4.7 Review

Authentication of organically grown plants - advantages and limitations of atomic spectroscopy for multi-element and stable isotope analysis

Journal

TRAC-TRENDS IN ANALYTICAL CHEMISTRY
Volume 59, Issue -, Pages 73-82

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.trac.2014.04.008

Keywords

Adulteration; Atomic spectroscopy; Authenticity; Fraud; Inductively-coupled plasma-mass spectrometry; Isotope ratio-mass spectrometry; Multi-element analysis; Organic agriculture; Plant; Stable-isotope analysis

Funding

  1. CORE Organic II Funding Bodies
  2. being partners of the FP7 ERA-Net Project
  3. CORE Organic II (Coordination of European Transnational Research in Organic Food and Farming systems via AuthenticFood Project [249667]
  4. Ministry of Food, Agriculture and Fisheries, Denmark, via the OrgTrace Research Project [3304-FOTO-05-45-01]
  5. Danish Council for Independent Research - Technology and Production Sciences [DFF-1337-00055]
  6. Department for Environment, Food & Rural affairs, United Kingdom

Ask authors/readers for more resources

Organic food products are believed to be healthier, safer and more environment-friendly than their conventional counterparts and are sold at premium prices. Consequently, adulteration of organic plants and fraudulent activities for economic profit are increasing. This has spurred the development of sophisticated analytical procedures for testing authenticity. We review the use of multi-element and stable-isotope analysis based on atomic spectroscopy for discriminating between organic and conventional plants. We conclude that inductively-coupled plasma-mass spectrometry, stable-isotope analysis of bulk plant tissue, and compound-specific isotope analysis based on isotope ratio-mass spectrometry are promising tools for documenting the fertilization history of organic plants. However, these techniques are challenged by the potential diversity of fertilization practices of organic and conventional plant production. We therefore recommend that analytical techniques are combined and coupled with chemometrics to develop statistical models that can classify the agricultural origin of plant products. (C) 2014 Elsevier B.V. All rights reserved.

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