Speaker
Description
The characterization of mechanical properties is essential not only in traditional engineering applications but also in food science, where it serves as a powerful tool for texture profile analysis (TPA) of food. Standardized TPA is commonly used to extract properties such as hardness and springiness [1]. To modify food texture, additional ingredients, such as thickeners, are often introduced to alter peak forces during chewing. Often, these ingredients are successively added, which is not only time-consuming but also tedious. Studying food from an engineering perspective offers new ways to improve the properties. Mechanically, food can be treated as a visco-elastic solid, allowing its mechanical properties – such as relaxation behavior and stiffness – to be systematically analyzed. Instead of introducing artificial additives to achieve a desired texture, an alternative might be evaluating the inherent properties of existing ingredients through material modeling. Therefore, we aim to identify the material model that best describes the mechanical behavior of the examined food. Rather than presupposing a specific behavior, such as that described by the elastic Arruda-Boyce or inelastic Drucker-Prager models, we leverage the recent advancements in constitutive modeling using neural networks. By employing interpretable and physically consistent models, we aim to determine the most suitable material description in an unbiased manner. Using compression test data from tofu conducted from recent experiments, we investigate the presence of inelasticity through inelastic constitutive artificial neural networks (iCANNs) [2].
[1] Rahman, M. S., Al-Attabi, Z. H., Al-Habsi, N., \& Al-Khusaibi, M. (2021). Measurement of instrumental texture profile analysis (TPA) of foods. Techniques to Measure Food Safety and Quality: Microbial, Chemical, and Sensory, 427-465.
[2] Holthusen, H., Linka, K., Kuhl, E., \& Brepols, T. (2025). A generalized dual potential for inelastic Constitutive Artificial Neural Networks: A JAX implementation at finite strains. arXiv preprint arXiv:2502.17490.
