Determining and verifying product provenance remains a critical challenge in global supply chains, particularly as geopolitical conflicts and shifting borders create new incentives for misrepresentation of commodities, such as hiding the origin of illegally harvested timber or agriculture grown on illegally cleared land. Stable Isotope Ratio Analysis (SIRA), combined with Gaussian process regression-based isoscapes, has emerged as a powerful tool for geographic origin verification. However, the effectiveness of these models is often constrained by data scarcity and suboptimal dataset selection. In this work, we introduce a novel data valuation framework designed to enhance the selection and utilization of training data for machine learning models applied in SIRA. By prioritizing high-informative samples, our approach improves model robustness and predictive accuracy across diverse datasets and geographies. We validate our methodology with extensive experiments, demonstrating its potential to significantly enhance provenance verification, mitigate fraudulent trade practices, and strengthen regulatory enforcement of global supply chains.
View on arXiv@article{yousuf2025_2502.15177,
title={ Optimizing Product Provenance Verification using Data Valuation Methods },
author={ Raquib Bin Yousuf and Hoang Anh Just and Shengzhe Xu and Brian Mayer and Victor Deklerck and Jakub Truszkowski and John C. Simeone and Jade Saunders and Chang-Tien Lu and Ruoxi Jia and Naren Ramakrishnan },
journal={arXiv preprint arXiv:2502.15177},
year={ 2025 }
}