Investigation of oral gels breakdown using image analysis

Abstract

Characterizing the dynamics of food oral breakdown is of interest to understand the temporal perception of food products. The present work aimed at studying the possible contribution of artificial vision for studying bolus formation. Four emulsion-filled gels were prepared from two concentrations of agar and gelatin. By combining two different layers of these gels, four samples of homogeneous composition and 6 samples of heterogeneous composition were prepared. The layers were colored independently in order to study their breakdown and mixing during oral processing. Images of spat out boluses were collected at different stages of the chewing process and studied by different image analysis methods: gray-level histograms, histogram of shape area, mathematical morphology and gray level co-occurrence matrix. Methods were compared for their ability in discriminating boluses as function of homogeneous gel composition and mastication time. Three methods were found to be relevant and mathematical morphology provided the best results. Using this method, we further analyzed the impact of heterogeneous gels composition on the evolution of boluses properties. Results showed that when two gel layers of different composition were combined, the agar layer dominated bolus properties and that the presence of gelatin impacted the dynamics of gel breakdown. The results were in agreement with results obtained previously when characterizing the physical properties of the boluses. This study showed that artificial vision provides reliable tools for evaluating the dynamics of bolus formation, which is complementary to the methods commonly used in literature while avoiding extensive manipulation of boluses.