Honey Authentication with Machine Learning Augmented Bright-Field Microscopy
This addresses honey mislabelling and adulteration, a global economic and environmental issue, but appears incremental as it builds on existing microscopy techniques.
The paper tackles honey fraud by proposing a machine learning method to identify fraudulent honey using augmented microscopy, though no specific results or numbers are provided.
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.