Variational Autoencoders for Generating Hyperspectral Imaging Honey Adulteration Data

Tessa Phillips, Waleed Abdulla; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 214-221

Honey fraud and adulteration are an increasing concern globally. Hyperspectral imaging and machine learning can detect adulterated honey within a known set of honey, where we have captured data at different sugar concentrations. Previous work in this area has used a minimal number of honey types, as sample preparation and data capture is a time-consuming process. This paper develops a new approach using variational autoencoders (VAEs) for generating adulterated honey data for unseen honey types. The results show that the binary adulteration detector can achieve on average 81.3% accuracy on unseen honey types by adding the generated data to the existing training data. Without including the generated data while training, the classifier can only achieve 44% on unseen honey types.