Pore Structure in Food

Porosity is the ratio of the pore volume to the apparent volume of the porous medium. Food products such as baked, extruded, puffed, dried and frozen foods have an inherent porous micro-structure that gives the product its characteristic texture (Roca et al. 2006; Gogoi et al. 2000) measured in terms of physical properties such as tensile strength, compressive strength and stiffness (Stasiak and Jamroz 2009). Porosity is the macroscopic pore structure parameter which develops in conjunction with the microscopic pore structure. Microscopic pore structure parameters can be summarized as the specific surface area, average pore size and pore size distribution s based on volume, surface area and number of pores, cell wall thickness, pore shape distributions, polydispersity indices for pore sizes and shapes and the pore interconnectivity. These microscopic pore structure parameters also affect transport properties such as thermal diffusivity and moisture diffusivity as well as texture.

This is a special area of research in which there are numerous techniques, equipment and software that allow determination of the pore structure with respect to the number of pores, pore size and shape distributions (Kocer et al. 2007; Hicsasmaz et al. 2003), number of cell faces, cell wall thickness, pore interconnectivity and polydispersity index (Trater et al. 2005). Pore structure analysis is a high technology field in which microscopic techniques such as light microscopy, scanning electron microscopy (SEM) and transmission electron microscopy (TEM) besides numerous image analysis techniques primarily used to visualize the human body for purposes of medical diagnosis are well-adapted. X-ray microtomography (Frisullo et al. 2012; Trater et al. 2005) is one such technique in which 2-D sections can be imaged non-destructively, and then a qualitative 3-D reconstruction can be performed. 2-D micro-tomography images and related software allow measurement of cell wall thicknesses with accuracy. Magnetic resonance imaging (MRI) (Wagner et al. 2008; Ishida et al. 2001), SEM and environmental scanning microscopy (ESEM) (Stokes and Donald 2000) also provide data on the pore structure, SEM being one of the most widely used methods. Ultrasound (Lagrain et al. 2006) is used in microstructure measurements. Mercury porosimetry (Hicsasmaz and Clayton 1992) and liquid extrusion porosimetry (Datta et al. 2007) are quantitative methods which do not use imaging techniques, but that use the capillary penetration technique.

Dependence of texture on the pore structure is one of the most important fields of research. This is due to high sample-to-sample variations in application of mechanical tests. Tension, compression and flexure tests are the most common mechanical tests used in texture evaluation. Textural properties are related to microstructural features by using mechanical models, especially for brittle, solid cellular foods that have undergone glass transition during structural setting.

Processes that occur in porous media such as drying, vapor-induced puffing and rehydration take place in a dynamic environment constrained by initial porosity and/or changes in porosity as the process proceeds. In the literature, modeling of porosity is performed using fundamental concepts (Datta 2007) or empirical correlations (Mayor and Sereno 2004; Saguy et al. 2005).

Gueven and Hicsasmaz (2011) studied the microstructure of bread and cookie samples based on a 1-D corrugated pore network (Tsetsekou et al. 1991) (Fig. 4.​1b). Experimental data on porosity and pore size distribution (Hicsasmaz and Clayton 1992) were used to construct the pore network. Intrusion of non-wetting mercury was simulated on the constructed porous network. The simulation results were compared with the experimental mercury porosimetry data in terms of mercury intruded pore volume versus capillary pressure (Figs. 5.1 and 5.2).

Although the pore network model is widely used in petroleum reservoir engineering, soil science and catalysis, very few applications of the model to food systems are available. One of the applications is the 1-D geometric network applied to capillary penetration as described above (Gueven and Hicsasmaz 2011). Besides, 2-D geometric network models to simulate drying (Surasani et al. 2009) and moisture diffusion (Pratomak et al. 2010) were proposed.

As a result, it can be concluded that a pore network model is capable of simulating the internal pore structure of porous food samples with relatively higher porosity (porosity >0.5). Realistic network simulations were possible for materials with porosity <0.85 using the 1-D network under the constraints of experimental pore size histograms, experimentally measured porosity and the experimental mercury intrusion curves. The major difference between the proposed network model and the ones applied for lower porosity materials is that a complete random distribution of all capillary sizes could not be used. Instead, the capillary diameter range was divided into a number of diameter sub-ranges each of which was separately distributed. Also, the network model had to be further constrained with respect to various diameter sub-ranges being placed next to each other.