Computational Modeling of Food Oral Breakdown Using Smoothed Particle Hydrodynamics. Issue 2 (5th March 2014) - Harrison, Simon M.; Eyres, Graham; Cleary, Paul W.; Sinnott, Matthew D.; Delahunty, Conor; Lundin, Leif
Abstract The manner in which food breaks down during mastication directly influences not only perception of texture but also the temporal release of taste and aroma compounds and perception of flavor. Although sampling chewed food during mastication allows local quantitative measurement of particle properties and taste and aroma compound concentrations, it is not possible to measure the full spatiotemporal variations of these quantities. Computational modeling can be used to estimate the values of these quantities throughout the volume of the oral cavity at any point in the chewing cycle or allow controlled parametric analysis that would be impractical in physical experimentation. We present a coupled biomechanical‐smoothed particle hydrodynamics model of human mastication and predict the mechanical behavior and breakdown of two agar model foods. The results of these simulations show that our model has the potential to enhance the understanding of the relationship between food structure and oral breakdown during mastication. Practical Applications Development of a computational model of chewing and oral breakdown will enhance the understanding of the relationship between food structure, oral breakdown and flavor release during mastication. A functional model may be utilized to design the material and structural properties of food products that will break down in a desired fashion during chewing. Future model extensions that facilitate the prediction of flavor release given known mechanical properties will facilitate how to add flavor to provide a target sensory profile. This knowledge will fast‐track innovations in new food product development and facilitate reformulation of healthier food products without compromising sensory quality. In the future, the ability to predict the physical and chemical stimuli may facilitate the prediction of sensory perception from first principles.