000128225 001__ 128225
000128225 005__ 20231215091009.0
000128225 0247_ $$2doi$$a10.1016/j.jfoodeng.2023.111697
000128225 0248_ $$2sideral$$a135553
000128225 037__ $$aART-2023-135553
000128225 041__ $$aeng
000128225 100__ $$0(orcid)0000-0001-8219-2365$$aCabeza-Gil, Iulen$$uUniversidad de Zaragoza
000128225 245__ $$aDigital twins for monitoring and predicting the cooking of food products: A case study for a French crêpe
000128225 260__ $$c2023
000128225 5060_ $$aAccess copy available to the general public$$fUnrestricted
000128225 5203_ $$aThe food industry is shifting toward automated and customized processes, leading to the emergence of smart cooking devices that improve cooking outcomes. However, these devices can be invasive, costly, and only applicable to certain foods. To address these issues, a noninvasive digital twin that monitors food during cooking using a common frying pan with a temperature sensor and a weighing scale is proposed. A case study for a French crêpe is presented, in which we developed a digital twin using a neural network trained on over 400,000 simulation data points. The results show that the digital twin can accurately estimate the properties of the crêpe during cooking in real time with a mean absolute percentage error of less than 5% and predict when it will be cooked according to user criteria. The approach offers significant benefits over existing smart cooking devices, as it can be applied to a wide range of cooking processes. The proposed technology enables food process automation and has potential applications in both home and professional kitchens.
000128225 536__ $$9info:eu-repo/grantAgreement/EUR/AEI/CPP2021-008938$$9info:eu-repo/grantAgreement/ES/DGA/T07-23R$$9info:eu-repo/grantAgreement/ES/DGA/T24-23R
000128225 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000128225 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000128225 700__ $$0(orcid)0000-0003-4128-2836$$aRíos-Ruiz, Itziar$$uUniversidad de Zaragoza
000128225 700__ $$0(orcid)0000-0002-8375-0354$$aMartínez, Miguel Ángel$$uUniversidad de Zaragoza
000128225 700__ $$0(orcid)0000-0001-9713-1813$$aCalvo, Begoña$$uUniversidad de Zaragoza
000128225 700__ $$0(orcid)0000-0002-6870-0594$$aGrasa, Jorge$$uUniversidad de Zaragoza
000128225 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000128225 773__ $$g359 (2023), 111697 [12 pp.]$$pJ. food eng.$$tJOURNAL OF FOOD ENGINEERING$$x0260-8774
000128225 8564_ $$s3266555$$uhttps://zaguan.unizar.es/record/128225/files/texto_completo.pdf$$yVersión publicada
000128225 8564_ $$s2790727$$uhttps://zaguan.unizar.es/record/128225/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000128225 909CO $$ooai:zaguan.unizar.es:128225$$particulos$$pdriver
000128225 951__ $$a2023-12-15-09:04:42
000128225 980__ $$aARTICLE