Methods of Crumb Structure Analysis in Bread — Part 2

Three-Dimensional Imaging Techniques

Bread crumb’s true structure is three-dimensional – a complex network of voids in a continuous solid matrix. In recent decades, 3D imaging technologies have enabled researchers to visualize and quantify this structure in situ, overcoming the limitations of 2D slices. Those are available at the lab scale only, but curiosity keeps pushing the boundaries.

X-ray Microtomography (Micro-CT)

X-ray micro-computed tomography is a powerful non-destructive technique that generates high-resolution 3D images of a sample’s internal architecture. In the context of bread, micro-CT can scan a small piece of crumb and reconstruct the entire porous network in 3D, with voxel resolutions on the order of a few microns to tens of microns. Falcone et al. (2004) first demonstrated the application of phase-contrast micro-CT to bread, allowing clear visualization of alveolar structure in different bread types¹. Since then, micro-CT has been widely used to characterize bread crumb morphology quantitatively in three dimensions.² The technique maps out each air pocket and the surrounding solid phase, enabling measurement of:

• Total porosity: the volume fraction of the crumb that is air (void volume / total volume).² This is the 3D counterpart of void fraction, but more accurate as it captures all pores throughout the sample.
• Pore size distribution: using stereological analysis, one can compute the distribution of pore volumes or equivalent diameters in 3D.² Often, micro-CT reveals many more small bubbles than are apparent in 2D, as well as the true size of large pores.
• Open vs. closed cells: whether pores are interconnected or isolated. Bread crumb typically forms an interconnected “maze” of pores – in fact, micro-CT studies have shown that a significant portion of the void space in bread is one continuous network of connected cells (open pores), rather than discrete trapped bubbles.² Micro-CT image analysis algorithms can identify which pores are interconnected (open) and which are fully enclosed by crumb (closed). This yields metrics like the percentage of closed pores and an interconnectivity index (the volume of the largest connected pore network relative to total void volume).²
• Cell shape and topology: Micro-CT can assess each pore’s shape (sphericity, aspect ratio) and surface area, as well as the thickness of the solid matrix between pores (often termed wall thickness or strut thickness in 3D).²

The information depth provided by micro-CT far exceeds that of 2D imaging. For instance, 2D methods cannot interpret a pore as “closed” or “open”, whereas micro-CT can reveal that the same pore tunnels into neighboring cells beyond the slice. One micro-CT study noted that traditional crumb analyzers could not easily capture whether a pore structure was one large labyrinth or many discrete holes, whereas 3D imaging clearly showed the labyrinthine connectivity of bread pores.² Quantifying open vs. closed porosity is not just academic – it relates to how crumb texture behaves (closed cells can act like isolated bubble wrap pockets that compress differently than an open foam). Indeed, by analyzing 3D data, researchers have found that crumb hardness increases when a larger fraction of pores are closed and cell walls are thicker and more abundant (higher solid fraction).² For example, micro-CT on whole-wheat breads showed that adding certain functional ingredients increased the proportion of small, closed cells and wall material, resulting in a denser, harder crumb.² These results confirm earlier 2D findings and offer direct evidence of how structural parameters influence texture in three dimensions.

Advantages: Micro-CT provides a comprehensive, true volumetriccharacterization of the crumb. It is non-destructive, requiring minimal sample prep (just fixing the sample in place, sometimes drying or freezing is done to improve X-ray contrast). The 3D data enable rigorous calculation of structural metrics and visualization of the crumb’s internal “architecture” that would otherwise be invisible. It has become a gold-standard for microstructure studies of porous foods because of this detail. Micro-CT is especially powerful for research purposes: for instance, it allows scientists to virtually “slice” the crumb in any orientation, count all the pores, or simulate compression by finite element models using the real structure. The resolution of lab micro-CT (often 5–50 μm for food samples) is sufficient for bread’s gas cells.

Limitations: The downsides are mostly practical – micro-CT scanners are expensive, and scanning plus image reconstruction can be time-consuming. Typical micro-CT scanning of bread might cover a sample on the order of a few cubic centimeters; larger samples would either have lower resolution or require combining multiple scans. There is also a high data-processing burden: segmenting 3D images and extracting quantitative features requires advanced image analysis software and significant computing power. Despite these challenges, micro-CT’s ability to see inside the crumb in 3D has revolutionized our understanding of crumb structure. It has validated and extended the results of simpler 2D methods, confirming for example that 2D image analysis is generally reliable for pore size and density estimation,² but also highlighting phenomena like the extensive connectivity of bread’s internal pores that 2D methods inherently miss.

Magnetic Resonance Imaging (MRI)

Magnetic Resonance Imaging offers another non-destructive avenue for peering into bread and dough structure. MRI is sensitive to hydrogen nuclei (protons) and thus excels at imaging water distributions in foods. In a bread dough or crumb, the contrast in MR images comes from the difference between water-rich regions (the dough/crumb matrix) and water-free regions (air cells). This makes MRI suitable for visualizing gas bubbles within dough in situ during fermentation and baking, as well as the final air cell structure of the crumb. Researchers have exploited MRI to dynamically monitor dough expansion: for example, Bajd and Serša used magnetic resonance microscopy to continuously track bubble growth and movement in fermenting dough and even during baking, without disturbing the sample.³ Their high-field MRI could capture 3D images showing how the dough’s pore structure evolved over time and how bubbles coalesced or remained stable. Similarly, De Guio et al. (2009) demonstrated an MRI method based on magnetic susceptibility effects to estimate bubble sizes in dough during proofing – essentially using the signal voids (from air) to quantify the evolving pore size distribution noninvasively. These studies indicate that MRI has proven its potential in monitoring gas cell development in bread dough and other dynamic processes like proving and oven spring.⁴

For the baked product and its crumb analysis, MRI can also be used to visualize the porous structure post-baking, though the resolution is typically lower than X-ray micro-CT. One particular advantage of MRI is that it can also measure local moisture content and mobility, so it has been used to study phenomena like crumb setting and staling (e.g. water migration out of crumb over time). MRI of fresh bread might show a uniform sponge-like porous network, whereas MRI of staled bread could reveal zones of hardened crumb with different proton relaxation properties, indirectly reflecting structural changes as starch recrystallizes.

Advantages: MRI is non-destructive and does not involve ionizing radiation, so live fermentation or even in vivo studies (inside an MRI oven or chamber) are possible. It can provide 3D data and can be tuned to measure not only structure but also chemical information (through spectroscopy or relaxation times) about the crumb. It’s uniquely suited for real-time observation – unlike X-rays, MRI can take repeated scans over time without harming the dough, enabling a “movie” of crumb formation. This dynamic imaging has yielded insights into bubble growth kinetics and dough stability that complement the static snapshots from other methods.

Limitations: The primary limitation is spatial resolution – standard MRI might have resolution on the order of 50–100 microns at best for a small sample, which is lower than micro-CT. Very small pores might not be resolved clearly. High-field systems (7 Tesla or above) are often needed for fine detail, which are expensive and not widely available outside specialized labs. Scan times can be long, especially for high-resolution 3D imaging, though faster imaging sequences are continually being developed. Another practical issue is that performing MRI during actual baking is tricky – custom MRI-compatible ovens or rapid transfer of dough to a pre-scanned hot chamber are required. As with micro-CT, the data processing can be complex, especially if quantitative extraction of bubble size or volume is needed (sometimes image analysis of MR images is less straightforward due to lower contrast edges between gas and solid).