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Title: Comparison 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)
Authors: Ángela Melado-Herreros; Sonia Nieto-Ortega; Idoia Olabarrieta; Graciela Ramilo-Fernández; Carmen G. Sotelo; Bárbara Teixeira; Amaya Velasco; Rogério Mendes
Citation: Journal of Food Engineering, 2022, 322, 110979
Abstract: Bioelectrical 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).
Keywords: Traceability, Chemometrics, Fish chain, Added water, Smart sensors
Issue Date: 2022
DOI: https://doi.org/10.1016/j.jfoodeng.2022.110979
URI: http://dspace.azti.es/handle/24689/1313
ISSN: 0260-8774
Appears in Publication types:Artículos científicos



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