Literature DB >> 24425170

Metabolite profiling on wheat grain to enable a distinction of samples from organic and conventional farming systems.

Anja Bonte1, Heiko Neuweger, Alexander Goesmann, Cécile Thonar, Paul Mäder, Georg Langenkämper, Karsten Niehaus.   

Abstract

BACKGROUND: Identification of biomarkers capable of distinguishing organic and conventional products would be highly welcome to improve the strength of food quality assurance. Metabolite profiling was used for biomarker search in organic and conventional wheat grain (Triticum aestivum L.) of 11 different old and new bread wheat cultivars grown in the DOK system comparison trial. Metabolites were extracted using methanol and analysed by gas chromatography-mass spectrometry.
RESULTS: Altogether 48 metabolites and 245 non-identified metabolites (TAGs) were detected in the cultivar Runal. Principal component analysis showed a sample clustering according to farming systems and significant differences in peak areas between the farming systems for 10 Runal metabolites. Results obtained from all 11 cultivars indicated a greater influence of the cultivar than the farming system on metabolite concentrations. Nevertheless, a t-test on data of all cultivars still detected 5 metabolites and 11 TAGs with significant differences between the farming systems.
CONCLUSION: Based on individual cultivars, metabolite profiling showed promising results for the categorization of organic and conventional wheat. Further investigations are necessary with wheat from more growing seasons and locations before definite conclusions can be drawn concerning the feasibility to evolve a combined set of biomarkers for organically grown wheat using metabolite profiles.
© 2014 Society of Chemical Industry.

Entities:  

Keywords:  GC-MS; biomarker; metabolite profiling; organic farming; winter wheat

Mesh:

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Year:  2014        PMID: 24425170     DOI: 10.1002/jsfa.6566

Source DB:  PubMed          Journal:  J Sci Food Agric        ISSN: 0022-5142            Impact factor:   3.638


  3 in total

1.  Learning to Classify Organic and Conventional Wheat - A Machine Learning Driven Approach Using the MeltDB 2.0 Metabolomics Analysis Platform.

Authors:  Nikolas Kessler; Anja Bonte; Stefan P Albaum; Paul Mäder; Monika Messmer; Alexander Goesmann; Karsten Niehaus; Georg Langenkämper; Tim W Nattkemper
Journal:  Front Bioeng Biotechnol       Date:  2015-03-24

2.  Metabolomics for organic food authentication: Results from a long-term field study in carrots.

Authors:  Elena Cubero-Leon; Olivier De Rudder; Alain Maquet
Journal:  Food Chem       Date:  2017-07-01       Impact factor: 7.514

3.  Comprehensive profiling of semi-polar phytochemicals in whole wheat grains (Triticum aestivum) using liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry.

Authors:  Leslie Tais; Hartwig Schulz; Christoph Böttcher
Journal:  Metabolomics       Date:  2021-01-27       Impact factor: 4.290

  3 in total

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