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This study (purchase required) reports development of a Loop-Mediated Isothermal Amplification (LAMP) assay to detect several common avian meat species as adulterants in raw and heat- and pressure-treated meat products. This is an on-site test, taking about 1 hour, with the results visualised by colour changes in the SYTO 24 nucleic acid marker dye.

Conserved regions of the glyceraldehyde-3-phosphate dehydrogenase (gapdh) gene were targeted to design a LAMP primer set specific to avian species. To assess the assay’s performance, six common avian species (chicken, turkey, goose, duck, ostrich, quail) and four non-avian species (sheep, cattle, goat, camel) were tested. DNA was extracted using a salt-based method, and the assay’s specificity and sensitivity were evaluated on raw, cooked, and autoclaved samples.

The authors report that the LAMP assay successfully detected chicken, turkey, goose, and duck DNA. They report detection limits of 110 femtograms chicken DNA In chicken–beef mixtures, 0.1 % chicken in raw and cooked samples and 1 % in autoclaved samples.

For the principle of LAMP, see FAN’s method explainer pages.

Photo by FitNish Media on Unsplash

This thesis (open access) set out to prove the concept that multi-spectral imaging  (MSI) could be used to build a classification model to differentiate chicken breast with undeclared added water from that with no, or legally-permitted low, added water content.

The researcher built a model based upon an in-house reference set of chicken breast samples; 12 with no added water, 12 with water added at a level that need not be legally declared (3 – 5%) and 12 with water added at a level that should be legally declared on-pack (9-11%).  The protein/water content of the samples was then calculated using classical analysis, in order to label the MSI scans.  MSI used two cameras , FX10 and  FX17.  After annotation, the samples were saved and analysed in MATLAB for model development

The researcher concluded that the method holds promise but would need a much more robust database.  With this limited database, the model could distinguish added-water from non-added-water samples but could not robustly distinguish between amounts of added water which would be legal if undeclared and those which would not be legal.

Photo by Philippe Zuber on Unsplash