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From farm to fork, via AI - a case study for distinguishing different grain cultivars

In their ongoing work, QuoData's scientists are pleased to share some of the recent developments in their latest paper - "AI-based identification of grain cultivars via non-target mass spectrometry". The paper deals with the use of non-target high-resolution mass spectrometry and the processing of the extensive data using artificial intelligence approaches.

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By employing advanced computational and biological methods, we can aim to leap forward in tackling issues of food fraud and food authentication. The work here-in involves investigating the classification and identification of different grain species, especially the classification of wheat and spelt (dinkel). The differentiation of wheat and spelt is a non-trivial problem, since both are genetically and morphologically very similar.

With this work, QuoData's scientists look forward to forge relationships and engage with new partners and stakeholders to explore, expand, and employ the proposed methodology.

You can view the paper here.

Read our previous blog post on in the build up towards this work.

Figure reproduced from https://doi.org/10.1101/2020.05.07.082065

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From farm to fork