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<p>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 macrostructural analysis revealed critical cracks, including macro-cracks (&gt; 0.3&#xa0;mm³). These internal structural defects were consistent with stresses driven by physicochemical mismatches, specifically moisture migration from the pineapple jam and fat migration from the peanut butter. These findings validate the developed X-ray CT workflow as a powerful tool for identifying internal multi-phase food systems, such as praline chocolate.</p>

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

  • 期刊论文

  • 发表日期：

    2026年5月8日

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  • Bayu Nugraha,
  • Yoga Arif Firmansyah,
  • Joko Nugroho Wahyu Kariyadi,
  • Fahrizal Yusuf Affandi,
  • Arifin Dwi Saputro

摘要

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 macrostructural analysis revealed critical cracks, including macro-cracks (> 0.3 mm³). These internal structural defects were consistent with stresses driven by physicochemical mismatches, specifically moisture migration from the pineapple jam and fat migration from the peanut butter. These findings validate the developed X-ray CT workflow as a powerful tool for identifying internal multi-phase food systems, such as praline chocolate.