168644 20260212205631.0 doi 10.1016/j.crfs.2026.101327 sideral 148056 ART-2026-148056 eng Hernández-Alhambra, E. Universidad de Zaragoza FEM modeling of hamburger pan cooking: Fat content influence and neural network-based prediction of fat loss 2026 In this study, we developed an improved contact-cooking model that incorporates variations in fat content and its retention capacity, aiming to accurately simulate products with different compositions. The proposed approach incorporates the structural heterogeneity of meat by distinguishing between muscle fibers and interstitial fluid, and simulates the transport of water and fat between these regions. The model also accounts for heat transfer and meat deformation, representing the tissue as a hyperelastic material. The computational framework was implemented for the pan-cooking of hamburgers with different fat contents (ranging from 3% to 24%) and periodic flipping during the process. To validate the model, cooking experiments were performed. Although increasing fat content did not significantly affect water loss or core temperature (p>0.05), it strongly influenced other critical aspects of cooking performance and product quality, such as surface temperature, fat loss, total cooking losses, and shrinkage. These changes are relevant because they impact texture and consumer perception. In addition, hamburgers with a higher fat content exhibited lower hardness, cohesiveness, gumminess, and chewiness. The model successfully predicted these trends, demonstrating its potential to capture fat-related effects beyond thermal behavior and enabling the use of model-generated data for the training, validation, and testing of a simple neural network to predict fat loss during the cooking of hamburgers with varying water, fat, and protein contents. Access copy available to the general public Unrestricted info:eu-repo/grantAgreement/EUR/AEI/CPP2021-008938 info:eu-repo/grantAgreement/ES/DGA/PROY_T03_24 IACOOK info:eu-repo/grantAgreement/ES/DGA/T07-23R info:eu-repo/grantAgreement/ES/DGA/T24-23R info:eu-repo/semantics/openAccess by-nc-nd https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Guiu, P. Universidad de Zaragoza (orcid)0009-0000-9592-6655 Ferrer-Mairal, A. Universidad de Zaragoza (orcid)0000-0001-5765-2972 Martínez, M.A. Universidad de Zaragoza (orcid)0000-0002-8375-0354 Calvo, B. Universidad de Zaragoza (orcid)0000-0001-9713-1813 Grasa, J. Universidad de Zaragoza (orcid)0000-0002-6870-0594 Salvador, M.L. Universidad de Zaragoza (orcid)0000-0001-6013-3399 5005 555 Universidad de Zaragoza Dpto. Ing.Quím.Tecnol.Med.Amb. Área Ingeniería Química 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 2008 780 Universidad de Zaragoza Dpto. Produc.Animal Cienc.Ali. Área Tecnología de Alimentos 12 (2026), 101327 [17 pp.] Current Research in Food Science 2665-9271 6829813 http://zaguan.unizar.es/record/168644/files/texto_completo.pdf Versión publicada 2749562 http://zaguan.unizar.es/record/168644/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:168644 articulos driver 2026-02-12-20:38:38 ARTICLE