Adapting mechanistic isotope models to trace the geographical origin of agricultural products

Cueni, Florian. Adapting mechanistic isotope models to trace the geographical origin of agricultural products. 2020, Doctoral Thesis, University of Basel, Faculty of Science.


Official URL: https://edoc.unibas.ch/90039/

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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.
Advisors:Kahmen, Ansgar and Salzburger, Walter and West, Jason B
Faculties and Departments:05 Faculty of Science > Departement Umweltwissenschaften > Integrative Biologie > Physiological Plant Ecology (Kahmen)
UniBasel Contributors:Kahmen, Ansgar and Salzburger, Walter
Item Type:Thesis
Thesis Subtype:Doctoral Thesis
Thesis no:14834
Thesis status:Complete
Number of Pages:211
Identification Number:
  • urn: urn:nbn:ch:bel-bau-diss148342
edoc DOI:
Last Modified:02 Nov 2022 05:30
Deposited On:01 Nov 2022 14:57

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