The techniques that are currently available to assess fat crystal networks are compromised with respect to invasive sample preparation and ability to quantify compositional and structural features. Raman confocal hyperspectral imaging coupled to analysis with multivariate curve resolution can address these bottlenecks, as it provides label-free, noninvasive chemical information in three dimensions (3D). We demonstrate the ability to acquire compositional maps of dispersions of micronized fat crystals (MFC) in oil, which contain local concentrations of liquid oil and solid fat with submicron spatial resolution and with acquisition times in the order of 10 min. From the compositional maps, we can derive quantitative information on the size and porosity of fat crystal flocs, as well as the solid fat content of the embedding continuous phase. Furthermore, the fractal dimension of the fat crystal network could be determined from the compositional maps via the box-counting method and via the porosities of the crystal flocs. This makes it feasible to assess the validity of the weak-link network theory under industrial relevant conditions. The confocal imaging mode allows for straightforward acquisition of 3D compositional cubes by recording a stack of two-dimensional (2D) images. The box-counting fractal dimension analysis performed on 2D maps can be extended to 3D cubes, which allows for straightforward verification that MFC networks are self-similar rather than self-affine.
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