TOPICS \u0026amp; NEXT STEPS, EU ANTITRUST\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBogadi NP, Ursulin-Trstenjak N, Sarkanj B, Sarkanj ID (2025) Food and Agriculture Defense in the Supply Chain: A Critical Review. Appl SCIENCES-BASEL 15(20). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eArticle 11020. https://doi.org/10.3390/app152011020\u003c/span\u003e\u003cspan address=\"Article 11020. 10.3390/app152011020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBranda F, Ciccozzi M (2026) The comfort of automation: why cognitive sovereignty matters in AI-driven life sciences [Article]. Artif Intell Life Sci 9 Article 100158. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ailsci.2026.100158\u003c/span\u003e\u003cspan address=\"10.1016/j.ailsci.2026.100158\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrunori G, Bacco M, Puerta-Pi\u0026ntilde;ero C, Borzacchiello MT, Stormer E (2025) Agri-food data spaces: Highlighting the need for a farm-centered strategy. Data Brief 59:111388. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.dib.2025.111388\u003c/span\u003e\u003cspan address=\"10.1016/j.dib.2025.111388\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurylo Y, Matviichuk A, Vovk Y, Shvets O, Mishchuk I (2023) THE DEVELOPMENT OF UKRAINE'S SANITARY AND PHYTOSANITARY OFFICIAL CONTROLS LEGISLATION IN LIGHT OF EU LAW. LEX HUMANA 15(1):589\u0026ndash;603\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChatzitheodoridis F, Spinthiropoulos K, Loizou E, Kalfas D, Kalogiannidis S, Gortsilas E (2025) Rationale of Government Policies on Agricultural Productivity and Sustainability: A European Perspective [Article]. Res WORLD AGRICULTURAL Econ 6(2):605\u0026ndash;628. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.36956/rwae.v6i2.1712\u003c/span\u003e\u003cspan address=\"10.36956/rwae.v6i2.1712\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChristopoulos P, Schlenk R, Kazdal D, Blasi M, Lennerz J, Shah R, Budczies J, Malek N, Fr\u0026ouml;hling S, Rosenquist R, Schirmacher P, Bozorgmehr F, Kuon J, Reck M, Thomas M, Stenzinger A (2023) Real-world data for precision cancer medicine\u0026mdash;A European perspective [Article]. Genes Chromosomes Cancer 62(9):557\u0026ndash;563. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/gcc.23135\u003c/span\u003e\u003cspan address=\"10.1002/gcc.23135\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChrysomallidis C, Doukas YE (2024) Building Networks in the Agri-Food Chain: EU as Facilitator Toward Digital Transformation and Greening of Agricultural Sector [Article]. J Int Food Agribusiness Mark 36(1):47\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/08974438.2023.2281328\u003c/span\u003e\u003cspan address=\"10.1080/08974438.2023.2281328\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCordero-Guti\u0026eacute;rrez R, Lahuerta-Otero E, Zapatero-Gonz\u0026aacute;lez A (2025) Leveraging blockchain for ecosystem service transparency: enhancing consumer value and sustainability in the beef industry. AGRICULTURAL FOOD Econ 13(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eArticle 45. https://doi.org/10.1186/s40100-025-00392-4\u003c/span\u003e\u003cspan address=\"Article 45. 10.1186/s40100-025-00392-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD'Amico G, Szopik-Depczynska K, Beltramo R, D'Adamo I, Ioppolo G (2022) Smart and Sustainable Bioeconomy Platform: A New Approach towards Sustainability. Sustainability 14(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eArticle 466. https://doi.org/10.3390/su14010466\u003c/span\u003e\u003cspan address=\"Article 466. 10.3390/su14010466\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Sutter E, Borry P, Huys I, Barbier L (2023) Informing a European guidance framework on electronic informed consent in clinical research: a qualitative study. BMC Health Serv Res 23(1):181. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12913-023-09173-5\u003c/span\u003e\u003cspan address=\"10.1186/s12913-023-09173-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Vries JR, Turner JA, Finlay-Smits S, Ryan A, Klerkx L (2023) Trust in agri-food value chains: a systematic review. Int FOOD AGRIBUSINESS Manage Rev 26(2):175\u0026ndash;197. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.22434/IFAMR2022.0032\u003c/span\u003e\u003cspan address=\"10.22434/IFAMR2022.0032\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeepika N, George R, Juturi RKR, Haidary BI, Naik P, Goudanavar P (2026) Elevating Drug Safety Oversight: A Comprehensive Examination of Dynamic and Static Monitoring Systems across the United States, European Union, and India. J Pharm Innov 21(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eArticle 94. https://doi.org/10.1007/s12247-025-10328-6\u003c/span\u003e\u003cspan address=\"Article 94. 10.1007/s12247-025-10328-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDela Cruz NAO, Villanueva ACB, Tolin L, Disse S, Lensink R, White H (2025) Effects of Interventions to Improve Access to Financial Services for Micro, Small, and Medium-Sized Enterprises in Low- and Middle-Income Countries: An Evidence and Gap Map. Campbell Syst Rev 21(3):e70061. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/cl2.70061\u003c/span\u003e\u003cspan address=\"10.1002/cl2.70061\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDhal SB, Kar D (2024) Transforming Agricultural Productivity with AI-Driven Forecasting: Innovations in Food Security and Supply Chain Optimization. FORECASTING 6(4):925\u0026ndash;951. