Texture Analysis through Soft-Machine Technology Mimicking Human Mastication

Associate Professor, Graduate School of Science and Engineering / Faculty of Engineering

OGAWA Jun

When humans perceive the “texture” of food, multiple sensory inputs are integrated — including hardness and elasticity detected by the teeth and tongue, as well as changes in pressure transmitted through the gums. Such perception of texture is an important parameter not only in food product evaluation but also in the assessment of masticatory function in oral and maxillofacial surgery. To address this, we have developed the Gel Biter, a chewing device incorporating an artificial oral cavity fabricated with a 3D printer using a combination of rigid and soft materials to replicate the hardness and alignment of teeth and the softness of gums and tongue. Embedded pressure sensors allow the device to determine, with high accuracy, both what is being chewed and how it is being chewed based on the mechanical signals generated during mastication. Interestingly, like humans, the device can misinterpret texture depending on chewing speed, suggesting the potential to reproduce human-like texture perception. We are currently working to enhance the device’s customizability and performance, with the goal of extending its applications to food evaluation, oral frailty prevention, and broader uses in medical and welfare fields.

▲Texture Analysis AI “Gel Biter”

▲3D-Printed Oral Model and the Biting Data Obtained

▲Study Case on Texture Perception Using the Gel Biter