Foodstuff authentication from spectral data: toward a species-independent discrimination between fresh and frozen-thawed fish samples

The substitution of fresh fish with frozen–thawed fish is a typical fraud that can damage consumers for several reasons. In fact, not only the quality of thawed meat can be negatively affected during freezing, but also safety issues can arise, as thawed meat is more susceptible to microbial growth. Though several strategies have been proposed for fresh fish authentication, their classification ability is strongly affected by the fish species being considered. In this paper, we propose three different strategies based on latent variable modeling techniques in order to develop a multi-species classifier of the fresh/frozen–thawed status of fish samples using near-infrared spectra. Whereas the first two strategies model the information related to the species and to the fish together (either jointly or sequentially), the third strategy aims at explicitly separating them to improve the classification performance. The proposed strategies were validated over a database of more than 1200 samples of several different species, with near-infrared spectra collected with two different instruments. The overall classification accuracies ranged between 80% and 91%, according to the strategy and the instrument used. We believe that this study can contribute to the development of a species-independent approach to foodstuff classification.