**X-Ray Microtomography for Food Quality Analysis**

Janine Laverse, Pierangelo Frisullo, Amalia Conte and Matteo Alessandro Del Nobile *University of Foggia, Agricultural Faculty, Department of Food Science, Via Napoli, Italia* 

#### **1. Introduction**

338 Food Industrial Processes – Methods and Equipment

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coffee wastewater through chemical flocculation and advanced oxidation processes. *Journal of Environmental Sciences,* Vol. 19, No. 3, pp. 300-305, ISSN 1001-

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> In an effort to understand the physical and rheological behavior as well as the mechanical and sensory attributes of foods, processing focus and emphasis have shifted to the microstructure level. Microstructure elements such as air bubbles or cells, starch granules, protein assemblies and food biopolymer matrices contribute greatly to the identity and quality of foods (Aguilera, 2005). The microstructure of food has an influence over the key attributes of a product as evaluated by consumers. Many of these properties are synergetic, therefore having multiple interactions, and are poorly understood as a result. Advances in the last decade in microscopy techniques, along with an improvement in computing capabilities, has made it possible to understand a food's structure; its relation to physical properties (so called structure-property relationships) and how to engineer and control these properties (Aguilera, 2005). Structure-property relationships can strongly affect the physiochemical, functional, technological and even nutritional properties of foods. For example, with regards to solid food foams like bread, extruded cereals, biscuits and cakes, the consumer appreciation of these products is strongly linked to the texture. For texture, sensory properties of solid food foams are related to both mechanical properties and cellular structure. In this context, determining the relationships between a given mechanical property and the cellular structure is thus of prime importance. It has also been found that the structural organization of the components of cheese, especially the protein network, affect the texture of cheese: in particular the stress at fracture, the modulus and work at fracture could be predicted very well from the size of the protein aggregates (Wium et al., 2003). Cheeses having a regular and close protein matrix with small and uniform (in size and shape) fat globules show a more elastic behavior than cheeses with open structure and numerous and irregular cavities (Buffa et al., 2001). The mechanical properties of cocoa butter are strongly dependent from its morphology at microscopic level and, in particular, from the polymorphic transformation of the fat crystals and the coexistence of different polymorphic forms (Brunello et al., 2003). Thorvaldsson et al. (1999) studied the influence of heating rate on rheology and structure of heat-treated pasta dough. They found that the fastheated samples had pores smaller than the slowly heated one and that the pore dimension affects the energy required to cause a fracture. In particular, the energy required to determine a fracture in the samples having the smallest pores was more than for the

recent years much attention has been focused on extending this imaging technique to food science as a useful technique to aid in the study of food microstructure. The microstructure of food products determines to a large extent the physical, textural and sensory properties of these products. Developing a proper understanding of the microstructure, particularly the spatial distribution and interaction of food components, is a key tool in developing products with desired mechanical and organoleptic properties. Information about the 3-D microstructure of food products and ingredients can be obtained using various imaging techniques. To-date, commonly used techniques are bright-field, polarising and fluorescence light microscopy (LM), confocal scanning laser microscopy (CSLM), transmission electron microscopy (TEM) and scanning electron microscopy (SEM). Other techniques such as atomic force microscopy (AFM), ultrasound and magnetic resonance imaging (MRI) are used for specific food applications. LM requires the staining of the different chemical components of a food (proteins, fat droplets, proteins etc.), therefore it is a more suitable technique for the investigation of multicomponent or multiphase foods such as cereal-based foods (Autio and Salmenkallio-Marttila, 2001). LM, SEM and TEM can be used to highlight various aspects of particulate structures, e. g. in a study on micro-porous, particulate gels (Langton et al., 1996), LM was used to visualize pores, TEM was applied to evaluate particle size and SEM was used to detect how the particles were linked together, i.e. the threedimensional structure. As these techniques require some sample preparation (freezing, dehydration, staining etc.) that may lead to artifacts (Kalab, 1984). On the other hand, CLSM is a more suitable alternative method of analysis of food microstructure, as it requires minimum sample preparation. CLSM has been used for examining the three-dimensional structure of the protein network of pasta samples (Fardet et al., 1998), doughs (Thorvaldsson et al., 1999) and for high-fat foods (Wendin et al., 2000) that cannot be prepared for conventional microscopy without the loss of fat (Autio and Laurikainen, 1997). Atomic force microscopy (AFM) and magnetic resonance imaging (MRI) have been recently introduced into food science as non-destructive techniques. The former is particularly suitable for studying surface roughness, especially in fresh foods (Kaláb et al., 1995) and the latter can be successfully applied for studying processing such as frying, foam drainage, fat crystallization and other operations in which a dynamic study of food structure needs (Kaláb et al., 1995). Takano et al. (2002) and Grenier et al. (2003) used the MRI technique to study qualitatively and quantitatively the local porosity in dough during proving, stage in which invasive analytical methods may cause the dough to collapse. The ultrasound imaging technique is used primarily to investigate the structural properties of meat. It has been used to distinguish crystalline fats from liquid fats (McClements and Povey, 1988) or to determine a food's composition (Chanamai and McClements, 1999). Although, these wide varieties of imaging techniques exist, they are mostly invasive, as they require sample preparation hence, formation of artifacts or are restricted to certain types of food products.

