Morphometric Characterization Workflows of Praline Chocolates using X-ray Computed Tomography

The structural integrity of praline chocolates is a determinant factor for consumer acceptance, yet assessing it remains challenging due to the complex internal interactions between chocolate shells and fillings. This study establishes a robust non-destructive characterization protocol using X-ray Computed Tomography (CT) to evaluate the morphometrics of dark, milk, and white chocolates filled with water-based pineapple jam and fat-based peanut butter. A critical challenge addressed was the low radiodensity contrast between the chocolate matrix and fillings. To resolve this, the research compared global threshold, Volume of Interest (VOI)-based, and a Seeded Region-Growing Algorithm (Grow from Seeds/GFS) segmentations. Results indicated a strong relationship (R²=0.9255) between CT greyscale intensity and physical densities of the praline components. The GFS method demonstrated higher accuracy on low-contrast images of the praline than Otsu and VOI-based segmentation method. This method successfully reconstructed the internal architecture and matched the actual filling mass fraction (~ 15%) with high precision. Furthermore, 3D microstructural analysis revealed that physicochemical mismatches-specifically moisture migration in pineapple jam and fat migration in peanut butter-induced critical defects, including macro-voids (> 0.3 mm³). These findings validate the developed X-ray CT workflow as a powerful tool for identifying internal multi-phase food systems, such as praline chocolate.