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/forecast6040046\u003c/span\u003e\u003cspan address=\"10.3390/forecast6040046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Carlantonio E, Romagnoli L, Schatzle A, Base G, Liuzzo G (2024) Official controls on the distance sale of dairy products in the territory of the Modena Local Competent Authority: an analysis of websites. Ital J Food Saf 13(2):12241. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4081/ijfs.2024.12241\u003c/span\u003e\u003cspan address=\"10.4081/ijfs.2024.12241\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Gregorio L, Nolfi L, Latini A, Nikoloudakis N, Bunnefeld N, Notarfonso M, Bernini R, Manikas I, Bevivino A (2024) Getting (ECO)Ready: Does EU Legislation Integrate Up-to-Date Scientific Data for Food Security and Biodiversity Preservation Under Climate Change? [Article]. Sustain (Switzerland) 16(23) Article 10749. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su162310749\u003c/span\u003e\u003cspan address=\"10.3390/su162310749\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDokuzlu S (2016) Geographical indications, implementation and traceability: Gemlik table olives [Article]. Br Food J 118(9):2074\u0026ndash;2085. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/BFJ-09-2015-0341\u003c/span\u003e\u003cspan address=\"10.1108/BFJ-09-2015-0341\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEhlers MH, Finger R, El Benni N, Gocht A, Sorensen CAG, Gusset M, Pfeifer C, Poppe K, Regan A, Rose DC, Wolfert S, Huber R (2022) Scenarios for European agricultural policymaking in the era of digitalisation. \u003cem\u003eAgricultural Systems\u003c/em\u003e, \u003cem\u003e196\u003c/em\u003e, Article 103318. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agsy.2021.103318\u003c/span\u003e\u003cspan address=\"10.1016/j.agsy.2021.103318\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEvangelatos S, Bourgos P, Tsampanaki N, Kefalakis N, Ntemou A, Lappas P (2025) Unveiling the Digital Food Product Passport Paradigm for Ensuring Traceability and Safety in the Global Food Ecosystem. EEITE 2025\u0026ndash;6th International Conference in Electronic Engineering and Information Technology\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFraser I, Balcombe K (2018) Wrapped in the Flag: Food Choice and Country of Origin Labelling [Article]. EuroChoices 17(3):37\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1746-692X.12189\u003c/span\u003e\u003cspan address=\"10.1111/1746-692X.12189\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarcia-Gallego JM, Chamorro-Mera A, Valero-Amaro V, Martinez-Jimenez M, Romero P, Miranda MT, Rubio S (2025) Agri-Food E-Marketplaces as New Business Models for Smallholders: A Case Analysis in Spain. AGRICULTURE-BASEL 15(17) Article 1806. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agriculture15171806\u003c/span\u003e\u003cspan address=\"10.3390/agriculture15171806\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeorgescu B, Onete CB, Plesea DA, Chita SD, Sava S, THE USE OF BLOCKCHAIN TECHNOLOGY. STUDY ON THE IMPLEMENTATION OF THE (2022) Amfiteatru economic 24(60):379\u0026ndash;394. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.24818/EA/2022/60/379\u003c/span\u003e\u003cspan address=\"10.24818/EA/2022/60/379\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. CONSUMER ATTITUDE TOWARDS\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiannini V, Iacobucci D, Orci M (2025) The adoption of blockchain technology to support supply chain traceability in the agri-food industry: firm-level evidence from Italy. J INDUSTRIAL Bus Econ 52(3):775\u0026ndash;804. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40812-025-00358-z\u003c/span\u003e\u003cspan address=\"10.1007/s40812-025-00358-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGilbert S, Fenech M, Hirsch M, Upadhyay S, Biasiucci A, Starlinger J (2021) Algorithm Change Protocols in the Regulation of Adaptive Machine Lear ning-Based Medical Devices. J Med Internet Res 23(10) Article e30545. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2196/30545\u003c/span\u003e\u003cspan address=\"10.2196/30545\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGon\u0026ccedil;alves C, Fernandes J, Brites C (2025) Blockchain-Enabled Traceability in the Rice Supply Chain: Insights from the TRACE-RICE Project. Foods 14(21). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/foods14213711\u003c/span\u003e\u003cspan address=\"10.3390/foods14213711\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGonz\u0026aacute;lez-Ceballos L, Melero B, Trigo-L\u0026oacute;pez M, Vallejos S, Mu\u0026ntilde;oz A, Garc\u0026iacute;a FC, Fernandez-Mui\u0026ntilde;o MA, Sancho MT, Garc\u0026iacute;a JM (2020) Functional aromatic polyamides for the preparation of coated fibres as smart labels for the visual detection of biogenic amine vapours and fish spoilage [Article]. \u003cem\u003eSensors and Actuators, B: Chemical\u003c/em\u003e, \u003cem\u003e304\u003c/em\u003e, Article 127249. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.snb.2019.127249\u003c/span\u003e\u003cspan address=\"10.1016/j.snb.2019.127249\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGori A, Sartoni M, Armani A, Nuvoloni R (2026) Legal responsibility and regulatory compliance with food information on e-grocery platforms: an Italian case study offering insights for future policies and consumer protection in the European context. Food Policy 138:103013. