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Oregano has been identified as one of the herbs most susceptible to adulteration. Methods based on DNA, spectroscopic analysis and even microscopy have already been used. In this paper, a new approach for authentication of oregano, which combines metabarcoding by NGS (next generation sequencing) and metabolomics/chemometrics by NMR, has been developed. The industry standard for oregano permits only 2% extraneous matter. A previous survey on oregano has shown that the most common plant adulterants are olive, sweet marjoram and myrtle leaves, and non-leaf plant material. In this study, 92 oregano, 38 sweet marjoram, and 2 olive leaf samples from 6 different countries in total were used. Metabarcoding by NGS was used to identify the nature of oregano products and possible adulterations. Metabolomic profiles obtained by NMR correlated well with oregano species and their regional origin. Using chemometric analysis, it was possible to quantify of the percentage of an adulterant with error rates of 3–7%.

Read the open access paper here

This research provides validated methods for specific food adulteration scenarios, guidance on general MSI validation, and recommendations on technology transfer and feasibility of developing additional MSI related resources (e.g. an MSI database). The project builds on a previous FSA (Food Standards Agency) project, which demonstrated proof-of-principle on the applicability of MSI as a rapid, multi-analyte, non-targeted and non-invasive screening approach for food and feed analysis. In this project, 5 validated methods were successfully developed: adulteration in oregano, presence of offal in meat, ground peanut in ground almond, presence of pork in beef products, and presence of almond in commercial paprika samples, where the application, scope and key performance characteristics were captured for each application. A sixth single fully validated method for determination of multiple fish species was not successful, thought mainly due to the broad scope of the method and associated data used to build the models. The report also identifies six further areas of work that can give greater applicability of imaging technology to food and feed analysis.

The report is available here.