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Identification of Animal Food Products with Molecular Analysis Techniques: Detection...
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Identification of Animal Food Products with Molecular Analysis Techniques: Detection of Mislabeling

A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Animal System and Management".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 3652

Special Issue Editor

Dr. Anastasia Imsiridou

E-Mail Website
Guest Editor
Department of Food Science and Technology, School of Geotechnical Sciences, International Hellenic University, Thessaloniki, Greece
Interests: population genetics; genetic identification; aquatic organisms; bioinformatics; food products; food fraud; molecular techniques

Special Issue Information

Dear Colleagues,

Food authenticity has received increased focus as a result of high profile cases of substitution/mislabeling, with many investigations also identifying sales of undeclared, endangered, or prohibited species. At the same time, the European Union regulation  requires that consumers should be appropriately informed regarding the food they consume. This is vital in order to achieve a high level of health protection and to guarantee their right to information, as well as to protect the businesses of scrupulous producers from unfair competition. DNA-based methodologies have been shown to be an ideal tool to address the problem of mislabelling, because of the sensitivity, accuracy, and ease of testing, as well as the stability of DNA under a range of food processing methods. The identification of many products can be achieved through the direct Sanger sequencing of short, standardized gene fragments, while next generation sequencing technologies have the ability to simultaneously screen multiple different genomic regions, in order to enable the identification of all animal ingredients in complex food commodities. With the authenticity of animal food products in the spotlight, food industries and control laboratories must make use of all available DNA techniques to verify their products and control their ingredient providers.

The aim of this Special Issue is to present recent research about the important subject of mislabelling detection in animal products with genetic methodologies.

Prof. Anastasia Imsiridou
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Animals is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Mislabeling
  • Animal food products
  • DNA barcoding
  • NGS
  • Real time PCR
  • Food fraud

Published Papers (1 paper)

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Research

23 pages, 5280 KiB  
Article
A 16S Next Generation Sequencing Based Molecular and Bioinformatics Pipeline to Identify Processed Meat Products Contamination and Mislabelling
by Nyaradzo Stella Chaora, Khulekani Sedwell Khanyile, Kudakwashe Magwedere, Rian Pierneef, Frederick Tawi Tabit and Farai Catherine Muchadeyi
Animals 2022, 12(4), 416; https://doi.org/10.3390/ani12040416 - 10 Feb 2022
Cited by 8 | Viewed by 2986
Abstract
Processed meat is a target in meat adulteration for economic gain. This study demonstrates a molecular and bioinformatics diagnostic pipeline, utilizing the mitochondrial 16S ribosomal RNA (rRNA) gene, to determine processed meat product mislabeling through Next-Generation Sequencing. Nine pure meat samples were collected [...] Read more.
Processed meat is a target in meat adulteration for economic gain. This study demonstrates a molecular and bioinformatics diagnostic pipeline, utilizing the mitochondrial 16S ribosomal RNA (rRNA) gene, to determine processed meat product mislabeling through Next-Generation Sequencing. Nine pure meat samples were collected and artificially mixed at different ratios to verify the specificity and sensitivity of the pipeline. Processed meat products (n = 155), namely, minced meat, biltong, burger patties, and sausages, were collected across South Africa. Sequencing was performed using the Illumina MiSeq sequencing platform. Each sample had paired-end reads with a length of ±300 bp. Quality control and filtering was performed using BBDuk (version 37.90a). Each sample had an average of 134,000 reads aligned to the mitochondrial genomes using BBMap v37.90. All species in the artificial DNA mixtures were detected. Processed meat samples had reads that mapped to the Bos (90% and above) genus, with traces of reads mapping to Sus and Ovis (2–5%) genus. Sausage samples showed the highest level of contamination with 46% of the samples having mixtures of beef, pork, or mutton in one sample. This method can be used to authenticate meat products, investigate, and manage any form of mislabeling. Full article
(This article belongs to the Special Issue Identification of Animal Food Products with Molecular Analysis Techniques: Detection of Mislabeling)
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