Using recurrent neural networks to identify broken-cold-chain fish fillet from spectral profiles
Author:
Funder
Universidad Nacional de Frontera
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-023-09311-4.pdf
Reference43 articles.
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2. Chen S, Zhang F, Ning J, Liu X, Zhang Z, Yang S (2015) Predicting the anthocyanin content of wine grapes by NIR hyperspectral imaging. Food Chem 172:788–793. https://doi.org/10.1016/j.foodchem.2014.09.119
3. Anderssen K, Stormo S, Skåra T, Skjelvareid M, Heia K (2020) Predicting liquid loss of frozen and thawed cod from hyperspectral imaging. LWT 133:110093. https://doi.org/10.1016/j.lwt.2020.110093
4. Cheng J-H, Sun D-W, Pu H-B, Chen X, Liu Y, Zhang H, Li J-L (2015) Integration of classifiers analysis and hyperspectral imaging for rapid discrimination of fresh from cold-stored and frozen-thawed fish fillets. J Food Eng 161:33–39. https://doi.org/10.1016/j.jfoodeng.2015.03.011
5. Xie A, Sun D-W, Zhu Z, Pu H (2016) Nondestructive measurements of freezing parameters of frozen porcine meat by NIR hyperspectral imaging. Food Bioprocess Technol 9(9):1444–1454. https://doi.org/10.1007/s11947-016-1766-2
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