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Misdescription of fish species is a major global problem. DNA identification of single fish species is now well researched, but authentication and quantification of fish species in mixtures remains a challenge. An international group of scientists have applied a novel high-throughput shotgun DNA sequencing and mass spectrometry-based proteomics in parallel on the same samples to estimate the relative abundance of fish species in a mixed sample. Seven species of fish were used for the individual fish samples, but the mixture was only made up of 4 species (Atlantic cod (Gadus morhua), Atlantic haddock (Melanogrammus aeglefinus), Nile tilapia (Oreochromis niloticus), and platyfish (Xiphophorus maculatus)).The DNA sequencing approach applying masked reference libraries was able to discriminate and predict relative abundances of different fish species in the mixed sample with high accuracy. Also the proteomics tools based on direct spectra comparisons showed feasibility in the identification of individual fish species, and the estimation of their respective relative abundances in a mixed sample. 

The results showed that DNA sequencing was more accurate for the quantification of closely related species, but proteomics was more accurate for quantification at the taxonomic family level. In practice, a possible tiered approach, taking advantage of the specificity of DNA sequencing and the abundance accuracy of proteomics would be best suited for tackling fish species misdescription.

 Read the full open access paper here

MALDI-TOF mass spectrometry has been the technique of choice in many applications of food authentication because of requiring simple pretreatments even with complex samples, its ease of use, and speed in giving results. This review discusses the advantages of using MALDI-TOF, and examines its published application to authenticating milk and dairy products, oils, meat, fish and seafood, fruits and vegetables, truffles, and even insect proteins. 

Read the full open access paper here

This article summarises the authenticity analytical approaches (based on building blocks of food) to identify the most suitable procedures to prevent food fraud. The methods described are not exhaustive, but cover the majority of approaches that are currently
undertaken. In particular, DNA methodology, proteomics, chromatographic methods and stable isotope ratio analysis are discussed.

Read the full article here

This project aimed to develop and validate a multi-species proteomics screening tool for meat species verification in processed meat products. The method covers nine meat species: beef, pork, horse, goat, lamb, donkey, rabbit, chicken and turkey. The research has improved the original database of marker peptides for the nine animal species studied. The work has expanded previous research using high-resolution mass spectrometry to achieve detection of pork, horse, donkey, lamb, rabbit and chicken at 1% (w/w) adulteration levels. It has developed and validated a triple quadrupole mass spectrometry method suitable for simultaneous identification of the nine meat species in processed meat products. Beef, pork, horse and chicken products were used to carry out an intra-laboratory method validation.

Project FA0166 is in the Research Section of the website

German and Italian researchers have used a proteomics based assay to distinguish wild from farmed salmon. A total of 13 farmed and 13 wild Canadian salmon (Salmo salar) species were extracted and digested with trypsin. The peptide digest was analysed by an optimised LC- MS system(quadrupole time-of-flight mass spectrometer) followed by statistical analysis based on principal component (PC) analysis.This untargeted approach, using a data-independent acquisition MS scheme, demonstrated the ability to effectively discriminate salmon belonging to the two classes. Furthermore, selected peptides showing high loadings on PC1 could represent potential targeted candidate peptide markers able to discriminate farmed from wild-type salmon.

 Read the abstract here and the complete journal paper

In this review by Spanish researchers, an updated, comprehensive and balanced overview of the recent studies (2015-2018) that have applied omics-based technologies for the authentication of food is given. The omics-based molecular tools discussed in the review include genomics, proteomics and metabolomics-based methods.

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Russian researchers have published a paper on their development of proteomic and peptide identification to identify pork, beef , horse and poultry in meat produced after slaughter. The methodology development can identify peptides which occur in specific tissues or fluids associated with meat species. The researchers are planning the next stage of using the peptide information of the raw materials to identify species in meat products.

  Read the full paper here

The CFIA has signed a science-sharing MOU with the French Food Regulator (ANSES). The agreement will strengthen and formalise scientific cooperation on innovative research taking place at the CFIA network of 13 reference and research laboratories, and the ANSES network of 11 laboratories throughout France.The collaboration is envisioned to further develop research on genomics, and proteomics.  

Read the article at: CFIA Agreement with ANSES

JRC (the EU Commission's Joint Research Centre) has just published a report summarising 20 years of EU funded projects on the development of emerging technologies to identify fish species and improve fish trade traceability. The report covers methods based on DNA amplification, DNA sequencing, DNA arrays, proteomics and chemical profiling.

Read the report at: JRC fish species report

This is a review paper, which focuses on important issues for consideration when validating a molecular biology assay and the various factors that can impact on the measurement uncertainty of a result associated with molecular biology approaches used in detection of food fraud, with a particular focus on quantitative PCR-based and proteomics assays.

Read more at:  http://pubs.rsc.org/en/content/articlelanding/2015/an/c5an01392e#!divAbstract