335

**3. X-Ray microtomography – An overview** 

X-ray microtomography (µCT), on the other hand, is a non-invasive technique that has several advantages over other methods, including the ability to image low moisture materials. It uses the differences in X-ray attenuation arising, principally, from differences in density within the specimen. A series of X-ray projections are recorded at a number of angles around the specimen (usually over a range of either 180 or 360 degrees). In µCT, unlike medical CT, the specimen is usually rotated, rather than the X-ray source and

samples having the highest pores. A study carried out on the effects of grind size on peanut butter texture demonstrated that an increase of that variable decreases sensory smoothness, spreadability and adhesiveness (Crippen et al., 1989). Langton et al. (1996) studied correlations existing between microstructure and texture of a particulate protein gel (spherical particles joined together to form strands). They found that the texture, as measured with destructive methods, was sensitive to pore size and particle size, whereas it was sensitive to the strand characteristics if measured with non-destructive methods. Martens and Thybo (2000) investigated the relationships among microstructure and quality attributes of potatoes. They found that volume fraction of raw starch, volume fraction of gelatinized starch and dry matter were positively correlated to reflection, graininess, mealiness, adhesiveness and chewiness and negatively correlated to moistness. From the evidence that microstructure affects food sensorial properties, an important consideration derives: foods having a similar microstructure also have a similar behavior (Kalab et al., 1995). All foods can be analyzed in terms of their chemical composition. This gives limited information about the structure, physical state or sensorial properties. The natural building blocks of foods can be considered as water, air, carbohydrates, proteins and fats. The way in which these are structured during processing ultimately determines the functionality of the food. For example, fat content in meat products is a very important compound influencing the palatability characteristics such as taste, juiciness and texture. In addition, the visual appearance of the fat could affect the consumers overall acceptability of product and therefore the choice when selecting meat product before buying (Helgesen et al., 1998). The design of a food product must account for all these relationships whilst maintaining the high standards expected by consumers. Since microstructure is determined both by nature and processing, food processing can be considered as the way to obtain the desired microstructure (and consequently the desired properties) from the available food components (Aguilera, 2000). As a consequence, knowledge of microstructure must precede the regulation of texture (Ding and Gunasekaran, 1998) and other food attributes. It is also possible to obtain microstructural information by studying mechanical and viscoelastic properties of foods. A food sample submitted to mechanical tests gives rise to a force-time curve from which several parameters related to microstructure can be extrapolated: hardness, cohesiveness, springiness, chewiness, gumminess, stickiness (Martinez et al., 2004). When submitted to a stress (under compression, tension or shear conditions), food samples suffer a strain. The elastic modulus or Young modulus of the analyzed sample can be obtained from the stress-strain curve (Del Nobile et al., 2003; Liu et al., 2003). The viscoelastic properties of a food can be expressed in terms of G', G" and tan δ parameters. G' takes into account the elastic (solid-like) behavior of a material, G" is a measure of the viscous (fluid-like) behavior of a material and tan δ represents the ratio between G" and G'. These parameters can be evaluated by performing dynamic-mechanical and rheological tests (Kokelaar et al., 1996; Brunello et al., 2003; Wildmoser et al., 2004; Ross et al., 2004). Therefore, relationships between microstructural and mechanical properties can therefore be analyzed by means of directionally dependent morphological parameters.

#### **2. Imaging techniques for microstructure studies**

X-ray microtomography (µCT) is a miniaturized version of medical CT or CAT (computed axial tomography) scanning and given the enormous success of x-ray computed tomography (µCT) in medical applications and material science, it is not surprising that in

