English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/156478
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:


Advances in the analysis of complex food matrices: Species identification in surimibased products using Next Generation Sequencing technologies

AuthorsGiusti, Alice; Armani, Andrea; González Sotelo, Carmen
Issue Date2017
PublisherPublic Library of Science
CitationPLoS ONE 12(10): e0185586 (2017)
AbstractThe Next Generation Sequencing (NGS) technologies represent a turning point in the food inspection field, particularly for species identification in matrices composed of a blend of two or more species. In this study NGS technologies were applied by testing the usefulness of the Ion Torrent Personal Genome Machine (PGM) in seafood traceability. Sixteen commercial surimi samples produced both in EU and non-EU countries were analysed. Libraries were prepared using a universal primer pair able to amplify a short 16SrRNA fragment from a wide range of fish and cephalopod species. The mislabelling rate of the samples was also evaluated. Overall, DNA from 13 families, 19 genera and 16 species of fish, and from 3 families, 3 genera and 3 species of cephalopods was found with the analysis. Samples produced in non-EU countries exhibited a higher variability in their composition. 37.5% of the surimi products were found to be mislabelled. Among them, 25% voluntary declared a species different from those identified and 25% (all produced in non-EU countries) did not report the presence of molluscs on the label, posing a potential health threat for allergic consumers. The use of vulnerable species was also proved. Although the protocol should be further optimized, PGM platform proved to be a useful tool for the analysis of complex, highly processed products
Description18 páginas, 4 tablas, 4 figuras.-- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Publisher version (URL)https://doi.org/10.1371/journal.pone.0185586
Appears in Collections:(IIM) Artículos
Files in This Item:
File Description SizeFormat 
Advances_analysis_complex_2017.pdf4,68 MBAdobe PDFThumbnail
Show full item record
Review this work

Related articles:

WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.