Understanding δ 18O and δ 2H values of agricultural products like fruit is of particular scientific interest in plant physiology, ecology, and forensic studies. Applications of mechanistic stable isotope models to predict δ 18O and δ 2H values of water and organic compounds in fruit, however, are hindered by a lack of empirical parameterizations and validations. We addressed this lack of data by experimentally evaluating model parameter values required to model δ 18O and δ 2H values of water and organic compounds in berries and leaves from strawberry and raspberry plants grown at different relative humidities. Our study revealed substantial differences between leaf and berry isotope values, consistent across the different RH treatments. We demonstrated that existing isotope models can reproduce water and organic δ 18O and δ 2H values for leaves and berries. Yet, these simulations require organ-specific model parameterization to accurately predict δ 18O and δ 2H values of leaf and berry tissue and water pools. We quantified these organ-specific model parameters for both species and RH conditions. Depending on the required model accuracy, species- and environment-specific model parameters may be justified. The parameter values determined in this study thus facilitate applications of stable isotope models where understanding δ 18O and δ 2H values of fruit is of scientific interest.

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.

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