English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Discovery of food identity markers by metabolomics and machine learning technology

Erban, A., Fehrle, I., Martinez-Seidel, F., Brigante, F., Más, A. L., Baroni, V., et al. (2019). Discovery of food identity markers by metabolomics and machine learning technology. Scientific Reports, 9(1): 9697. doi:10.1038/s41598-019-46113-y.

Item is

Basic

show hide
Genre: Journal Article

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Erban, A.1, Author              
Fehrle, I.1, Author              
Martinez-Seidel, F.1, Author              
Brigante, Federico2, Author
Más, Agustín Lucini2, Author
Baroni, Veronica2, Author
Wunderlin, Daniel2, Author
Kopka, J.1, Author              
Affiliations:
1Applied Metabolome Analysis, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753338              
2external, ou_persistent22              

Content

show
hide
Free keywords: -
 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.

Details

show
hide
Language(s): eng - English
 Dates: 2019
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1038/s41598-019-46113-y
Other: Erban2019
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Scientific Reports
  Abbreviation : Sci. Rep.
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: London, UK : Nature Publishing Group
Pages: - Volume / Issue: 9 (1) Sequence Number: 9697 Start / End Page: - Identifier: ISSN: 2045-2322
CoNE: https://pure.mpg.de/cone/journals/resource/2045-2322