Kernel Lot Distribution Assessment (KeLDA): a study on the distribution of GMO in large soybean shipments


  • Paoletti, C. , Heissenberger, A. , Mazzara, M. , Larcher, S. , Grazioli, E. , Corbisier, P. , Hess, N. , Berben, G. , Lübeck, P. , De Loose, M. , Moran, G. , Henry, C. , Brera, C. , Folch, I. , Ovesna, J. & Van den Eede, G. (2006). Kernel Lot Distribution Assessment (KeLDA): a study on the distribution of GMO in large soybean shipments. Eur. Food Res. Technol. 224: (1), 129-139.
Type Journal Article
Year 2006
Title Kernel Lot Distribution Assessment (KeLDA): a study on the distribution of GMO in large soybean shipments
Journal Eur. Food Res. Technol.
Label 517
Recnumber 517
Volume 224
Issue 1
Pages 129-139
Date November 2006
Type of article avec comité de lecture
Endnote keywords GMO, detection, PCR, sampling Pr-KeLDA RA-CRA-W 2005-2006
Abstract The reliability of analytical testing is strongly affected by sampling uncertainty. Sampling is always a source of error and the aim of ?good? sampling practice is to minimize this error. Generally the distribution of genetically modified (GM) material within lots is assumed to be random in order to use binomial distribution to make inferences. This assumption was never verified in practice and no experimental data investigating the distribution of genetically modified organisms (GMOs) exist. The objectives of the KeLDA project were: (1) to assess the distribution of GM material in soybean lots (2) to estimate the amount of variability of distribution patterns among lots. The GM content of 15 soybean lots imported into the EU was estimated (using real-time PCR methodology) analyzing 100 increment samples systematically sampled from each lot at predetermined time intervals during the whole period of off-loading. The distribution of GM material was inferred by the one-dimensional (temporal) distribution of contaminated increments. All the lots display significant spatial structuring, indicating that randomness cannot be assumed a priori. The evidence that the distribution of GM material is heterogeneous highlights the need to develop sampling protocols based on statistical models free of distribution requirements. Keywords Sampling - Bulk commodities - Spatial autocorrelation - Heterogeneity - Soybean - GMOs
Author address Berben Gilbert, Quality Department of Agro-food Products, Walloon Agricultural Research Centre (CRA-W), Chaussée de Namur, 24, B-5030 Gembloux, berben@cra.wallonie.be
Fichier 1417388544517-berben-2006.pdf
Caption 517-berben-2006.pdf
Lien http://dx.doi.org/10.1007/s00217-006-0299-8
Authors Paoletti, C. , Heissenberger, A. , Mazzara, M. , Larcher, S. , Grazioli, E. , Corbisier, P. , Hess, N. , Berben, G. , Lübeck, P. , De Loose, M. , Moran, G. , Henry, C. , Brera, C. , Folch, I. , Ovesna, J. & Van den Eede, G.