Food Cooking Process Modeling With Neural Networks

Abstract

Food cooking process are complex dynamical systems to model. In the state of the art we find that a good solution consists of physics-based finite element models (FEM). FEM models, although being very accurate, have a high computational cost making them unfeasible for real-time applications. To solve this problem, we consider neural networks (NN) trained from FEM simulations. Specifically, we propose a Nonlinear AutoRegressive with eXogenous inputs Neural Network (NARX-NN). The main novelty is that we define a novel training algorithm adapted to the modeling of real-time dynamical systems, allowing a NARX-NN with a simple structure to obtain a negligible error compared to the results of the original FEM model. The NARX-NN trained with the proposed training algorithm obtains an R-squared of more than 0.99 in the test simulations, while the same NARX-NN trained with the standard training algorithm obtains an R-squared of 0.78 in the same tests. The proposed NARX-NN achieves a speedup of 8 orders of magnitude compared to the original FEM model. Moreover, the developed NN is able to predict the complete cooking of the food in a few milliseconds without the need of external sensors. Alternatively, our approach can also be used in real time with information captured with sensors. The presented methodology is highly scalable and could be adapted to different types of food and cooking processes, as well as to other dynamical systems in general.

Keywords

• Food modeling
• neural network
• real-time
• sensors
• simulation
• training algorithm