Sweet Deception: Detecting Honey Adulteration Using Generative AI and Hyperspectral Imaging

Abstract

Food safety and authenticity are critical global concerns, increasingly challenged by the complexity of modern supply chains and the rise of sophisticated fraud techniques. In this paper, we propose a novel approach for detecting honey adulteration by combining hyperspectral imaging with a generative AI framework based on deep autoencoders. Our method leverages the autoencoder’s ability to model the intrinsic spectral patterns of pure honey, enabling the detection of subtle anomalies introduced by adulterants. Through a rigorous binary classification task, our system achieves a high F1-score, demonstrating both accuracy and robustness. In addition, we provide an indepth analysis of reconstruction quality and error distributions, highlighting the capacity of the model for interpretable and reliable fraud detection. The proposed methodology offers a scalable and data-efficient solution with broad applicability to food authentication, paving the way for future deployment in real-time quality assurance systems.