Discovery of food identity markers by metabolomics and machine learning technology View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-07-04

AUTHORS

Alexander Erban, Ines Fehrle, Federico Martinez-Seidel, Federico Brigante, Agustín Lucini Más, Veronica Baroni, Daniel Wunderlin, Joachim Kopka

ABSTRACT

Verification of food authenticity establishes consumer trust in food ingredients and components of processed food. Next to genetic or protein markers, chemicals are unique identifiers of food components. Non-targeted metabolomics is ideally suited to screen food markers when coupled to efficient data analysis. This study explored feasibility of random forest (RF) machine learning, specifically its inherent feature extraction for non-targeted metabolic marker discovery. The distinction of chia, linseed, and sesame that have gained attention as “superfoods” served as test case. Chemical fractions of non-processed seeds and of wheat cookies with seed ingredients were profiled. RF technology classified original seeds unambiguously but appeared overdesigned for material with unique secondary metabolites, like sesamol or rosmarinic acid in the Lamiaceae, chia. Most unique metabolites were diluted or lost during cookie production but RF technology classified the presence of the seed ingredients in cookies with 6.7% overall error and revealed food processing markers, like 4-hydroxybenzaldehyde for chia and succinic acid monomethylester for linseed additions. RF based feature extraction was adequate for difficult classifications but marker selection should not be without human supervision. Combination with alternative data analysis technologies is advised and further testing of a wide range of seeds and food processing methods. More... »

PAGES

9697

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-019-46113-y

DOI

http://dx.doi.org/10.1038/s41598-019-46113-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1117737292

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/31273246


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