000168644 001__ 168644
000168644 005__ 20260212205631.0
000168644 0247_ $$2doi$$a10.1016/j.crfs.2026.101327
000168644 0248_ $$2sideral$$a148056
000168644 037__ $$aART-2026-148056
000168644 041__ $$aeng
000168644 100__ $$aHernández-Alhambra, E.$$uUniversidad de Zaragoza
000168644 245__ $$aFEM modeling of hamburger pan cooking: Fat content influence and neural network-based prediction of fat loss
000168644 260__ $$c2026
000168644 5060_ $$aAccess copy available to the general public$$fUnrestricted
000168644 5203_ $$aIn this study, we developed an improved contact-cooking model that incorporates variations in fat content and its retention capacity, aiming to accurately simulate products with different compositions. The proposed approach incorporates the structural heterogeneity of meat by distinguishing between muscle fibers and interstitial fluid, and simulates the transport of water and fat between these regions. The model also accounts for heat transfer and meat deformation, representing the tissue as a hyperelastic material. The computational framework was implemented for the pan-cooking of hamburgers with different fat contents (ranging from 3% to 24%) and periodic flipping during the process. To validate the model, cooking experiments were performed. Although increasing fat content did not significantly affect water loss or core temperature (p>0.05), it strongly influenced other critical aspects of cooking performance and product quality, such as surface temperature, fat loss, total cooking losses, and shrinkage. These changes are relevant because they impact texture and consumer perception. In addition, hamburgers with a higher fat content exhibited lower hardness, cohesiveness, gumminess, and chewiness. The model successfully predicted these trends, demonstrating its potential to capture fat-related effects beyond thermal behavior and enabling the use of model-generated data for the training, validation, and testing of a simple neural network to predict fat loss during the cooking of hamburgers with varying water, fat, and protein contents.
000168644 536__ $$9info:eu-repo/grantAgreement/EUR/AEI/CPP2021-008938$$9info:eu-repo/grantAgreement/ES/DGA/PROY_T03_24 IACOOK$$9info:eu-repo/grantAgreement/ES/DGA/T07-23R$$9info:eu-repo/grantAgreement/ES/DGA/T24-23R
000168644 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
000168644 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000168644 700__ $$0(orcid)0009-0000-9592-6655$$aGuiu, P.$$uUniversidad de Zaragoza
000168644 700__ $$0(orcid)0000-0001-5765-2972$$aFerrer-Mairal, A.$$uUniversidad de Zaragoza
000168644 700__ $$0(orcid)0000-0002-8375-0354$$aMartínez, M.A.$$uUniversidad de Zaragoza
000168644 700__ $$0(orcid)0000-0001-9713-1813$$aCalvo, B.$$uUniversidad de Zaragoza
000168644 700__ $$0(orcid)0000-0002-6870-0594$$aGrasa, J.$$uUniversidad de Zaragoza
000168644 700__ $$0(orcid)0000-0001-6013-3399$$aSalvador, M.L.$$uUniversidad de Zaragoza
000168644 7102_ $$15005$$2555$$aUniversidad de Zaragoza$$bDpto. Ing.Quím.Tecnol.Med.Amb.$$cÁrea Ingeniería Química
000168644 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000168644 7102_ $$12008$$2780$$aUniversidad de Zaragoza$$bDpto. Produc.Animal Cienc.Ali.$$cÁrea Tecnología de Alimentos
000168644 773__ $$g12 (2026), 101327 [17 pp.]$$tCurrent Research in Food Science$$x2665-9271
000168644 8564_ $$s6829813$$uhttps://zaguan.unizar.es/record/168644/files/texto_completo.pdf$$yVersión publicada
000168644 8564_ $$s2749562$$uhttps://zaguan.unizar.es/record/168644/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000168644 909CO $$ooai:zaguan.unizar.es:168644$$particulos$$pdriver
000168644 951__ $$a2026-02-12-20:38:38
000168644 980__ $$aARTICLE