Újonnan felmerülő kockázatok és adatvezérelt rendszerek az élelmiszerláncban = Emerging risks and data-driven systems in the food chain

Farkas, Zsuzsa and Jóźwiak, Ákos (2025) Újonnan felmerülő kockázatok és adatvezérelt rendszerek az élelmiszerláncban = Emerging risks and data-driven systems in the food chain. SCIENTIA ET SECURITAS, 6 (1-2). pp. 171-176. ISSN 3057-9759

Preview

Text 112-article-p171.pdf - Published Version Available under License Creative Commons Attribution. Download (248kB) | Preview

Abstract

A globális hajtóerők (klímaváltozás, demográfiai folyamatok, technológiai innováció és geopolitikai instabilitás) elősegítik az új élelmiszerlánc-biztonsági kockázatok megjelenését. A hatalmas mennyiségű adatot/információt lehetetlen manuálisan feldolgozni, az adatalapú előfeldolgozás elengedhetetlen. Az Állatorvostudományi Egyetem Élelmiszerlánc-tudományi Intézetében kidolgozásra került egy az élelmiszerlánc-biztonsági kockázatok korai azonosítását lehetővé tevő, adatvezérelt és szakértői validálással támogatott keretrendszer. Az adattudomány és a multidiszciplináris szakértelem szinergiája nélkülözhetetlen a proaktív kockázatkezeléshez; ez megalapozza a hatósági döntéseket, a kutatási prioritásokat és az ipari önellenőrzési rendszerek adaptálását a turbulens globális környezetben. | Global drivers like climate change, demographic shifts, technological innovations, and geopolitical instability are accelerating emerging food chain risks. In summary, these forces are reshaping risk landscapes, such as through rising temperatures altering pathogen distributions and urbanization extending vulnerable supply chains. Drawing on vast datasets that are impossible to process manually, therefore advanced data analytics must be combined with multidisciplinary expertise to support regulatory decisions, research priorities, and industry self-monitoring in a turbulent environment. A data-driven framework for adequately addressing and handling the effects of these drivers operates through a three-tiered horizon scanning approach: (1) short-term early warning (days to weeks) for real-time incident detection via automated tools; (2) medium-term emerging risks (months to years) for systematic identification and ranking; and (3) long-term foresight (5–30 years) for scenario-based analysis of systemic drivers. The University of Veterinary Medicine’s Institute of Food Chain Science has developed a robust framework for early risk identification, merging data analytics – such as rapid alert trend analysis for temporal clustering of RASFF notifications, text and media mining for co-occurrence networks and sentiment analysis, patent network analysis using structural holes and Bray–Curtis dissimilarity, and weak-signal mining to detect growing terms – with multidisciplinary expert knowledge to validate and prioritize threats. Based on the framework’s analysis of risks from 2020–2024, emerging threats are categorized into ten classes: 1. Microbial safety in fresh, ready-to-eat vegetables and mushrooms; 2. Toxins and antimicrobial resistance (AMR) in pet animal feeds; 3. Accumulation of micro- and nanomaterials and chemically bound contaminants; 4. New transmission routes for AMR; 5. Novel chemical contaminants (e.g., FCM migrants, PFAS compounds); 6. Risks linked to sustainability trends, such as alternative proteins and food waste recycling; 7. Hazards from genetic and nanotechnology innovations; 8. Climate change-induced changes in mycotoxin patterns; 9. Emerging or newly invasive zoonotic pathogens; and 10. Household risks from consumer trends (e.g., home fermentation). By fostering an adaptive, feedback-driven system, this framework enhances regulatory decision-making, research priorities, and industry resilience, addressing global public health challenges like the 600 million annual foodborne illnesses reported by WHO and promoting sustainable food security amid rapid changes.

Actions (login required)

Edit Item