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RESEARCH PRODUCT

Analysis of oral breakdown of food gels using image analysis

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Characterizing the dynamics of food oral breakdown is of interest to understand the temporal perception of food products. Several studies aimed at characterizing bolus properties through the determination of particle size distribution, saliva uptake and rheological properties. For soft and heterogeneous foods, it is difficult to have a description of the evolution of food breakdown in the mouth, because the concept of individual particles is not clear and the bolus texture is not always homogeneous. For this reason, it is convenient to examine the possible utilization of other analytical techniques. 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: grey-level histograms, histogram of shape area, mathematical morphology and grey 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 providing 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.