Estimation of the Moisture Content of In Vitro Chewed Food Boluses by Image Processing

This paper presents a study on the evaluation of moisture content in in vitro chewed food boluses using image processing techniques. This integration offers a novel, non-destructive method that enhances the accuracy and efficiency of moisture assessments compared to traditional gravimetric methods and external sensors, which are typically more time-consuming and potentially alter the sample's properties. Invitro food samples were taken for moisture analysis from a biomimetic robot equipped with automatic saliva injections. Image skewness, variance, lightness, and Gray-Level Co-occurrence Matrix (GLCM) analysis were found to quantitatively indicate moisture content for two types of in vitro chewed food samples, at different chewing cycles and trajectories. This pilot study was conducted with two food samples, but the approach can be applied to a broader range. The study's findings highlight the effectiveness of image processing in analyzing moisture dynamics within chewed food boluses, providing valuable insights that could enhance the design and functionality of biomimetic chewing robots to resemble human oral processing more closely.