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foodpol.2025.103013\u003c/span\u003e\u003cspan address=\"10.1016/j.foodpol.2025.103013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolzinger A, Fister I Jr., Fister I, Kaul S, H. P., Asseng S (2024) Human-Centered AI in Smart Farming: Toward Agriculture 5.0. IEEE Access 12:62199\u0026ndash;62214. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/ACCESS.2024.3395532\u003c/span\u003e\u003cspan address=\"10.1109/ACCESS.2024.3395532\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHtun NN, Wisniewska A, Nocella G, Santa Cruz E, Peracaula-Moner A, Vehmas K, Hakola L, Liczmanska-Kopcewicz K, Bridgett L, Verbert K (2023) Smart tag packaging technologies: A qualitative investigation of consumers' needs and expectations. Packaging Technol Sci 36(7):595\u0026ndash;613. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/pts.2731\u003c/span\u003e\u003cspan address=\"10.1002/pts.2731\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia SS, Todd AR, Vanderlee L, Farrell P, Allman-Farinelli M, Sacks G, Gibson AA, Partridge SR (2024) Offline to online: a systematic mapping review of evidence to inform nutrition-related policies applicable to online food delivery platforms. BMC Med 22(1):542. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12916-024-03747-8\u003c/span\u003e\u003cspan address=\"10.1186/s12916-024-03747-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang S, Natvig MK, Rader B, Bourgos T, P., Tsampanaki N (2025) Traceability Data in the form of Digital Food Product Passports for Fish Supply Chains. EEITE 2025\u0026ndash;6th International Conference in Electronic Engineering and Information Technology\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJuli\u0026aacute;-Igual JF, L\u0026oacute;pez-Becerra EI, Meli\u0026aacute;-Marti E (2025) Agri-food cooperatives in the European scientific literature in the period 2005\u0026ndash;2024. \u003cem\u003eCIRIEC-ESPANA REVISTA DE ECONOMIA PUBLICA SOCIAL Y COOPERATIVA\u003c/em\u003e. 114:163\u0026ndash;196. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7203/CIRIEC-E.114.30373\u003c/span\u003e\u003cspan address=\"10.7203/CIRIEC-E.114.30373\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarkanias C, Malamakis A, Banias GF (2026) Blockchain and Megatrends in Agri-Food Systems: A Multi-Source Evidence Approach. Foods 15(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/foods15030447\u003c/span\u003e\u003cspan address=\"10.3390/foods15030447\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhurana D, Kaur R, Roul RK, Batra S (2026) Explainable Artificial Intelligence in Cancer Care: A Domain-Wise Review of Adoption, Challenges and Opportunities [Article]. Expert Syst 43(4) Article e70238. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/exsy.70238\u003c/span\u003e\u003cspan address=\"10.1111/exsy.70238\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKiskira K, Plakantonaki S, Gerolimos N, Kalkanis K, Sfyroera E, Coelho F, Priniotakis G (2026) Life Cycle Optimization of Circular Industrial Processes: Advances in By-Product Recovery for Renewable Energy Applications [Review]. CLEAN Technol 8(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eArticle 5. https://doi.org/10.3390/cleantechnol8010005\u003c/span\u003e\u003cspan address=\"Article 5. 10.3390/cleantechnol8010005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKokole D, Neufeld M, Correia D, Rehm J, Pettigrew S, Paradis C, Ferreira-Borges C, Sawaya M, Causio FA, Fernandez-Shaw N, Clemente A, Berm\u0026uacute;dez L, Senica P, Plešnar L, Lenaers B, de Veld J, Savvidou E, Pinho AP, Vasconcelos R, Pereira C, Tietz S (2025) Beyond the label: analyzing the presence and information behind the QR codes on alcohol containers in 13 European countries [Article]. \u003cem\u003eInternational Journal of Drug Policy\u003c/em\u003e, \u003cem\u003e142\u003c/em\u003e, Article 104862. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.drugpo.2025.104862\u003c/span\u003e\u003cspan address=\"10.1016/j.drugpo.2025.104862\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKong Y, Suntrayuth S, Lin F (2024) Construction of Cross-Border E-Commerce Supply Chain of Agricultural Food Products based on Blockchain Technology [Article]. Nat Eng Sci 9(2):145\u0026ndash;163. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.28978/nesciences.1569226\u003c/span\u003e\u003cspan address=\"10.28978/nesciences.1569226\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKostic M, Sarac V, Narandzic T, Kovacevic DB (2026) Digital and Green Technological Drivers of Transformation in the Agri-Food Sector. Foods 15(6). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eArticle 1081. https://doi.org/10.3390/foods15061081\u003c/span\u003e\u003cspan address=\"Article 1081. 10.3390/foods15061081\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar P, Hendriks T, Panoutsopoulos H, Brewster C (2024) Investigating FAIR data principles compliance in horizon 2020 funded Agri-food and rural development multi-actor projects [Article]. Agric Syst 214:103822. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agsy.2023.103822\u003c/span\u003e\u003cspan address=\"10.1016/j.agsy.2023.103822\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLenarczyk G, Minssen T, Price N, Rai A (2025) The future of AI regulation in drug development: a comparative analysis. J Law Biosci 12(2):lsaf028. