Erban, Alexander; Fehrle, Ines; Martinez-Seidel, Federico; Brigante, Federico; Mas, Agustin Lucini; Baroni, Veronica; Wunderlin, Daniel; Kopka, Joachim published the artcile< Discovery of food identity markers by metabolomics and machine learning technology>, Electric Literature of 87-73-0, the main research area is Salvia Linum Sesamum food processing marker metabolomics.
Verification of food authenticity establishes consumer trust in food ingredients and components of processed food. Next to genetic or protein markers, chems. are unique identifiers of food components. Non-targeted metabolomics is ideally suited to screen food markers when coupled to efficient data anal. 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. Chem. fractions of non-processed seeds and of wheat cookies with seed ingredients were profiled. RF technol. 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 technol. 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 anal. technologies is advised and further testing of a wide range of seeds and food processing methods.
Scientific Reports published new progress about Flax. 87-73-0 belongs to class alcohols-buliding-blocks, and the molecular formula is C6H10O8, Electric Literature of 87-73-0.
Referemce:
Alcohol – Wikipedia,
Alcohols – Chemistry LibreTexts