Quantitative image analysis of protein foam microstructure and its correlation with rheological properties: Egg white foam

Recent advances in software development and machine learning algorithms are revolutionizing the way microstructures analyzed quantified fields like biology neuroscience. With this, comes an upsurge opportunities to apply these tools study food microstructure gain a deeper understanding of structure-function relationships. This article shows how recently developed deep-learning cellular segmentation algorithm, ‘Cellpose’ can be used identify foam for further quantification. It successfully identified air bubbles protein matrix, from microscopic images captured on simple brightfield microscope. The algorithm allowed quantification microstructural parameters phase (bubbles) liquid (lamella) foams. Egg white foams were made with basic ingredients meringue effect sugar concentration acidic conditions studied. Microstructural relation rheological responses underlying mechanisms governing changes foams' stiffness linearity viscoelastic response shown. Sugar bubble size distribution by thickening egg white, it also shortened linear deformation due its decreasing lamella thickness. Acidity re-shaped into more ‘hexagon-like’ structures allowing incorporated shape under makes extend their deformation. achieved data algorithm-identified presents new characterize