Evidence map›Paper›PMID 42045329›Full record

ArticleScientific reports2026

Emerging risk identification in the food chain using text mining-supported monitoring of food safety news: insights from 2022 to 2023.

Zsuzsa Farkas, Andrea Zentai, Máté Farkas, Krisztián Vribék, Szilveszter Csorba, Erika Országh, Orsolya Strang, Tekla Engelhardt, Miklós Süth, Ákos Józwiak

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Zsuzsa FarkasInstitute of Food Chain Science, Department of Digital Food Science, University of Veterinary Medicine Budapest, István str. 2., Budapest, 1078, Hungary.
Andrea ZentaiInstitute of Food Chain Science, Department of Digital Food Science, University of Veterinary Medicine Budapest, István str. 2., Budapest, 1078, Hungary. zentai.andrea@univet.hu.
Máté FarkasInstitute of Food Chain Science, Department of Digital Food Science, University of Veterinary Medicine Budapest, István str. 2., Budapest, 1078, Hungary.
Krisztián VribékInstitute of Food Chain Science, Department of Digital Food Science, University of Veterinary Medicine Budapest, István str. 2., Budapest, 1078, Hungary.
Szilveszter CsorbaInstitute of Food Chain Science, Department of Digital Food Science, University of Veterinary Medicine Budapest, István str. 2., Budapest, 1078, Hungary.
Erika OrszághInstitute of Food Chain Science, Department of Digital Food Science, University of Veterinary Medicine Budapest, István str. 2., Budapest, 1078, Hungary.
Orsolya StrangInstitute of Food Chain Science, Department of Digital Food Science, University of Veterinary Medicine Budapest, István str. 2., Budapest, 1078, Hungary.
Tekla EngelhardtInstitute of Food Chain Science, Department of Digital Food Science, University of Veterinary Medicine Budapest, István str. 2., Budapest, 1078, Hungary.
Miklós SüthNational Laboratory of Infectious Animal Diseases, Antimicrobial Resistance, Veterinary Public Health and Food Chain Safety, University of Veterinary Medicine Budapest, Budapest, 1078, Hungary.
Ákos JózwiakInstitute of Food Chain Science, Department of Digital Food Science, University of Veterinary Medicine Budapest, István str. 2., Budapest, 1078, Hungary.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The identification of emerging risks in the food chain is essential for proactive food safety management in a rapidly evolving global environment. In this study, we applied a structured emerging risk identification framework supported by text mining of food safety news collected from the Europe Media Monitor (EMM) to analyse signals from 2022 to 2023. The workflow combines automated news collection, topic detection, and expert evaluation within a multi-stage filtering system. Using this approach, 164 emerging risks and drivers were identified during the study period. Chemical risks-including pesticide-related issues, per- and polyfluoroalkyl substances, and microplastics-were among the most frequently detected topics, while sustainability-related developments such as alternative proteins and novel packaging technologies emerged as major drivers of change. Comparison with the 2020-2021 period indicates a marked increase in signals related to chemical contaminants and sustainability. These findings highlight the value of combining automated text mining of food safety news with expert-based evaluation to support timely emerging risk identification in the food chain.

Indexed as

Data MiningFood ChainFood SafetyFood ContaminationHumansRisk AssessmentData analysisEmerging risk identificationFood chain risksText miningTopic detection

Identifiers

PMID42045329
PMCPMC13287678

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.