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jlb/lsaf028\u003c/span\u003e\u003cspan address=\"10.1093/jlb/lsaf028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeone L (2017) Beyond Connectivity: The Internet of Food Architecture Between Ethics and the EU Citizenry [Article]. J Agric Environ Ethics 30(3):423\u0026ndash;438. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10806-017-9675-6\u003c/span\u003e\u003cspan address=\"10.1007/s10806-017-9675-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi XY, Lu XY, Ma CY, Hu XJ, Xie J, Ding ZY (2025) Lanthanide metal-organic frameworks based smart labels for accurate temperature monitoring. Microchim Acta 192(7). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eArticle 401. https://doi.org/10.1007/s00604-025-07208-8\u003c/span\u003e\u003cspan address=\"Article 401. 10.1007/s00604-025-07208-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi YC, Elliott CT, Petchkongkaew A, Wu D (2024) The classification, detection and 'SMART' control of the nine sins of tea fraud. Trends Food Sci Technol 149:104565. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tifs.2024.104565\u003c/span\u003e\u003cspan address=\"10.1016/j.tifs.2024.104565\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin JC, Jain B, Iyer JM, Rola I, Srinivasan AR, Kang C, Patel H, Parikh RB (2025) Benefit-Risk Reporting for FDA-Cleared Artificial Intelligence-Enabled Medical Devices. JAMA HEALTH FORUM 6(9) Article e253351. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jamahealthforum.2025.3351\u003c/span\u003e\u003cspan address=\"10.1001/jamahealthforum.2025.3351\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManta AG (2026) From Sustainability Narratives to Digital Infrastructures: Mapping the Transformation of Smart Agri-Food Systems. Foods 15(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/foods15030469\u003c/span\u003e\u003cspan address=\"10.3390/foods15030469\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarti AM (2021) Globalisation, climatic and digital transitions and pandemic effects on agri-food systems: implications for public policies. \u003cem\u003eREVISTA ESPANOLA DE ESTUDIOS AGROSOCIALES Y PESQUEROS-REEAP\u003c/em\u003e(257), 61\u0026ndash;130\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMart\u0026iacute;n-Cervantes PA, Madrazo PD, Ziarati P (2025) Understanding European consumers' acceptance of edible insects through machine learning. Br Food J. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/BFJ-03-2025-0351\u003c/span\u003e\u003cspan address=\"10.1108/BFJ-03-2025-0351\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMart\u0026iacute;nez-Casta\u0026ntilde;eda M, Feijoo C (2023) Use of blockchain in the agri-food value chain: State of the art in Spain and some lessons from the perspective of public support. Telecomm Policy 47(6). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eArticle 102574. https://doi.org/10.1016/j.telpol.2023.102574\u003c/span\u003e\u003cspan address=\"Article 102574. 10.1016/j.telpol.2023.102574\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarvin HJP, Bouzembrak Y, van der Fels-Klerx HJ, Kempenaar C, Veerkamp R, Chauhan A, Stroosnijder S, Top J, Simsek-Senel G, Vrolijk H, Knibbe WJ, Zhang L, Boom R, Tekinerdogan B (2022) Digitalisation and Artificial Intelligence for sustainable food systems [Note]. Trends Food Sci Technol 120:344\u0026ndash;348. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tifs.2022.01.020\u003c/span\u003e\u003cspan address=\"10.1016/j.tifs.2022.01.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMascarello G, Pinto A, Crovato S, Pezzoli BT, Pietropaoli M, Bertola M, Mutinelli F, Formato G (2024) Consumers' Perceptions and Behaviors Regarding Honey Purchases and Expectations on Traceability and Sustainability in Italy. Sustainability 16(20) Article 8846. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su16208846\u003c/span\u003e\u003cspan address=\"10.3390/su16208846\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMazz\u0026ugrave; MF, Marozzo V, Baccelloni A, Pompeis FD (2021) Measuring the Effect of Blockchain Extrinsic Cues on Consumers' Perceived Flavor and Healthiness: A Cross-Country Analysis. Foods 10(6). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eArticle 1413. https://doi.org/10.3390/foods10061413\u003c/span\u003e\u003cspan address=\"Article 1413. 10.3390/foods10061413\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeng ZZ, Wu ZP, Gray J (2019) RFID-Based Object-Centric Data Management Framework for Smart Manufacturing Applications. IEEE Internet Things J 6(2):2706\u0026ndash;2716. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/JIOT.2018.2873426\u003c/span\u003e\u003cspan address=\"10.1109/JIOT.2018.2873426\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoosavy M, Khatibi SA, Faraz AS, Tarighi A (2025) Identification and quantification of permitted and non-permitted synthetic food colorants in meat products [Article]. Food Control 178:111434. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foodcont.2025.111434\u003c/span\u003e\u003cspan address=\"10.1016/j.foodcont.2025.111434\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuehlematter UJ, Daniore P, Vokinger KN (2021) Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015-20): a comparative analysis. Lancet Digit Health 3(3):e195\u0026ndash;e203. