Advancing fraud detection in New Zealand Ma<overline>nuka honey: Integrating hyperspectral imaging and GANomaly-based one-class classification

New Zealand Ma<overline>nuka honey has become a prime target for adulteration due to its high commercial value. Given the diverse possibilities of fraudulent activities, training a supervised model that exhaustively covers all potential fraud scenarios is challenging. This study presents a new method for detecting fraudulent behavior in New Zealand Ma<overline>nuka honey by combining hyperspectral imaging (HSI) with the GANomaly-based One-Class Classification method. We collected 18 different UMF-graded pure Ma<overline>nuka honey samples from five New Zealand brands, which were used for training. The model was tested on fraudulent honey, including aged and syrupadulterated honey, and compared with the traditional One-Class Classification methods. The results demonstrate that the HSI combined with the GANomaly method achieved 100% discrimination for all test samples, outperforming the standard rival techniques. In conclusion, this research developed a versatile model capable of detecting honey fraudulent behavior, showing significant practical implications for honey quality assessment.