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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

The  development of rapid non-destructive hand held devices for testing for food authenticity has been growing at a pace in recent years. US researchers have developed the MasSpec Pen technology, which is placed on the food and connects with a mass spectrometer that employs a solvent droplet and gives an answer in 15 seconds.The MasSpec Pen has been used to authenticate grain-fed beef, grass-fed beef, venison, cod, halibut, Atlantic salmon, sockeye salmon, and steelhead trout. Statistical models developed with the Lasso method using a training set of samples yielded per-sample accuracies of 95% for the beef model, 100% for the beef versus venison model, and 84% for the multiclass fish model. In addition, feasibility testing for classifying venison and grass-fed beef samples adulterated with grain-fed beef achieved prediction accuracies of 100% for both classifiers using test sets of samples.

Read the abstract here