SEATRACES PUBLICATION
Sonia Nieto-Ortega , Ángela Melado-Herreros, Giuseppe Foti, Idoia Olabarrieta,
Graciela Ramilo-Fernández, Carmen Gonzalez Sotelo, Bárbara Teixeira, Amaya Velasco
and Rogério Mendes
Abstract:
The performances of three non-destructive sensors, based on different principles, bioelectrical impedance analysis (BIA), near-infrared spectroscopy (NIR) and time domain reflectometry (TDR), were studied to discriminate between unfrozen and frozen-thawed fish. Bigeye tuna (Thunnus obesus) was selected as a model to evaluate these technologies. The addition of water and additives is usual in the fish industry, thus, in order to have a wide range of possible commercial conditions, some samples were injected with different water solutions (based on different concentrations of salt, polyphosphates and a protein hydrolysate solution). Three different models, based on partial least squares discriminant analysis (PLS-DA), were developed for each technology. This is a linear classification method that combines the properties of partial least squares (PLS) regression with the classification power of a discriminant technique. The results obtained in the evaluation of the test set were satisfactory for all the sensors, giving NIR the best performance (accuracy = 0.91, error rate = 0.10). Nevertheless, the classification accomplished with BIA and TDR data resulted also satisfactory and almost equally as good, with accuracies of 0.88 and 0.86 and error rates of 0.14 and 0.15, respectively.
![Unfrozen-and-Frozen-Thawed-Tuna-03](https://www.seatraces.eu/wp-content/uploads/2022/01/Unfrozen-and-Frozen-Thawed-Tuna-03.jpg)
This work opens new possibilities to discriminate between unfrozen and frozen-thawed fish samples with different non-destructive alternatives, regardless of whether or not they have added water.
Journal: Foods