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Transgenic Research

, Volume 16, Issue 1, pp 51–63 | Cite as

Sources of uncertainty in the quantification of genetically modified oilseed rape contamination in seed lots

  • Graham S. Begg
  • Danny W. Cullen
  • Pietro P. M. Iannetta
  • Geoff R. Squire
Original Paper

Abstract

Testing of seed and grain lots is essential in the enforcement of GM labelling legislation and needs reliable procedures for which associated errors have been identified and minimised. In this paper we consider the testing of oilseed rape seed lots obtained from the harvest of a non-GM crop known to be contaminated by volunteer plants from a GM herbicide tolerant variety. The objective was to identify and quantify the error associated with the testing of these lots from the initial sampling to completion of the real-time PCR assay with which the level of GM contamination was quantified.

The results showed that, under the controlled conditions of a single laboratory, the error associated with the real-time PCR assay to be negligible in comparison with sampling error, which was exacerbated by heterogeneity in the distribution of GM seeds, most notably at a small scale, i.e. 25 cm3. Sampling error was reduced by one to two thirds on the application of appropriate homogenisation procedures.

Keywords

Genetic modification Seed lot Sampling error Measurement error Heterogeneity Real-time PCR 

Notes

Acknowledgements

We wish to thank Geoff Robertson, Martin Elliott, Gill Banks and Joyce McKlusky for their technical assistance. This study was supported by the Scottish Executive Environment and Rural Affairs Department and Department of Environment, Food and Rural Affairs (Defra)

