In trades related to fisheries and aquaculture, fraud is a major issue. The scam usually leverages on parameters such as species identity, production method, and geographical origin.
Take the case of the shrimp trade. It is a global business which feeds the staple food systems of several continents. But, often, the species and their geographical origin are mis-labeled. Scientists from across the world have come up with several methods to screen for fraudulent practices but most of them involve tedious experimental setups and complex databases.
A team of scientists from the ICAR-Central Institute of Fisheries Technology, Kochi, the United Kingdom, and the Czech Republic recently came up with a simple technique to tag shrimp species and their origin.
Each species has its own unique identity in terms of the metabolic chemical profile. The geological origin also leaves a mark on the profile because food availability for shrimps varies with location. Using high resolution mass spectrometry, one can get a profile of the chemicals for each species. Once this database is created for each species from different regions, it is easy to identify shrimp species and origin by comparing profiles.
The researchers made profiles of geotagged Madagascan wild tiger prawn, Argentinian red shrimp, Viet king prawn, Srilankan farmed tiger prawn, and Indian pink shrimp.
They first used data from two other chemical-based identification methods to verify the metabolome-based tagging. Then they checked the strength of the technique by metabolomic fingerprinting and verifying shrimps from different species and geographical regions.
The scientists are sure that this strategy of combining metabolome database and spectroscopy can bypass the technical difficulties of the previous methods. It is now up to the shrimp industry to adopt it to curb fraud in shrimp trade. The study also opens up a path for using spectroscopy-based metabolome fingerprinting in other fresh and processed food trades. The method will turn out to be faster and cheaper than conventional genomic fingerprinting.
J. Chrom. A., 1599: 75-84 (2019);
Siva Shakthi A