A Novel Approach to Evaluate Robotic in Vitro Chewing Effect on Food Bolus Formation Using the GLCM Image Analysis Technique

Abstract

In the context of food science and engineering, the in vitro chewing effect on food bolus formation is a critical area of research that explores the mechanical and textural properties of ingested materials. This article presents a pioneering approach to assess the in vitro chewing impact on food bolus formation using the gray level co-occurrence matrix (GLCM) image analysis technique. As technological advancements lead to the development of mastication robots, the need for evaluating in vitro chewed food bolus has grown. To address this challenge, a case study is conducted. The study's objectives encompass utilizing GLCM to determine the in vitro chewing cycle phase, analyzing texture features, and investigating chewing trajectory differences for beef and plant-based burger patties. Applying GLCM as a methodology, the research quantitatively analyzes textural features of food bolus formations under controlled in vitro chewing conditions. The outcomes reveal distinct differences between beef and plant-based samples through GLCM parameters. Significantly, the study identifies a consistent trend across various scenarios, indicating an increase in energy and homogeneity and a decrease in dissimilarity with an increasing number of in vitro chewing cycles. This investigation offers valuable insights into the dynamic relationship between chewing cycles and textural features in the oral processing of beef and plant-based burger patties.

Keywords

• Food bolus formation
• image processing
• in vitro chewing
• mastication robots
• oral processing
• textural features