In Vitro Robotic Chewing Studies of Food Texture Changes During Mastication

Abstract

The aim of this study was to investigate food texture changes during mastication using a robotic chewing system. Roasted peanuts and white bread were used as representative food samples to explore how different chewing behaviors affect textural transformations. Initially, the number of cycles required to chew each food sample for swallowing was recorded through in vivo experiments. Subsequently, various molar chewing trajectories, occlusal forces, and artificial saliva flow rates were applied in the robotic chewing system to simulate a range of chewing behaviors. Each food type was chewed by the robot for 0%, 25%, 50%, 75%, and 100% of the total determined number of chewing cycles. Nine food texture variables were measured using texture profile analysis (TPA). Principal component analysis (PCA) and partial least squares regression (PLSR) were employed to assess the correlations between chewing behaviors and food texture changes. Results showed that for roasted peanuts, hardness, adhesive force, and cohesiveness had strong correlations with chewing cycles, while for white bread, these relationships were less pronounced. The mechanisms underlying the texture changes were analyzed and explained. For roasted peanuts, texture changes were primarily governed by chewing stages, with an average hardness reduction of 81.5% over the chewing process. Springiness and its index were mainly influenced by saliva secretion rate. Conversely, white bread initially exhibited increased hardness due to compression, followed by gradual softening. Its adhesive force was chiefly impacted by saliva secretion, while cohesiveness was more strongly affected by chewing trajectory. These findings should be interpreted cautiously, as the limited and homogeneous participant sample restricts their broader applicability.