Using plant physiological stable oxygen isotope models to counter food fraud

Abstract Fraudulent food products, especially regarding false claims of geographic origin, impose economic damages of $30–$40 billion per year. Stable isotope methods, using oxygen isotopes (δ18O) in particular, are the leading forensic tools for identifying these crimes. Plant physiological stable...

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Autores principales:	Florian Cueni, Daniel B. Nelson, Markus Boner, Ansgar Kahmen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5e2dc18b99cb4f20ac3d6680bb851ee4
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spelling	oai:doaj.org-article:5e2dc18b99cb4f20ac3d6680bb851ee42021-12-02T18:53:19ZUsing plant physiological stable oxygen isotope models to counter food fraud10.1038/s41598-021-96722-92045-2322https://doaj.org/article/5e2dc18b99cb4f20ac3d6680bb851ee42021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96722-9https://doaj.org/toc/2045-2322Abstract Fraudulent food products, especially regarding false claims of geographic origin, impose economic damages of $30–$40 billion per year. Stable isotope methods, using oxygen isotopes (δ18O) in particular, are the leading forensic tools for identifying these crimes. Plant physiological stable oxygen isotope models simulate how precipitation δ18O values and climatic variables shape the δ18O values of water and organic compounds in plants. These models have the potential to simplify, speed up, and improve conventional stable isotope applications and produce temporally resolved, accurate, and precise region-of-origin assignments for agricultural food products. However, the validation of these models and thus the best choice of model parameters and input variables have limited the application of the models for the origin identification of food. In our study we test model predictions against a unique 11-year European strawberry δ18O reference dataset to evaluate how choices of input variable sources and model parameterization impact the prediction skill of the model. Our results show that modifying leaf-based model parameters specifically for fruit and with product-independent, but growth time specific environmental input data, plant physiological isotope models offer a new and dynamic method that can accurately predict the geographic origin of a plant product and can advance the field of stable isotope analysis to counter food fraud.Florian CueniDaniel B. NelsonMarkus BonerAnsgar KahmenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Florian Cueni Daniel B. Nelson Markus Boner Ansgar Kahmen Using plant physiological stable oxygen isotope models to counter food fraud
description	Abstract Fraudulent food products, especially regarding false claims of geographic origin, impose economic damages of $30–$40 billion per year. Stable isotope methods, using oxygen isotopes (δ18O) in particular, are the leading forensic tools for identifying these crimes. Plant physiological stable oxygen isotope models simulate how precipitation δ18O values and climatic variables shape the δ18O values of water and organic compounds in plants. These models have the potential to simplify, speed up, and improve conventional stable isotope applications and produce temporally resolved, accurate, and precise region-of-origin assignments for agricultural food products. However, the validation of these models and thus the best choice of model parameters and input variables have limited the application of the models for the origin identification of food. In our study we test model predictions against a unique 11-year European strawberry δ18O reference dataset to evaluate how choices of input variable sources and model parameterization impact the prediction skill of the model. Our results show that modifying leaf-based model parameters specifically for fruit and with product-independent, but growth time specific environmental input data, plant physiological isotope models offer a new and dynamic method that can accurately predict the geographic origin of a plant product and can advance the field of stable isotope analysis to counter food fraud.
format	article
author	Florian Cueni Daniel B. Nelson Markus Boner Ansgar Kahmen
author_facet	Florian Cueni Daniel B. Nelson Markus Boner Ansgar Kahmen
author_sort	Florian Cueni
title	Using plant physiological stable oxygen isotope models to counter food fraud
title_short	Using plant physiological stable oxygen isotope models to counter food fraud
title_full	Using plant physiological stable oxygen isotope models to counter food fraud
title_fullStr	Using plant physiological stable oxygen isotope models to counter food fraud
title_full_unstemmed	Using plant physiological stable oxygen isotope models to counter food fraud
title_sort	using plant physiological stable oxygen isotope models to counter food fraud
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5e2dc18b99cb4f20ac3d6680bb851ee4
work_keys_str_mv	AT floriancueni usingplantphysiologicalstableoxygenisotopemodelstocounterfoodfraud AT danielbnelson usingplantphysiologicalstableoxygenisotopemodelstocounterfoodfraud AT markusboner usingplantphysiologicalstableoxygenisotopemodelstocounterfoodfraud AT ansgarkahmen usingplantphysiologicalstableoxygenisotopemodelstocounterfoodfraud
_version_	1718377327942959104

Using plant physiological stable oxygen isotope models to counter food fraud

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