Quantification of food quality is a critical process for ensuring public health. Fish correspond to a particularly challenging case due to its high perishable nature as food. Existing approaches require laboratory testing, a laborious and timeconsuming process. In this paper, we propose a novel approach for evaluating fish freshness by exploiting the information encoded in the spectral profile acquired by a snapshot spectral camera. To extract the relevant information, we employ state-ofthe- art Convolutional Neural Networks and treat the problem as an instance of multi-class classification, where each class corresponds to a two-day period since harvesting. Experimental evaluation on individuals from the Sparidae (Boops sp.) family demonstrates that the proposed approach constitutes a valid methodology, offering both accuracy as well as effortless application.