Measuring the effects of in vitro mastication on bolus granulometry of shredded meat: A proposal for a new methodological procedure. (February 2019) - Duconseille, Anne; François, Olivier; Bruno, Pereira; Céline, Lambert; Marie-Agnès, Peyron; Martine, Hennequin
Abstract: People with poor oral status and/or neuromuscular deficiencies avoid eating meat because of chewing and swallowing difficulties. Consequently, they expose themselves to the risk of malnutrition and sarcopenia or nutrient deficiencies. To develop a new process for meat preparation, it is necessary to understand how meat is broken down during food oral processing to become a swallowable bolus. However, it is unknown whether the initial texture of meat impacts the meat bolus granulometry. As meat particles are fibrous, making the granulometry analysis with sieving methods is difficult. As the approach of meat bolus granulometry is necessary, it is of importance to control the methodological parameters that affect the measures. This study describes a procedure that allows granulometry analysis of shredded meat before and after in vitro mastication. Meat particles were dispersed in glycerol before image analysis. Granulometry curves were then drawn using generalized linear models, particle by particle, or after applying virtual sieves. Artificial meat bolus granulometry was also analyzed by manual sieving and the different methods were compared through several characteristics, including D50 . Image analysis in glycerol preserves meat particles and allows particle distribution characterization and shape descriptors analysis. This method is well adapted to shredded meat and could be performed to evaluate the mastication of meat preparations adapted for deficient mastication. Graphical abstract: Unlabelled Image Highlights: It is necessary to understand how mastication reduces meat into swallowable boluses Meat bolus granulometry analysis allows the understanding of meat bolus formation Image analysis in glycerol preserves the shape of meat fibrous particles The use of generalized linear models allows particles distribution characterization