Expert Study to Select Indicators of the Occurence of Emerging Mycotoxin Hazards

M.C. Kandhai, C.J.H. Booij, H.J. van der Fels-Klerx

    Research output: Contribution to journalArticleAcademicpeer-review

    4 Citations (Scopus)

    Abstract

    This article describes a Delphi-based expert judgment study aimed at the selection of indicators to identify the occurrence of emerging mycotoxin hazards related to Fusarium spp. in wheat supply chains. A panel of 29 experts from 12 European countries followed a holistic approach to evaluate the most important indicators for different chain stages (growth, transport and storage, and processing) and their relative importance. After three e-mailing rounds, the experts reached consensus on the most important indicators for each of the three stages: wheat growth, transport and storage, and processing. For wheat growth, these indicators include: relative humidity/rainfall, crop rotation, temperature, tillage practice, water activity of the kernels, and crop variety/cultivar. For the transport and storage stage, they include water activity in the kernels, relative humidity, ventilation, temperature, storage capacity, and logistics. For wheat processing, indicators include quality data, fraction of the cereal used, water activity in the kernels, quality management and traceability systems, and carryover of contamination. The indicators selected in this study can be used in an identification system for the occurrence of emerging mycotoxin hazards in wheat supply chains. Such a system can be used by risk managers within governmental (related) organizations and/or the food and feed industry in order to react proactively to the occurrence of these emerging mycotoxins.
    Original languageEnglish
    Pages (from-to)160-170
    JournalRisk Analysis
    Volume31
    Issue number1
    DOIs
    Publication statusPublished - 2011

    Keywords

    • wheat-grain
    • deoxynivalenol content
    • delphi technique
    • previous crop
    • weather data
    • accumulation
    • strategies
    • tillage
    • disease
    • models

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