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Article

Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with Non-Destructive Methods and Classification Models: Bioelectrical Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time Domain Reflectometry (TDR)

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AZTI, Food Research, Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Bizkaia, Astondo Bidea, Edificio 609, 48160 Derio, Spain
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Instituto de Investigaciones Marinas, CSIC, Eduardo Cabello, 6, 36208 Vigo, Spain
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Portuguese Institute for the Sea and Atmosphere, IPMA, R. Alfredo Magalhães Ramalho, 6, 1449-006 Lisbon, Portugal
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Interdisciplinary Center of Marine and Environmental Research (CIIMAR), University of Porto, Rua das Bragas 289, 4050-123 Porto, Portugal
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Author to whom correspondence should be addressed.
Academic Editors: Michael Ngadi, Laura Liu and Akinbode A. Adedeji
Foods 2022, 11(1), 55; https://doi.org/10.3390/foods11010055
Received: 25 November 2021 / Revised: 22 December 2021 / Accepted: 23 December 2021 / Published: 27 December 2021
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. 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. View Full-Text
Keywords: chemometrics; water injection; fishery products; authenticity; sensors; defrosted; freezing; quality control; consumer trust; labelling chemometrics; water injection; fishery products; authenticity; sensors; defrosted; freezing; quality control; consumer trust; labelling
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MDPI and ACS Style

Nieto-Ortega, S.; Melado-Herreros, Á.; Foti, G.; Olabarrieta, I.; Ramilo-Fernández, G.; Gonzalez Sotelo, C.; Teixeira, B.; Velasco, A.; Mendes, R. Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with Non-Destructive Methods and Classification Models: Bioelectrical Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time Domain Reflectometry (TDR). Foods 2022, 11, 55. https://doi.org/10.3390/foods11010055

AMA Style

Nieto-Ortega S, Melado-Herreros Á, Foti G, Olabarrieta I, Ramilo-Fernández G, Gonzalez Sotelo C, Teixeira B, Velasco A, Mendes R. Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with Non-Destructive Methods and Classification Models: Bioelectrical Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time Domain Reflectometry (TDR). Foods. 2022; 11(1):55. https://doi.org/10.3390/foods11010055

Chicago/Turabian Style

Nieto-Ortega, Sonia, Ángela Melado-Herreros, Giuseppe Foti, Idoia Olabarrieta, Graciela Ramilo-Fernández, Carmen Gonzalez Sotelo, Bárbara Teixeira, Amaya Velasco, and Rogério Mendes. 2022. "Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with Non-Destructive Methods and Classification Models: Bioelectrical Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time Domain Reflectometry (TDR)" Foods 11, no. 1: 55. https://doi.org/10.3390/foods11010055

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