Data in support of the detection of genetically modified organisms (GMOs) in food and feed samples

Food and feed samples were randomly collected from different sources, including local and imported materials from the Syrian local market. These included maize, barley, soybean, fresh food samples and raw material. GMO detection was conducted by PCR and nested PCR-based techniques using specific pri...

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Bibliographic Details
Main Authors: Noor Alasaad, Hussein Alzubi, Ahmad Abdul Kader
Format: Article
Language:English
Published: Elsevier 2016-06-01
Series:Data in Brief
Online Access:http://www.sciencedirect.com/science/article/pii/S2352340916300579
Description
Summary:Food and feed samples were randomly collected from different sources, including local and imported materials from the Syrian local market. These included maize, barley, soybean, fresh food samples and raw material. GMO detection was conducted by PCR and nested PCR-based techniques using specific primers for the most used foreign DNA commonly used in genetic transformation procedures, i.e., 35S promoter, T-nos, epsps, cryIA(b) gene and nptII gene.The results revealed for the first time in Syria the presence of GM foods and feeds with glyphosate-resistant trait of P35S promoter and NOS terminator in the imported soybean samples with high frequency (5 out of the 6 imported soybean samples). While, tests showed negative results for the local samples. Also, tests revealed existence of GMOs in two imported maize samples detecting the presence of 35S promoter and nos terminator. Nested PCR results using two sets of primers confirmed our data.The methods applied in the brief data are based on DNA analysis by Polymerase Chain Reaction (PCR). This technique is specific, practical, reproducible and sensitive enough to detect up to 0.1% GMO in food and/or feedstuffs. Furthermore, all of the techniques mentioned are economic and can be applied in Syria and other developing countries. For all these reasons, the DNA-based analysis methods were chosen and preferred over protein-based analysis.
ISSN:2352-3409