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s2589-7500(20)30292-2\u003c/span\u003e\u003cspan address=\"10.1016/s2589-7500(20)30292-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMugion RG, Ungaro V, Di Pietro L, Amin A, Bisceglia F (2026) Mapping Women's Role in Agriculture 4.0: A Bibliometric Analysis and Conceptual Framework. AGRICULTURE-BASEL 16(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eArticle 214. https://doi.org/10.3390/agriculture16020214\u003c/span\u003e\u003cspan address=\"Article 214. 10.3390/agriculture16020214\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMunoz-Arcentales A, L\u0026oacute;pez-Pernas S, Pozo A, Alonso A, Salvach\u0026uacute;a J, Huecas G (2020) Implement Using FIWARE Sustainability 12(9). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eArticle 3885. https://doi.org/10.3390/su12093885\u003c/span\u003e\u003cspan address=\"Article 3885. 10.3390/su12093885\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Data Usage and Access Control in Industrial Data Spaces\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMyshko A, Checchinato F, Colapinto C, Finotto V, Mauracher C (2024) Towards the twin transition in the agri-food sector? Framing the current debate on sustainability and digitalisation [Review]. J Clean Prod 452., Article 142063. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2024.142063\u003c/span\u003e\u003cspan address=\"10.1016/j.jclepro.2024.142063\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNarayana GP, Singh DK, Chudasama MH, Deotale S, Reddy NBP, Perumal T (2025) Intelligent packaging of functional foods: A comprehensive review of its advances, recyclability, and regulatory frameworks. Bioresource Technol Rep 32:102449. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biteb.2025.102449\u003c/span\u003e\u003cspan address=\"10.1016/j.biteb.2025.102449\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNicolau AM, Petcu P (2026) Beyond Green Labels: Leveraging Blockchain, IoT, and AI for Enhanced Traceability and Verification of Green Marketing Claims in Transnational Agri-Food Supply Chains. Sustainability 18(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eArticle 782. https://doi.org/10.3390/su18020782\u003c/span\u003e\u003cspan address=\"Article 782. 10.3390/su18020782\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNisa SK, Elias H, Usman S, Chuprat S (2024) Discovering the Global Landscape of Agri-Food and Blockchain: A Bibliometric Review. Int J Adv Comput Sci Appl 15(4):556\u0026ndash;575\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOdobasa R, Borzan Z (2021) \u003cem\u003eCHALLENGES AND IMPACTS OF COVID-19 PANDEMIC AND THE EUROPEAN GREEN PLAN ON THE DEVELOPMENT OF THE CROATIAN AGRICULTURAL AND FOOD SECTOR\u003c/em\u003e 10TH INTERNATIONAL SCIENTIFIC SYMPOSIUM REGION ENTREPRENEURSHIP DEVELOPMENT (RED 2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalka P, Palosz R, Porebski A, Wisniewska K (2024) A dataset on the contents of 100 terms of service of online platforms, analyzed and evaluated under the EU consumer law. DATA BRIEF 53:110136. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.dib.2024.110136\u003c/span\u003e\u003cspan address=\"10.1016/j.dib.2024.110136\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanagos P, Orgiazzi A, Lugato E, Vandenesch G, Thannberger L, Neff JC, Reijneveld A, Mathijs E, Chapot P, Guerrini S, Styriakov\u0026aacute; D, Kremers J, Martin P, van der Tol R, Burgeon V, Kalimeri E, Van Eynde E (2025) Healthy soils as a booster to EU competitiveness. \u003cem\u003eLAND USE POLICY\u003c/em\u003e, \u003cem\u003e158\u003c/em\u003e, Article 107755. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.landusepol.2025.107755\u003c/span\u003e\u003cspan address=\"10.1016/j.landusepol.2025.107755\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanni F, Massaro A, Bodenbender L, Zacometti C, Piro R, Savino AF, Weller P, Tata A (2026) HS-GC-IMS authenticates the botanical origin of seven types of Italian monofloral honey, annotating the significant volatile compounds by the novel HS- SPME-GC-IMS-MS prototype [Article]. Microchem J 224 Article 117526. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.microc.2026.117526\u003c/span\u003e\u003cspan address=\"10.1016/j.microc.2026.117526\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParra-L\u0026oacute;pez C, Carmona-Torres C (2026) Enabling the Circular Food Economy Through Industry 4.0: A Review of Applications, Challenges, and Policies in the European Union. Compr Rev Food Sci Food Saf 25(2):e70431. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1541-4337.70431\u003c/span\u003e\u003cspan address=\"10.1111/1541-4337.70431\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerret JK, Skretkowicz Y (2025) Nutri-Score - a bibliographic review and analysis of the literature. J FOOD Nutr Res 64(2):91\u0026ndash;103\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePesapane F, Volont\u0026eacute; C, Codari M, Sardanelli F (2018) Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States. Insights Imaging 9(5):745\u0026ndash;753. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13244-018-0645-y\u003c/span\u003e\u003cspan address=\"10.1007/s13244-018-0645-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRehm J, Ferreira-Borges C, Kokole D, Neufeld M, Olsen A, Rovira P, Garcia LS, Tran A, Colom J (2023) Assessing the impact of providing digital product information on the health risks of alcoholic beverages to the consumer at point of sale: A pilot study. Drug Alcohol Rev 42(6):1332\u0026ndash;1337. