|Title||Visual modeling of laser-induced dough browning|
|Author(s)||Chen, Peter Yichen; Blutinger, Jonathan David; Meijers, Yorán; Zheng, Changxi; Grinspun, Eitan; Lipson, Hod|
|Source||Journal of Food Engineering 243 (2019). - ISSN 0260-8774 - p. 9 - 21.|
|Department(s)||Food Process Engineering|
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||Browning - Deconvolution - Deep learning - Dough - Generative model - Infrared laser|
A data-driven model that predicatively generates photorealistic RGB images of dough surface browning is proposed. This model was validated in a practical application using a CO2 laser dough browning pipeline, thus confirming that it can be employed to characterize visual appearance of browned samples, such as surface color and patterns. A supervised deep generative network takes laser speed, laser energy flux, and dough moisture as an input and outputs an image (of 64×64 pixel size) of laser-browned dough. Image generation is achieved by nonlinearly interpolating high-dimensional training data. The proposed prediction framework contributes to the development of computer-aided design (CAD) software for food processing techniques by creating more accurate photorealistic models.