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dc.contributor.authorMelado-Herreros, Angela-
dc.contributor.authorNieto-Ortega, Sonia-
dc.contributor.authorOlabarrieta, Idoia-
dc.contributor.authorGraciela Ramilo-Fernández-
dc.contributor.authorCarmen G. Sotelo-
dc.contributor.authorBárbara Teixeira-
dc.contributor.authorAmaya Velasco-
dc.contributor.authorRogério Mendes-
dc.date.accessioned2022-03-28T10:05:31Z-
dc.date.available2022-03-28T10:05:31Z-
dc.date.issued2022-
dc.identifier.citationJournal of Food Engineering, 2022, 322, 110979-
dc.identifier.issn0260-8774-
dc.identifier.urihttp://dspace.azti.es/handle/24689/1313-
dc.description.abstractBioelectrical impedance analysis (BIA), near-infrared (NIR) spectroscopy and time domain reflectometry (TDR) were compared as non-destructive techniques, coupled with a classifier based on partial least square discriminant analysis (PLS-DA), to assess added water detection in a seafood model: tuna. Three classification models were developed for each technology in unfrozen, thawed and in a combination of both stages to distinguish between added and non-added water samples. Results were acceptable for the unfrozen stage with all the technologies, giving TDR the best performance (accuracy = 0.95; error rate = 0.06). However, results on the model for thawed stage were not satisfactory, due to the behavior of water during the freezing-thawing process in both types of samples (with and without added water). For the combined model, NIR failed in the classification (accuracy = 0.68; error rate = 0.32), BIA gave acceptable results (accuracy = 0.72; error rate = 0.28) and TDR made a good classification (accuracy = 0.87; error rate = 0.12).-
dc.subjectTraceability, Chemometrics, Fish chain, Added water, Smart sensors-
dc.titleComparison of three rapid non-destructive techniques coupled with a classifier to increase transparency in the seafood value chain: Bioelectrical impedance analysis (BIA), near-infrared spectroscopy (NIR) and time domain reflectometry (TDR)-
dc.identifier.journalJournal of Food Engineering-
dc.format.page110979-
dc.format.volume322-
dc.identifier.doihttps://doi.org/10.1016/j.jfoodeng.2022.110979-
Aparece en las tipos de publicación: Artículos científicos



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