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 Factor impacto JCR: 5.3 (2023) Categ. JCR: FOOD SCIENCE & TECHNOLOGY rank: 28 / 173 = 0.162 (2023) - Q1 - T1 Categ. JCR: ENGINEERING, CHEMICAL rank: 33 / 170 = 0.194 (2023) - Q1 - T1 Factor impacto CITESCORE: 11.8 - Food Science (Q1)