References

  1. Ahmed FE (2002) Detection of genetically modified organisms in foods. Trends Biotechnol 20:215–223PubMedCrossRefGoogle Scholar
  2. Auer CA (2003) Tracking genes from seed to supermarket: techniques and trends. Trends Plant Sci 8:591–597PubMedCrossRefGoogle Scholar
  3. Block A, Schwarz G (2003) Validation of different genomic and cloned DNA calibration standards for construct-specific quantification of LibertyLink in rapeseed by real-time PCR. Eur Food Res Technol 216:421–427Google Scholar
  4. Bonfini L, Petra H, Kay S, Van den Eede G (2002) Review of GMO detection and quantification techniques. EUR 20384 EN Google Scholar
  5. Brera C, Donnarumma E, Onori R, Foti N, Pazzaglini B, Miraglia M (2005) Evaluation of sampling criteria for the detection of GM soybeans in bulk. Ital J Food Sci 17:177–185Google Scholar
  6. Cox DR, Isham V, Northrop P (2000) Statistical modelling and analysis of spatial patterns In: Diekmann U, Law R, Metz JAA (eds) The geometry of ecological interactions: simplifying spatial complexity Cambridge University Pres, Cambridge, pp 65–88Google Scholar
  7. Deisingh AK, Badrie N (2005) Detection approaches for genetically modified organisms in foods Food. Res Int 38:639–649CrossRefGoogle Scholar
  8. EC 1829/2003 Regulation (EC) No. 1829/2003 of the European Parliament and of the Council of 22 September 2003 on genetically modified food and feed Official Journal of the European Union L 268/1Google Scholar
  9. EC 641/2004 Commission Regulation (EC) No 641/2004 of 6 April 2004 on detailed rules for the implementation of Regulation (EC) No 1829/2003 of the European Parliament and of the Council as regards the application for the authorisation of new genetically modified food and feed the notification of existing products and adventitious or technically unavoidable presence of genetically modified material which has benefited from a favourable risk evaluation Official Journal of the European Union L 102/14Google Scholar
  10. ENV/04/04 Draft Commission Recommendation of on technical guidance for sampling and detection of genetically modified organisms and material produced from genetically modified organisms as or in products in the context of Regulation (EC) No 1830/2003Google Scholar
  11. EFSA (2004) Guidance document of the scientific panel on genetically modified organisms for the risk assessment of genetically modified plants and derived food and feed. EFSA J 99:1–94Google Scholar
  12. Ellison SLR, Rosslein M, Williams A (2000) EURACHEM/CITAC Guide C.G. 4 Quantifying Uncertainty in Analytical Measurement. EURACHEM/CITACGoogle Scholar
  13. Firbank LG (2003) The farm scale evaluations of springsown genetically modified crops—introduction. Philos T Roy Soc B 358:1777–1778CrossRefGoogle Scholar
  14. Gy P (1998) Sampling for analytical purposes. John Wiley and SonsGoogle Scholar
  15. Holst-Jensen A, Berdal KG (2004) The modular analytical procedure and validation approach and the units of measurement for genetically modified materials in foods and feeds. J AOAC Int 87:927–936PubMedGoogle Scholar
  16. Hübner P, Waibling H-U, Pietsch K, Brodman P (2001) Validation of PCR methods for quantitation of genetically modified plants in food. J AOAC Int 84:1855–1864PubMedGoogle Scholar
  17. Jorgensen J, Kristensen K (1990) Heterogeneity of grass seed lots. Seed Sci Technol 18:515–523Google Scholar
  18. Kobilinsky A, Berthau Y (2005) Minimum cost acceptance sampling plans for grain control with application to GMO detection. Chemometr Intell Lab 75:189–200CrossRefGoogle Scholar
  19. Kruse M (1997) The effect of sampling intensity on the representativeness of the submitted sample as depending on the heterogeneity of the seed lot. Agribiol Res 50:128–145Google Scholar
  20. Kruse M, Steiner AM (1995) Variation between seed lots as an estimate for the risk of heterogeneity with increasing ISTA maximum lot size ISTA 24th Congress Copenhagen Seed Symposium Abstracts 21Google Scholar
  21. Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the \( 2^{ - \Delta \Delta C_T } \) method. Methods 25:402–408Google Scholar
  22. Paoletti C, Donatelli M, Kay S, Van den Eede G (2003) Simulating kernel lot sampling: the effect of heterogeneity on the detection of GMO contaminations. Seed Sci Technol 31:629–638Google Scholar
  23. Petersen L, Minkkinen P, Esbensen KH (2005) Representative sampling for reliable data analysis: theory of Sampling. Chemometr Intell Lab 77:261–277Google Scholar
  24. Pinheiro JC, Bates DM (2000) Mixed-effects models in S and S-plus. Springer Verlag, New YorkGoogle Scholar
  25. Remund KM, Dixon DA, Wright DL, Holden LR (2001) Statistical considerations in seed purity testing for transgenic traits. Seed Sci Res 11:101–119Google Scholar
  26. Tattersfield JG (1977) Further estimates of heterogeneity in seed lots. Seed Sci Technol 5:443–450Google Scholar
  27. Thompson M, Ellison SLR, Wood R (2002) Harmonized guidelines for single laboratory validation of methods of analysis (IUPAC Technical Report). Pure Appl Chem 74:835–855CrossRefGoogle Scholar
  28. Weighardt F, Barbati C, Paoletti C, Querci M, Kay S, DeBeukeleer M, Van den Eede G (2004) Real-time polymerase chain reaction-based approach for quantification of the pat gene in the T25 Zea mays event. J AOAC Int 87:1342–1355PubMedGoogle Scholar
  29. Yang L, Ding J, Zhang C, Jia J, Weng H, Liu W Zhang D (2005) Estimating the copy number of transgenes in transformed rice by real-time quantitative PCR. Plant Cell Rep 23:759–763PubMedCrossRefGoogle Scholar
  30. Zeitler R, Pietsch P, Waiblinger H-U (2002) Validation of real-time PCR methods for the quantification of transgenic contaminations in rape seed. Eur Food Res Technol 214:346–351CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2006

Authors and Affiliations

  • Graham S. Begg
    • 1
  • Danny W. Cullen
    • 1
  • Pietro P. M. Iannetta
    • 1
  • Geoff R. Squire
    • 1
  1. 1.Scottish Crop Research InstituteDundeeUK

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