Honey has been collected and used by humankind as both a food and medicine for thousands of years. However, in the modern economy, honey has become subject to mislabelling and adulteration making it the third most faked food product in the world. The international scale of fraudulent honey has had both economic and environmental ramifications. In this paper, we propose a novel method of identifying fraudulent honey using machine learning augmented microscopy.
Cite
@article{arxiv.1901.00516,
title = {Honey Authentication with Machine Learning Augmented Bright-Field Microscopy},
author = {Chloe He and Alexis Gkantiragas and Gerard Glowacki},
journal= {arXiv preprint arXiv:1901.00516},
year = {2023}
}
Comments
Accepted at the 'AI for Social Good' workshop at the 32nd Conference on Neural Information Processing Systems (NeurIPS2018), Montr\'eal, Canada