Literature DB >> 20151648

Influence of the input system (conventional versus organic farming) on metabolite profiles of maize ( Zea mays ) kernels.

Richard M Röhlig1, Karl-Heinz Engel.   

Abstract

Maize ( Zea mays ) kernels grown conventionally and organically, respectively, were investigated using a gas chromatography/mass spectrometry (GC/MS)-based metabolite profiling methodology. By analysis of three cultivars grown at two locations with different input systems and at a third location where both organic and conventional farming were applied, the impact of the growing regime on the metabolite spectrum should be put into the context of natural variability. The applied analytical approach involved consecutive extraction of freeze-dried maize flour and subsequent subfractionation. Approximately 300 compounds from a broad spectrum of chemical classes were detected, of which 167 were identified. The metabolite profiling data were statistically assessed via principal component analysis (PCA) and analysis of variance (ANOVA). The PCA demonstrated that the observed separations were mainly due to genetic differences (cultivars) and environmental influences. The different input systems (conventional/organic) only led to minor differentiations. ANOVA and quantification of selected constituents confirmed these observations. Only three metabolites (malic acid, myo-inositol, and phosphate) were consistently different because of the employed input system if samples from all field trials were considered.

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Year:  2010        PMID: 20151648     DOI: 10.1021/jf904101g

Source DB:  PubMed          Journal:  J Agric Food Chem        ISSN: 0021-8561            Impact factor:   5.279


  5 in total

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

Authors:  Axel Mie; Kristian Holst Laursen; K Magnus Åberg; Jenny Forshed; Anna Lindahl; Kristian Thorup-Kristensen; Marie Olsson; Pia Knuthsen; Erik Huusfeldt Larsen; Søren Husted
Journal:  Anal Bioanal Chem       Date:  2014-03-12       Impact factor: 4.142

2.  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

3.  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

Review 4.  Human health implications of organic food and organic agriculture: a comprehensive review.

Authors:  Axel Mie; Helle Raun Andersen; Stefan Gunnarsson; Johannes Kahl; Emmanuelle Kesse-Guyot; Ewa Rembiałkowska; Gianluca Quaglio; Philippe Grandjean
Journal:  Environ Health       Date:  2017-10-27       Impact factor: 5.984

5.  Metabolomic Approaches to Studying the Response to Drought Stress in Corn (Zea mays) Cobs.

Authors:  Isabella Gaffney; Jonathan Brett Sallach; Julie Wilson; Edmund Bergström; Jane Thomas-Oates
Journal:  Metabolites       Date:  2021-07-03
  5 in total

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