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/dar.13676\u003c/span\u003e\u003cspan address=\"10.1111/dar.13676\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodriguez J, Piccoli G (2024) Sustaining superior visibility within digital platforms through inside and outside competitive action repertoires [Article]. Electron Commer Res Appl 68 Article 101456. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.elerap.2024.101456\u003c/span\u003e\u003cspan address=\"10.1016/j.elerap.2024.101456\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRojano FJP, Garc\u0026iacute;a LV, Mart\u0026iacute;nez TF (2025) Disinformation in the food sector. Case studies published in Maldita, Newtral and EFE Verifica. REVISTA Int DE RELACIONES PUBLICAS 30(15):45\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5783/revrrpp.v15i30.913\u003c/span\u003e\u003cspan address=\"10.5783/revrrpp.v15i30.913\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRossi S, Gemma S, Borghini F, Perini M, Butini S, Carullo G, Campiani G (2025) Agri-food traceability today: Advancing innovation towards efficiency, sustainability, ethical sourcing, and safety in food supply chains. Trends Food Sci Technol 163:105154. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tifs.2025.105154\u003c/span\u003e\u003cspan address=\"10.1016/j.tifs.2025.105154\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoussaki I, Doolin K, Skarmeta A, Routis G, Claffey E, Mora M, Martinez JA (2023) Building an interoperable space for smart agriculture. Digit Commun NETWORKS 9(1):183\u0026ndash;193. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.dcan.2022.02.004\u003c/span\u003e\u003cspan address=\"10.1016/j.dcan.2022.02.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRyan M, G\u0026uuml;rtler P, Bogucki A (2024) Will the real data sovereign please stand up? An EU policy response to sovereignty in data spaces. Int J LAW Inform Technol 32(1):eaae006. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ijlit/eaae006\u003c/span\u003e\u003cspan address=\"10.1093/ijlit/eaae006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantra S, Kukreja P, Saxena K, Gandhi S, Singh OV (2024) Navigating regulatory and policy challenges for AI enabled combination devices. Front Med Technol 6:1473350. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmedt.2024.1473350\u003c/span\u003e\u003cspan address=\"10.3389/fmedt.2024.1473350\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSapienza S (2022) Smart Solutions in AgriTech: Research Trajectories in the Digital Transition. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSapienza S, Vedder A (2023) Principle-based recommendations for big data and machine learning in food safety: the P-SAFETY model [Article]. AI Soc 38(1):5\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00146-021-01282-1\u003c/span\u003e\u003cspan address=\"10.1007/s00146-021-01282-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSekulic NM, Narancic M, Nikolic M (2025) LEGAL DIMENSIONS OF EU SUSTAINABLE AGRICULTURE POLICIES. Ekon POLJOPRIVREDA-ECONOMICS Agric 72(3):1105\u0026ndash;1119. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.59267/ekoPolj25031105M\u003c/span\u003e\u003cspan address=\"10.59267/ekoPolj25031105M\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShete RP, Bongale AM, Laxmikantha K, Hiremath S, Dharrao D (2025) Comprehensive analysis of aquaculture research trends focusing on Internet of Things, machine learning, water quality monitoring, and cybersecurity over two decades using bibliometric data. RESULTS Eng 28:108266. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rineng.2025.108266\u003c/span\u003e\u003cspan address=\"10.1016/j.rineng.2025.108266\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh PK, Agrawal N, Khare AK (2026) Artificial Intelligence: An Advanced Tool to Improve Safety and Quality in the Meat Industry. J Food Process Eng 49(2) Article e70383. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jfpe.70383\u003c/span\u003e\u003cspan address=\"10.1111/jfpe.70383\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh R, Zhou K, Auclair JR (2025) Reimagining drug regulation in the age of AI: a framework for the AI-enabled Ecosystem for Therapeutics. Front Med (Lausanne) 12:1679611. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmed.2025.1679611\u003c/span\u003e\u003cspan address=\"10.3389/fmed.2025.1679611\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSolaiman B (2023) FROM 'AI TO LAW' IN HEALTHCARE: THE PROLIFERATION OF GLOBAL GUIDELINES IN A VOID OF LEGAL UNCERTAINTY [Article]. Med Law 42(2):391\u0026ndash;406\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSortino A, De Leo G, Caruso T, Zinnai A, Beltrame F (2023) \u003cem\u003eAgri-food: which technologies to guarantee our health?\u003c/em\u003e XXXI INTERNATIONAL HORTICULTURAL CONGRESS, IHC2022: III INTERNATIONAL SYMPOSIUM ON MECHANIZATION, PRECISION HORTICULTURE, AND ROBOTICS: PRECISION AND DIGITAL HORTICULTURE IN FIELD ENVIRONMENTS\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStaver L, THE INTEGRATION INTO THE EUROPEAN UNION (2025) OPTIMIZING MOLDOVA'S AGRI-FOOD SUPPLY CHAINS FOR. \u003cem\u003eSCIENTIFIC PAPERS-SERIES MANAGEMENT ECONOMIC ENGINEERING IN AGRICULTURE AND RURAL DEVELOPMENT\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(2)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStoyanov K (2024) STRATEGIES FOR POLICY IN SCIENCE AND EDUCATION-STRATEGII NA OBRAZOVATELNATA I NAUCHNATA POLITIKA. 32(6):127\u0026ndash;137. