Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 508716
Title SIGMO : A decision support System for Identification of genetically modified food or feed products
Author(s) Bohanec, Marko; Boshkoska, Biljana Mileva; Prins, Theo W.; Kok, Esther J.
Source Food Control 71 (2017). - ISSN 0956-7135 - p. 168 - 177.
Department(s) RIKILT - BU Toxicology Bioassays & Novel Foods
Publication type Refereed Article in a scientific journal
Publication year 2017
Keyword(s) Decision support system - Food and feed products - Genetically modified crops - Qualitative multi-attribute model - Traceability - Unauthorised GMOs

Since their introduction in 1994, more and more genetically modified (GM) crops are grown worldwide and introduced in food or feed products. In the European Union (EU), the production, trade and marketing of GM products is strictly regulated, but the situation is becoming more complex due to the increasing number and complexity of GM crops, and asynchronic approval procedures with the major GM crop producing countries. Importers and traders are obliged to assess their respective supply chains for the potential presence of authorised and unauthorised GM organisms (GMOs), where wrong decisions may lead to substantial economic losses. This article presents a decision support system SIGMO aimed at guiding producers and traders with the assessment of the likelihood that their products may comprise authorised or unauthorised GM materials. The assessment is based on traceability data about the product (nature and origin of the raw materials, transportation aspects), as well as analytical results of the presence of GMOs in the final product or its ingredients. The approach uses a combination of data-driven and model-driven decision support. SIGMO is composed of (1) a data base providing data about GMO crop species produced and approved in counties worldwide, (2) a multi-attribute model for the assessment of GMO presence in food/feed products, and (3) an on-line user interface. SIGMO helps producers and traders to better comply to valid EU GMO regulations and to better control their products and supply chains in terms of the unintended presence of (unauthorised) GMOs in a cost-effective way.

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