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Sampling and modeling for the quantification of adventitious genetically modified presence in maize

Allnutt, Theodore Richard, Dwyer, Mark, McMillan, Jillian, Henry, Christine and Langrell, Stephen 2008, Sampling and modeling for the quantification of adventitious genetically modified presence in maize, Journal of agricultural and food chemistry, vol. 56, no. 9, pp. 3232-3237, doi: 10.1021/jf800048q.

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Title Sampling and modeling for the quantification of adventitious genetically modified presence in maize
Author(s) Allnutt, Theodore Richard
Dwyer, Mark
McMillan, Jillian
Henry, Christine
Langrell, Stephen
Journal name Journal of agricultural and food chemistry
Volume number 56
Issue number 9
Start page 3232
End page 3237
Total pages 6
Publisher American Chemical Society
Place of publication Washington, D.C.
Publication date 2008
ISSN 0021-8561
Keyword(s) DNA, plant
models, biological
plants, genetically modified
polymerase chain reaction
reproducibility of results
sensitivity and specificity
Spain
zea mays
Summary The coexistence of genetically modified (GM) and non-GM crops is an important economic and political issue in the European Union. We examined the GM content in non-GM maize crops in Spain in 2005. Both the standing crop and the harvest were tested, and the %GM DNA was quantified by real-time polymerase chain reaction. We compared the level of GM as a function of distance from known GM source fields in a 1.2 km2 landscape. The distribution of GM was compared to predictions from previous studies, and good agreement was found. Control and monitoring of adventitious GM presence in non-GM crops can only be achieved by fit-for-purpose sampling and testing schemes. We used a GM dispersal function to simulate non-GM crops in the studied zone and tested the accuracy of five different sampling schemes. Random sampling was found to be the most accurate and least susceptible to bias by GM spatial structure or gradients. Simulations showed that to achieve greater than 95% confidence in a GM labeling decision of a harvest (when treated as a single marketed lot), 34 samples would be needed when the harvest was outside 50% of the GM threshold value. The number of samples required increased rapidly as the harvest approached the GM threshold, implying that accurate labeling when the harvest is within +/-17% of the threshold may not be possible with high confidence.
Language eng
DOI 10.1021/jf800048q
Field of Research 060499 Genetics not elsewhere classified
Socio Economic Objective 970106 Expanding Knowledge in the Biological Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2008, American Chemical Society
Persistent URL http://hdl.handle.net/10536/DRO/DU:30086573

Document type: Journal Article
Collection: School of Medicine
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