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Discrimination of conventional and organic white cabbage from a long-term field trial study using untargeted LC-MS-based metabolomics

The influence of organic and conventional farming practices on the content of single nutrients in plants is disputed in the scientific literature. Here, large-scale untargeted LC-MS-based metabolomics was used to compare the composition of white cabbage from organic and conventional agriculture, mea...

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Autores principales: Mie, Axel, Laursen, Kristian Holst, Åberg, K. Magnus, Forshed, Jenny, Lindahl, Anna, Thorup-Kristensen, Kristian, Olsson, Marie, Knuthsen, Pia, Larsen, Erik Huusfeldt, Husted, Søren
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3984666/
https://www.ncbi.nlm.nih.gov/pubmed/24618989
http://dx.doi.org/10.1007/s00216-014-7704-0
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author Mie, Axel
Laursen, Kristian Holst
Åberg, K. Magnus
Forshed, Jenny
Lindahl, Anna
Thorup-Kristensen, Kristian
Olsson, Marie
Knuthsen, Pia
Larsen, Erik Huusfeldt
Husted, Søren
author_facet Mie, Axel
Laursen, Kristian Holst
Åberg, K. Magnus
Forshed, Jenny
Lindahl, Anna
Thorup-Kristensen, Kristian
Olsson, Marie
Knuthsen, Pia
Larsen, Erik Huusfeldt
Husted, Søren
author_sort Mie, Axel
collection PubMed
description The influence of organic and conventional farming practices on the content of single nutrients in plants is disputed in the scientific literature. Here, large-scale untargeted LC-MS-based metabolomics was used to compare the composition of white cabbage from organic and conventional agriculture, measuring 1,600 compounds. Cabbage was sampled in 2 years from one conventional and two organic farming systems in a rigidly controlled long-term field trial in Denmark. Using Orthogonal Projection to Latent Structures–Discriminant Analysis (OPLS-DA), we found that the production system leaves a significant (p = 0.013) imprint in the white cabbage metabolome that is retained between production years. We externally validated this finding by predicting the production system of samples from one year using a classification model built on samples from the other year, with a correct classification in 83 % of cases. Thus, it was concluded that the investigated conventional and organic management practices have a systematic impact on the metabolome of white cabbage. This emphasizes the potential of untargeted metabolomics for authenticity testing of organic plant products. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00216-014-7704-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-39846662014-04-23 Discrimination of conventional and organic white cabbage from a long-term field trial study using untargeted LC-MS-based metabolomics Mie, Axel Laursen, Kristian Holst Åberg, K. Magnus Forshed, Jenny Lindahl, Anna Thorup-Kristensen, Kristian Olsson, Marie Knuthsen, Pia Larsen, Erik Huusfeldt Husted, Søren Anal Bioanal Chem Research Paper The influence of organic and conventional farming practices on the content of single nutrients in plants is disputed in the scientific literature. Here, large-scale untargeted LC-MS-based metabolomics was used to compare the composition of white cabbage from organic and conventional agriculture, measuring 1,600 compounds. Cabbage was sampled in 2 years from one conventional and two organic farming systems in a rigidly controlled long-term field trial in Denmark. Using Orthogonal Projection to Latent Structures–Discriminant Analysis (OPLS-DA), we found that the production system leaves a significant (p = 0.013) imprint in the white cabbage metabolome that is retained between production years. We externally validated this finding by predicting the production system of samples from one year using a classification model built on samples from the other year, with a correct classification in 83 % of cases. Thus, it was concluded that the investigated conventional and organic management practices have a systematic impact on the metabolome of white cabbage. This emphasizes the potential of untargeted metabolomics for authenticity testing of organic plant products. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00216-014-7704-0) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2014-03-12 2014 /pmc/articles/PMC3984666/ /pubmed/24618989 http://dx.doi.org/10.1007/s00216-014-7704-0 Text en © The Author(s) 2014 https://creativecommons.org/licenses/by/4.0/ Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
spellingShingle Research Paper
Mie, Axel
Laursen, Kristian Holst
Åberg, K. Magnus
Forshed, Jenny
Lindahl, Anna
Thorup-Kristensen, Kristian
Olsson, Marie
Knuthsen, Pia
Larsen, Erik Huusfeldt
Husted, Søren
Discrimination of conventional and organic white cabbage from a long-term field trial study using untargeted LC-MS-based metabolomics
title Discrimination of conventional and organic white cabbage from a long-term field trial study using untargeted LC-MS-based metabolomics
title_full Discrimination of conventional and organic white cabbage from a long-term field trial study using untargeted LC-MS-based metabolomics
title_fullStr Discrimination of conventional and organic white cabbage from a long-term field trial study using untargeted LC-MS-based metabolomics
title_full_unstemmed Discrimination of conventional and organic white cabbage from a long-term field trial study using untargeted LC-MS-based metabolomics
title_short Discrimination of conventional and organic white cabbage from a long-term field trial study using untargeted LC-MS-based metabolomics
title_sort discrimination of conventional and organic white cabbage from a long-term field trial study using untargeted lc-ms-based metabolomics
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3984666/
https://www.ncbi.nlm.nih.gov/pubmed/24618989
http://dx.doi.org/10.1007/s00216-014-7704-0
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