Mecanismes de detecció d'organismes genèticament modificats

El contingut d'organismes genèticament modificats (OGM) a l'alimentació humana i animal està regulat per la norma europea, que n'estableix l'etiquetatge obligatori quan el contingut en OGM és superior al 0,9 %. El Comitè Europeu per a l'Estandardització ha desenvolupat mètodes de detecció, d'identificació i de quantificació dels OGM per tal de dur a terme aquesta norma europea. Els laboratoris que treballen en OGM poden aplicar aquests mètodes específics, sensibles i precisos i poden repetir-los i reproduir-los amb resultats segurs.Food and feed GMO’s content is regulated by the European standards, which establish that the labeling is mandatory when the GMO content is higher than 0.9 %. The European Committee for Standardization has developed the detection, the identification and the quantification methods of GMOs to carry out this European standard. The laboratories that work in GMO can apply these specific, sensitive and precise methods and can repeat and reproduce them with safety results

Kernel Lot Distribution Assessment (KeLDA): a Comparative Study of Protein and DNA-Based Detection Methods for GMO Testing

Monitoring of market products for detection of genetically modified organisms (GMO) is needed to comply with legislation in force in many regions of the world, to enforce traceability and to allow official control along the production and the distribution chains. This objective can be more easily achieved if reliable, time and cost-effective analytical methods are available. A GMO can be detected using either DNA-based or protein-based methods; both present advantages and disadvantages. The objective of this work was to assess the performance of a protein-based (lateral flow strips—LFT) and of a DNA-based (polymerase chain reaction—PCR) detection method for GMO analysis. One thousand five hundred samples of soybean, deriving from the sampling of 15 independent bulk lots in large shipments, were analysed to assess and compare the performance of the analytical methods and evaluate their suitability for GMO testing. Several indicators were used to compare the performance of the methods, including the percentage correlation between the PCR and LFT results. The GMO content of the samples ranged from 0 up to 100 %, allowing a full assessment of both analytical approaches with respect to all possible GMO content scenarios. The study revealed a very similar performance of the two methodologies, with low false-negative and false-positive results, and a very satisfactory capacity of both methods in detecting low amounts of target. While determining the fitness for purpose of both analytical approaches, this study also underlines the importance of alternative method characteristics, like costs and time.JRC.I.3-Molecular Biology and Genomic

Kernel Lot Distribution Assessment (KeLDA): a Comparative Study of Protein and DNA-Based Detection Methods for GMO Testing

Monitoring of market products for detection of genetically modified organisms (GMO) is needed to comply with legislation in force in many regions of the world, to enforce traceability and to allow official control along the production and the distribution chains. This objective can be more easily achieved if reliable, time and cost-effective analytical methods are available. A GMO can be detected using either DNA-based or protein-based methods; both present advantages and disadvantages. The objective of this work was to assess the performance of a protein-based (lateral flow strips—LFT) and of a DNA-based (polymerase chain reaction—PCR) detection method for GMO analysis. One thousand five hundred samples of soybean, deriving from the sampling of 15 independent bulk lots in large shipments, were analysed to assess and compare the performance of the analytical methods and evaluate their suitability for GMO testing. Several indicators were used to compare the performance of the methods, including the percentage correlation between the PCR and LFT results. The GMO content of the samples ranged from 0 up to 100 %, allowing a full assessment of both analytical approaches with respect to all possible GMO content scenarios. The study revealed a very similar performance of the two methodologies, with low false-negative and false-positive results, and a very satisfactory capacity of both methods in detecting low amounts of target. While determining the fitness for purpose of both analytical approaches, this study also underlines the importance of alternative method characteristics, like costs and time

Mecanismes de detecció d'organismes genèticament modificats

El contingut d'organismes genèticament modificats (OGM) a l'alimentació humana i animal està regulat per la norma europea, que n'estableix l'etiquetatge obligatori quan el contingut en OGM és superior al 0,9 %. El Comitè Europeu per a l'Estandardització ha desenvolupat mètodes de detecció, d'identificació i de quantificació dels OGM per tal de dur a terme aquesta norma europea. Els laboratoris que treballen en OGM poden aplicar aquests mètodes específics, sensibles i precisos i poden repetir-los i reproduir-los amb resultats segurs.Food and feed GMOs content is regulated by the European standards, which establish that the labeling is mandatory when the GMO content is higher than 0.9 %. The European Committee for Standardization has developed the detection, the identification and the quantification methods of GMOs to carry out this European standard. The laboratories that work in GMO can apply these specific, sensitive and precise methods and can repeat and reproduce them with safety results

Kernel Lot Distribution Assessment (KeLDA): a Study on the Distribution of GMO in Large Soybean Shipments

Reliability of analytical testing is strongly affected by sampling uncertainty. Sampling os always a source of error and aim of "good" sampling practice is to minimize this error. Generally the distribution of 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 GMOs exist. Objectives of the KeLDA projects are: 1) assess the distribution of GM material in soybean lots 2) estimate the amount of variability of distribution patterns among lots. The GM content of 15 soybean lots imported in the EU was estimated (using real-time PCR methodology) analyzing 100 increments systematically sampled from each lot. 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 GM material distribution is heterogenous highlights the need to develop sampling protocols based on statistical models free of distribution requirements.JRC.D.2-Reference material

Kernel Lot Distribution Assessment (KeLDA)- A Study on the Distribution of GMO in Large Soybean Shipments

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 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 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.JRC.I.6-Biotechnology and GMO