3D Characterization of Sponge Cake as Affected by Freezing Conditions Using Synchrotron X-ray Microtomography at Negative Temperature
Abstract
:1. Introduction
2. Materials and Methods
2.1. Samples
2.1.1. Sponge Cake Preparation and Sampling
- Native corn starch (Cargill, Minneapolis, MN, USA), stored in glass bottles at 5 °C;
- Methylcellulose (MC) (Dow Chemical, Midland, MI, USA), type SGA7C, stored in a plastic box at room temperature;
- Hydroxypropylmethylcellulose (HPMC) (Dow Chemical, Midland, MI, USA), type K250M, stored in a plastic box at room temperature;
- Ultrapure water.
2.1.2. Sampling and Freezing
2.2. Characterization of Thermo-Physical Properties of the Sponge Cake
2.2.1. Density
2.2.2. Water Content
2.2.3. Differential Scanning Calorimetry Measurements (DSC)
2.3. Cryo-SEM Analysis
2.4. Synchrotron X-ray Micro-Tomography
2.4.1. Thermostated Cell
2.4.2. 3D Image Acquisition
2.4.3. Image Processing
2.4.4. Microstructural Description
- ▪
- The volume of each phase, using a simple voxel counting: , the total volume of the sample is . The volume fraction for each phase is then calculated:
- ▪
- The specific surface area (SSA) is defined by the total surface area of an interface between two phases per the total volume of the sample. It was calculated using MorpholibJ [34] on the one hand, for ice and air interfaces, and, on the other hand, for ice and starch interfaces.
- ▪
- The local thickness of ice inside and outside was computed: it represents the diameter of the largest sphere at a given point that can fit inside the object and containing the given point [35]. This parameter is calculated by the Saito-Toriwaki Euclidean distance transformation algorithm [36]. This algorithm has been implemented as a plugin for Fiji.
- ▪
- The mean curvature: each point of a 3D surface is characterized by two principal curvatures fmin and fmax, which correspond to the maximum and minimum value of the curvature at that point, respectively. The mean curvatures C (m−1) represent a useful descriptor to characterize the surface shapes [37,38].
2.4.5. Statistical Analysis
3. Results
3.1. Thermophysical Properties of the Model Sponge Cake
3.1.1. Model Sponge Cake Reproducibility
3.1.2. Freezing Point and Freezable Water Content
3.2. Microstructural Image Analysis
- Ice formation and location
- Effect of freezing rate
3.3. Quantitative Data Analysis
3.3.1. Representative Sub-Volume Analysis
3.3.2. Volume Fractions
- Porosity
- Ice volume fractions
3.3.3. Specific Surface Area (SSA) and Local Thickness
- ▪
- The migration of half the ice content to the pores’ interface with a thick layer of ice (20–30 µm)
- ▪
- A characteristic size of the ice inside the matrix of the same order (20–25 µm)
3.3.4. Mean Curvature
- For fast freezing, the curve is much steeper and high with negative curvature values representing the pore curvatures (e.g., a curvature of −2 mm−1 corresponds to a pore diameter of 1 mm). The prismatic ice crystals are characterised by rather flat surface. The graph C in Figure 11 shows that the amount of such surface is of the same order for slow and fast freezing
- For slow freezing, the curve is centred at zero and is much wider. This corresponds to a significant proportion of strong curvatures (concave or convex), which means that the surface is rough with several indentations and bumps.
4. Discussion
4.1. Microstructure Characterization
4.2. Effect of Freezing Rate on Ice Formation and Location
5. Conclusions
- For fast freezing, 69% of the ice formed during freezing is formed inside the matrix with a homogeneous distribution of small ice crystals;
- For slow freezing, almost 60% of the ice present in the sample is formed at the air-matrix interface; thick ice layers are visible in the pores and the ice is heterogeneously distributed in size and location inside the starch matrix.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Ingredients | Quantity (% w/w) | Density (kg/m3) |
---|---|---|
K250M (HPMC) | 0.35 | / |
SGA7C (MC) | 0.46 | / |
Water | 62.64 | 1000 |
Starch | 36.55 | 766 |
Batter Theoretical Density (kg/m3) | Batter Measured Density (kg/m3) | Cake Real Density (kg/m3) | Cake Apparent Density (kg/m3) | Porosity (%) | Water Content after Baking (%) |
---|---|---|---|---|---|
899 | 694 ± 2.2 | 645 ± 20 | 392 ± 15 | 56 ± 2 | 60 ± 1.5 |
Freezing Rate | Volume Fraction of Air (%) | Volume Fraction without Air (%) | |||
---|---|---|---|---|---|
Total Ice Fraction | Ice Inside the Matrix | Ice Outside the Matrix | Starch | ||
Unfrozen | 63 ± 6 a | / | / | / | / |
Fast freezing | 62 ± 6 a | 62 ± 3 c | 43 ± 3 d | 19 ± 3 f | 38 ± 2 h |
Slow freezing | 57 ± 6 b | 63 ± 1 c | 25 ± 5 e | 38 ± 5 g | 37 ± 1 h |
Freezing Rate | Interface Air—Ice Outside the Matrix | Interface Ice Inside the Matrix—Starch |
---|---|---|
Fast freezing | 8.4 ± 1.5 a | 52.6 ± 12.9 b |
Slow freezing | 8.0 ± 1.0 a | 31.9 ± 11.4 c |
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Zennoune, A.; Latil, P.; Ndoye, F.-T.; Flin, F.; Perrin, J.; Geindreau, C.; Benkhelifa, H. 3D Characterization of Sponge Cake as Affected by Freezing Conditions Using Synchrotron X-ray Microtomography at Negative Temperature. Foods 2021, 10, 2915. https://0-doi-org.brum.beds.ac.uk/10.3390/foods10122915
Zennoune A, Latil P, Ndoye F-T, Flin F, Perrin J, Geindreau C, Benkhelifa H. 3D Characterization of Sponge Cake as Affected by Freezing Conditions Using Synchrotron X-ray Microtomography at Negative Temperature. Foods. 2021; 10(12):2915. https://0-doi-org.brum.beds.ac.uk/10.3390/foods10122915
Chicago/Turabian StyleZennoune, Amira, Pierre Latil, Fatou-Toutie Ndoye, Frederic Flin, Jonathan Perrin, Christian Geindreau, and Hayat Benkhelifa. 2021. "3D Characterization of Sponge Cake as Affected by Freezing Conditions Using Synchrotron X-ray Microtomography at Negative Temperature" Foods 10, no. 12: 2915. https://0-doi-org.brum.beds.ac.uk/10.3390/foods10122915