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. • Representative Elementary Volume (REV) analysis provides crucial information for designing and optimizing image-based measurements of structured food systems. • Sub-millimeter samples of mozzarella can reliably capture its local microstructural characteristics. • Mozzarella exhibits low macroscale heterogeneity, enabling meaningful structural generalizations across larger volumes. • Local microstructural measurements are sensitive to formulation-induced changes, supporting their use in the analysis and large-scale monitoring of mozzarella, as well as similar structured foods.
Authors
- Anders Nymark Christensen (ORCID: https://orcid.org/0000-0002-3668-3128)
- Ulf Andersen (ORCID: https://orcid.org/0000-0002-4345-4403)
- Frans van den Berg (ORCID: https://orcid.org/0000-0002-9170-9052)
- Peter Winkel Rasmussen (ORCID: https://orcid.org/0000-0002-0823-0316)
- Jeppe Revall Frisvad (ORCID: https://orcid.org/0000-0002-0603-3669)
- Anders Bjorholm Dahl (ORCID: https://orcid.org/0000-0002-0068-8170)
- Søren Balling Engelsen (ORCID: https://orcid.org/0000-0003-4124-4338)
- H. S. Patel
- Joana Ortega-Anaya
- Pawel Tomasz Pieta (ORCID: https://orcid.org/0009-0005-7634-6627)
Institutions
- University of Copenhagen (DK)
- Agro Business Park (DK)
- Arla Foods (Denmark) (DK)
- Technical University of Denmark (DK)
Publication Details
- Journal
- Journal of Food Engineering
- Published
- 2025-10-21
- DOI
- https://doi.org/10.1016/j.jfoodeng.2025.112843
- Primary Topic
- Probiotics and Fermented Foods
- Type
- article
- Field-Weighted Citation Impact
- 0.00