Influence of sample size and spatial resolution on quantifying Mozzarella cheese microstructural properties: An X-ray tomography case study

Microscopic imaging techniques (2D and 3D) are widely employed to investigate structured food systems. However, the practical limitations of these imaging methods often restrict analyses to a small number of samples, which may be acquired under loosely defined imaging conditions and parameters. Assessing the representativeness of these measurements is further complicated by the fundamental properties of food matrices, such as their inherent heterogeneity, disordered nature, and microstructural complexity. To address this in a practical setting, a comprehensive dataset of high-resolution synchrotron X-ray tomography scans of Mozzarella cheese is analyzed. Representative Elementary Volume (REV) analysis is applied to key structural descriptors — such as anisotropy, width, and orientation — to determine the volume and resolution thresholds required for reliable local characterization. Additionally, macroscale heterogeneity is quantified by evaluating descriptor variability in samples from the same Mozzarella cheese formulation, followed by comparison to inter-cheese distances in descriptor space. These findings offer methodological guidance for designing reliable imaging protocols not only for Mozzarella but also for other structurally similar food matrices, supporting broader adoption of image-based structural measurements in both research and industrial applications.