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.53656/str2024-6s-10-int\u003c/span\u003e\u003cspan address=\"10.53656/str2024-6s-10-int\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. INTEGRATION OF BLOCKCHAIN TECHNOLOGIES IN THE FOOD SUPPLY CHAIN - REGULATORY FRAMEWORKS IN THE EUROPEAN UNION\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStranieri S, Riccardi F, Meuwissen MPM, Soregaroli C (2021) Exploring the impact of blockchain on the performance of agri-food supply chains. Food Control 119., Article 107495. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foodcont.2020.107495\u003c/span\u003e\u003cspan address=\"10.1016/j.foodcont.2020.107495\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTache I, THE LEADING INDUSTRIES IN THE NEW EU MEMBER STATES AND THEIR CONTRIBUTION TO INCREASING THE EU\u0026rsquo;S EXPORT POTENTIAL (2024). In \u003cem\u003eContemporary Studies in Economic and Financial Analysis\u003c/em\u003e (Vol. 115, pp. 141\u0026ndash;157). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/S1569-375920240000115009\u003c/span\u003e\u003cspan address=\"10.1108/S1569-375920240000115009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTadesse T, Ramirez-Rodriguez S, Rahmani D, Gezahegn TW, Verherbrugghen H, Vermeulen A, Skourletis N, Roig JMG (2025) Healthy but also traceable: consumer choices for traits of food waste-reducing innovations. J Clean Prod 521:146299. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2025.146299\u003c/span\u003e\u003cspan address=\"10.1016/j.jclepro.2025.146299\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTasic I, Benaissa RR, G\u0026oacute;mez PA, Fernandez JA, Cano MD (2025) Revisiting Traceability of Vegetable Fresh-Products in the EU: Why Blockchain Does (Not) Work? [Letter]. INTERNET Technol Lett 8(6) Article e647. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/itl2.647\u003c/span\u003e\u003cspan address=\"10.1002/itl2.647\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTausov\u0026aacute; M, Culkov\u0026aacute; K, Mykhei M, Taus P, Domarack\u0026aacute; L, Vrastiakov\u0026aacute; A (2026) Sectoral Analysis of Food Waste in EU Countries: Implications for Pro-Environmental Orientation and Policy. Foods 15(6):972. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/foods15060972\u003c/span\u003e\u003cspan address=\"10.3390/foods15060972\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTerzis P, Santamaria Echeverria OE (2023) Interoperability and governance in the European Health Data Space regulation [Note]. Med Law Int 23(4):368\u0026ndash;376. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/09685332231165692\u003c/span\u003e\u003cspan address=\"10.1177/09685332231165692\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eToader DC, Radulescu CM, Toader C (2024) Investigating the Adoption of Blockchain Technology in Agri-Food Supply Chains: Analysis of an Extended UTAUT Model. AGRICULTURE-BASEL 14(4):614. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agriculture14040614\u003c/span\u003e\u003cspan address=\"10.3390/agriculture14040614\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUmeda Y, Kitagawa K, Hirose Y, Akaho K, Sakai Y, Ohta M (2020) Potential impacts of the European Union\u0026rsquo;s circular economy policy on Japanese manufacturers [Article]. Int J Autom Technol 14(6):857\u0026ndash;866. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.20965/ijat.2020.p0857\u003c/span\u003e\u003cspan address=\"10.20965/ijat.2020.p0857\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUrdu D, Berre AJ, Sundmaeker H, Rilling S, Roussaki I, Marguglio A, Doolin K, Zaborowski P, Atkinson R, Palma R, Faraldi M, Wolfert S (2024) Aligning interoperability architectures for digital agri-food platforms [Article]. \u003cem\u003eComputers and Electronics in Agriculture\u003c/em\u003e, \u003cem\u003e224\u003c/em\u003e, Article 109194. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.compag.2024.109194\u003c/span\u003e\u003cspan address=\"10.1016/j.compag.2024.109194\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVaravallo G, Caragnano G, Bertone F, Vernetti-Prot L, Terzo O (2022) Traceability Platform Based on Green Blockchain: An Application Case Study in Dairy Supply Chain. Sustainability 14(6):3321. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su14063321\u003c/span\u003e\u003cspan address=\"10.3390/su14063321\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVillegas-Ch W, Govea J, Moncayo-Moncayo PS, Navarro AM, Chavez-Pirca C (2025) Optimization of the Traceability of Perishable Products Through Light Blockchain and IoT in the Food Industry. IEEE Access 13:67599\u0026ndash;67615. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/ACCESS.2025.3561596\u003c/span\u003e\u003cspan address=\"10.1109/ACCESS.2025.3561596\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWagan SM, Sidra S (2026) Interpreting Irrigation Decisions: Explainable AI Using SHAP and LIME in Agricultural Water Management Models. Water Resour Manage 40(1) Article 52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11269-025-04468-0\u003c/span\u003e\u003cspan address=\"10.1007/s11269-025-04468-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Wu W, Xu J, Gao M, Wu Z, Wang R, Liu H (2025) Changes in Quality and Safety Indexes During Rice Harvest and Discussion on Drying Technology. Foods 14(7). