Discovery of food identity markers by metabolomics and machine learning technology

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 effi...

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Autores principales:	Alexander Erban, Ines Fehrle, Federico Martinez-Seidel, Federico Brigante, Agustín Lucini Más, Veronica Baroni, Daniel Wunderlin, Joachim Kopka
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e43a47c8e9e649ffb640ab938f2e05ad
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spelling	oai:doaj.org-article:e43a47c8e9e649ffb640ab938f2e05ad2021-12-02T15:09:39ZDiscovery of food identity markers by metabolomics and machine learning technology10.1038/s41598-019-46113-y2045-2322https://doaj.org/article/e43a47c8e9e649ffb640ab938f2e05ad2019-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-46113-yhttps://doaj.org/toc/2045-2322Abstract 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.Alexander ErbanInes FehrleFederico Martinez-SeidelFederico BriganteAgustín Lucini MásVeronica BaroniDaniel WunderlinJoachim KopkaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-19 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Alexander Erban Ines Fehrle Federico Martinez-Seidel Federico Brigante Agustín Lucini Más Veronica Baroni Daniel Wunderlin Joachim Kopka Discovery of food identity markers by metabolomics and machine learning technology
description	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.
format	article
author	Alexander Erban Ines Fehrle Federico Martinez-Seidel Federico Brigante Agustín Lucini Más Veronica Baroni Daniel Wunderlin Joachim Kopka
author_facet	Alexander Erban Ines Fehrle Federico Martinez-Seidel Federico Brigante Agustín Lucini Más Veronica Baroni Daniel Wunderlin Joachim Kopka
author_sort	Alexander Erban
title	Discovery of food identity markers by metabolomics and machine learning technology
title_short	Discovery of food identity markers by metabolomics and machine learning technology
title_full	Discovery of food identity markers by metabolomics and machine learning technology
title_fullStr	Discovery of food identity markers by metabolomics and machine learning technology
title_full_unstemmed	Discovery of food identity markers by metabolomics and machine learning technology
title_sort	discovery of food identity markers by metabolomics and machine learning technology
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/e43a47c8e9e649ffb640ab938f2e05ad
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Discovery of food identity markers by metabolomics and machine learning technology

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