Data Fusion for Food Authentication: Fresh/Frozen–Thawed Discrimination in West African Goatfish (Pseudupeneus prayensis) Fillets

The substitution of fresh fish with frozen–thawed fish is a typical fraud that not only damages consumers from an economical point of view, but also causes safety issues. Furthermore, fish authentication is important for correct product labeling, as promoted by recent regulatory actions. In this paper, we present the results of the authentication of fresh west African goatfish (Pseudupeneus prayensis) fillets using different analytical technologies, namely a portable visible/nearinfrared spectrometer, a compact digital camera, and a texture analyzer. First, the classification performance of the abovementioned analytical technologies is evaluated and compared. Then, it is shown how the fusion of different technologies can be effectively used to improve the classification accuracy. Particularly, spectra and color features extracted from digital images returned a classification accuracy of 100 and 98.5 %, respectively, when considered separately. However, the classification accuracy fell to 80 % when considering measurements taken with a 24-h delay. Data fusion, instead, allowed a classification accuracy of 100 % also after 24 h. Hence, the combination of a spectrometer and a digital camera is very promising for cost-effective on-line/atline applications, as both sensors are rapid, non-invasive, and do not require sample preparation. Furthermore, since more than 200 samples were collected over a prolonged period of time (1 year), the classification models encompassed some sources of variability (seasonality effects and size) that are not usually accounted for in similar studies.