Digital twins for monitoring and predicting the cooking of food products: A case study for a French crêpe

Cabeza-Gil, Iulen (Universidad de Zaragoza) ; Ríos-Ruiz, Itziar (Universidad de Zaragoza) ; Martínez, Miguel Ángel (Universidad de Zaragoza) ; Calvo, Begoña (Universidad de Zaragoza) ; Grasa, Jorge (Universidad de Zaragoza)
Digital twins for monitoring and predicting the cooking of food products: A case study for a French crêpe
Resumen: The 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.
Idioma: Inglés
DOI: 10.1016/j.jfoodeng.2023.111697
Año: 2023
Publicado en: JOURNAL OF FOOD ENGINEERING 359 (2023), 111697 [12 pp.]
ISSN: 0260-8774

Financiación: info:eu-repo/grantAgreement/EUR/AEI/CPP2021-008938
Financiación: info:eu-repo/grantAgreement/ES/DGA/T07-23R
Financiación: info:eu-repo/grantAgreement/ES/DGA/T24-23R
Tipo y forma: Article (Published version)
Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)

Creative Commons You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. If you remix, transform, or build upon the material, you may not distribute the modified material.

Exportado de SIDERAL (2023-12-15-09:04:42)

Este artículo se encuentra en las siguientes colecciones:

 Record created 2023-11-29, last modified 2023-12-15

Versión publicada:
Rate this document:

Rate this document:
(Not yet reviewed)