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/foods14071225\u003c/span\u003e\u003cspan address=\"10.3390/foods14071225\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie X, Fu B, Zhang M, Zhang W, Xu Z, Liu S, Wang J (2024) Eugenol-infused alginate nanoemulsion: A fluorescent label for direct food shelf-life extension and non-contact detection of food spoilage through biogenic amine sensing [Article]. Food Control 163:110525. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foodcont.2024.110525\u003c/span\u003e\u003cspan address=\"10.1016/j.foodcont.2024.110525\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZafeiriou E, Galatsidas S, Tsantopoulos G, Sofios S, Siakou E, Arabatzis G (2025) Assessing environmental and economic dynamics in the EU agri-food sector: The impact of imports through a BVAR analysis. NEW MEDIT 24(2):37\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.30682/nm2502c\u003c/span\u003e\u003cspan address=\"10.30682/nm2502c\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZarb\u0026agrave; C, Chinnici G, Matarazzo A, Privitera D, Scuderi A (2024) The innovative role of blockchain in agri-food systems: A literature analysis. Food Control 164., Article 110603. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foodcont.2024.110603\u003c/span\u003e\u003cspan address=\"10.1016/j.foodcont.2024.110603\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Z, Yan B (2025) Smart Photoresponsive Label with Red Fluorescence and Blue Phosphorescence Promoted by Hydrogen-Bonded Organic Frameworks for Food Additives Detection. ACS Appl Mater Interfaces 17(20):29941\u0026ndash;29951. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acsami.5c06337\u003c/span\u003e\u003cspan address=\"10.1021/acsami.5c06337\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZyoud S, Zyoud AH (2025) Revealing global trends on nature-based solutions: Mapping and visualizing research landscapes [Article]. Nature-Based Solutions 7., Article 100229. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.nbsj.2025.100229\u003c/span\u003e\u003cspan address=\"10.1016/j.nbsj.2025.100229\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"10588e89-6837-4cd2-a8be-f998a7a8b2cd","identifier":"10.13039/501100015999","name":"Universitatea \"Lucian Blaga\" din Sibiu","awardNumber":"LBUS-IRG-2024","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial, Intelligence, Food, Traceability","lastPublishedDoi":"10.21203/rs.3.rs-9439187/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9439187/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid digitalisation of agri-food supply chains, coupled with the burgeoning expansion of e-commerce within the European Union (EU), necessitates a fundamental reassessment of existing traceability mechanisms and regulatory compliance frameworks. This systemic transition introduces multifaceted challenges concerning food safety, informational transparency, and consumer protection within the digital marketplace, where conventional traceability protocols are increasingly rendered inadequate. This systematic review aims to evaluate the influence of artificial intelligence (AI) and digital labelling technologies on food traceability and the efficacy of regulatory enforcement within the EU e-commerce landscape. Methodologically, the study adhered to the PRISMA 2020 guidelines, employing a rigorous PICO (Population, Intervention, Comparison, Outcome) framework to interrogate academic databases and European public policy repositories. The selection process comprised an exhaustive search strategy, sequential screening, deduplication, and a robust assessment of the risk of bias using validated appraisal tools. We extracted and synthesised data regarding the implementation of intelligent labelling, Quick Response (QR) codes, and sensor-integrated active packaging, alongside their broader implications for transnational data governance. Our thematic synthesis demonstrates that integrating AI into digital labelling architectures significantly enhances the optimisation of real-time product quality monitoring. The evidence indicates a strategic convergence between technological innovation and the mandates of the European Green Deal; however, ubiquitous implementation remains hindered by legislative fragmentation and inherent vulnerabilities regarding data privacy. the findings reveal a pronounced discrepancy between the technical capabilities of e-commerce platforms and existing regulatory control mechanisms, highlighting an urgent requirement for adaptive governance frameworks.\u003c/p\u003e","manuscriptTitle":"Artificial Intelligence for Food Traceability and Digital Labelling Within the Framework of E-commerce in the European Union: A Systematic Review of Regulatory, Policy, and Scientific Evidence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-17 07:56:48","doi":"10.21203/rs.3.rs-9439187/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"007afe7e-e4af-48c9-856c-cf11a2f1210a","owner":[],"postedDate":"April 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66501439,"name":"Artificial Intelligence and Machine Learning"},{"id":66501440,"name":"Food Science \u0026 Technology"},{"id":66501441,"name":"International and Comparative Law"},{"id":66501442,"name":"Agricultural Economics \u0026 Policy"}],"tags":[],"updatedAt":"2026-04-17T07:56:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-17 07:56:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9439187","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9439187","identity":"rs-9439187","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}