MeatSpec-G: Generalized Low-Cost Spectral Imaging for Ubiquitous Meat Fraud Inspection

Meat adulteration is a significant problem that can pose health risks economic losses to consumers. Current detection methods are hindered by high costs, limited capabilities, or time-consuming sample preparation, making them only accessible in laboratory tests and can not protect the safety of end-users. This paper introduces MeatSpec, a low-cost and user-friendly system for detecting meat adulteration using spectral imaging, to move the adulteration inspection out of laboratories. MeatSpec employs a multispectral camera to reduce costs while quickly capturing spectral images, but this leads to a decrease in spectral resolution and coverage. To solve this challenge, the system uses spectral reconstruction technology and innovative designs tailored for meat adulteration detection. This includes involving adulteration-related prior information during the reconstruction training phase and incorporating contrastive learning to enlarge the distances among reconstructed samples belonging to various adulteration types. Additionally, we devise distinct feature extractors for different bands based on characteristics of the reconstructed spectra and employ knowledge distillation to mitigate error in full-band reconstructed spectra while capturing features related to adulteration. Further, we extend our system to MeatSpec-G to improve its generalizability to varied adulteration conditions and unknown adulterants. To achieve this, we first propose a feature alignment-based training scheme to reduce the feature gap among samples of diverse concentrations and admixture patterns. Then, we propose a cascaded open-set recognition framework that decouples uncertainty quantification and anomaly feature discrimination, to address the limitations of softmax confidence in detecting distribution shifts and reconstruction artifacts. Experimental evaluations on 347 paired spectral images demonstrate that our system achieves a 91.06% accuracy in detecting multiple adulteration types, merely 7.78% inferior to the expensive professional solution, yet 21.58% superior to the baseline at the same price point. Moreover, our system can generalize to achieve an 88.89% detection accuracy in unknown adulteration conditions with a 27.78% improvement, and an 83.33% detection accuracy for unknown adulterants.