samples having the highest pores. A study carried out on the effects of grind size on peanut butter texture demonstrated that an increase of that variable decreases sensory smoothness, spreadability and adhesiveness (Crippen et al., 1989). Langton et al. (1996) studied correlations existing between microstructure and texture of a particulate protein gel (spherical particles joined together to form strands). They found that the texture, as measured with destructive methods, was sensitive to pore size and particle size, whereas it was sensitive to the strand characteristics if measured with non-destructive methods. Martens and Thybo (2000) investigated the relationships among microstructure and quality attributes of potatoes. They found that volume fraction of raw starch, volume fraction of gelatinized starch and dry matter were positively correlated to reflection, graininess, mealiness, adhesiveness and chewiness and negatively correlated to moistness. From the evidence that microstructure affects food sensorial properties, an important consideration derives: foods having a similar microstructure also have a similar behavior (Kalab et al., 1995). All foods can be analyzed in terms of their chemical composition. This gives limited information about the structure, physical state or sensorial properties. The natural building blocks of foods can be considered as water, air, carbohydrates, proteins and fats. The way in which these are structured during processing ultimately determines the functionality of the food. For example, fat content in meat products is a very important compound influencing the palatability characteristics such as taste, juiciness and texture. In addition, the visual appearance of the fat could affect the consumers overall acceptability of product and therefore the choice when selecting meat product before buying (Helgesen et al., 1998). The design of a food product must account for all these relationships whilst maintaining the high standards expected by consumers. Since microstructure is determined both by nature and processing, food processing can be considered as the way to obtain the desired microstructure (and consequently the desired properties) from the available food components (Aguilera, 2000). As a consequence, knowledge of microstructure must precede the regulation of texture (Ding and Gunasekaran, 1998) and other food attributes. It is also possible to obtain microstructural information by studying mechanical and viscoelastic properties of foods. A food sample submitted to mechanical tests gives rise to a force-time curve from which several parameters related to microstructure can be extrapolated: hardness, cohesiveness, springiness, chewiness, gumminess, stickiness (Martinez et al., 2004). When submitted to a stress (under compression, tension or shear conditions), food samples suffer a strain. The elastic modulus or Young modulus of the analyzed sample can be obtained from the stress-strain curve (Del Nobile et al., 2003; Liu et al., 2003). The viscoelastic properties of a food can be expressed in terms of G', G" and tan δ parameters. G' takes into account the elastic (solid-like) behavior of a material, G" is a measure of the viscous (fluid-like) behavior of a material and tan δ represents the ratio between G" and G'. These parameters can be evaluated by performing dynamic-mechanical and rheological tests (Kokelaar et al., 1996; Brunello et al., 2003; Wildmoser et al., 2004; Ross et al., 2004). Therefore, relationships between microstructural and mechanical properties can therefore be

analyzed by means of directionally dependent morphological parameters.

X-ray microtomography (µCT) is a miniaturized version of medical CT or CAT (computed axial tomography) scanning and given the enormous success of x-ray computed tomography (µCT) in medical applications and material science, it is not surprising that in

**2. Imaging techniques for microstructure studies** 

recent years much attention has been focused on extending this imaging technique to food science as a useful technique to aid in the study of food microstructure. The microstructure of food products determines to a large extent the physical, textural and sensory properties of these products. Developing a proper understanding of the microstructure, particularly the spatial distribution and interaction of food components, is a key tool in developing products with desired mechanical and organoleptic properties. Information about the 3-D microstructure of food products and ingredients can be obtained using various imaging techniques. To-date, commonly used techniques are bright-field, polarising and fluorescence light microscopy (LM), confocal scanning laser microscopy (CSLM), transmission electron microscopy (TEM) and scanning electron microscopy (SEM). Other techniques such as atomic force microscopy (AFM), ultrasound and magnetic resonance imaging (MRI) are used for specific food applications. LM requires the staining of the different chemical components of a food (proteins, fat droplets, proteins etc.), therefore it is a more suitable technique for the investigation of multicomponent or multiphase foods such as cereal-based foods (Autio and Salmenkallio-Marttila, 2001). LM, SEM and TEM can be used to highlight various aspects of particulate structures, e. g. in a study on micro-porous, particulate gels (Langton et al., 1996), LM was used to visualize pores, TEM was applied to evaluate particle size and SEM was used to detect how the particles were linked together, i.e. the threedimensional structure. As these techniques require some sample preparation (freezing, dehydration, staining etc.) that may lead to artifacts (Kalab, 1984). On the other hand, CLSM is a more suitable alternative method of analysis of food microstructure, as it requires minimum sample preparation. CLSM has been used for examining the three-dimensional structure of the protein network of pasta samples (Fardet et al., 1998), doughs (Thorvaldsson et al., 1999) and for high-fat foods (Wendin et al., 2000) that cannot be prepared for conventional microscopy without the loss of fat (Autio and Laurikainen, 1997). Atomic force microscopy (AFM) and magnetic resonance imaging (MRI) have been recently introduced into food science as non-destructive techniques. The former is particularly suitable for studying surface roughness, especially in fresh foods (Kaláb et al., 1995) and the latter can be successfully applied for studying processing such as frying, foam drainage, fat crystallization and other operations in which a dynamic study of food structure needs (Kaláb et al., 1995). Takano et al. (2002) and Grenier et al. (2003) used the MRI technique to study qualitatively and quantitatively the local porosity in dough during proving, stage in which invasive analytical methods may cause the dough to collapse. The ultrasound imaging technique is used primarily to investigate the structural properties of meat. It has been used to distinguish crystalline fats from liquid fats (McClements and Povey, 1988) or to determine a food's composition (Chanamai and McClements, 1999). Although, these wide varieties of imaging techniques exist, they are mostly invasive, as they require sample preparation hence, formation of artifacts or are restricted to certain types of food products.

#### **3. X-Ray microtomography – An overview**

X-ray microtomography (µCT), on the other hand, is a non-invasive technique that has several advantages over other methods, including the ability to image low moisture materials. It uses the differences in X-ray attenuation arising, principally, from differences in density within the specimen. A series of X-ray projections are recorded at a number of angles around the specimen (usually over a range of either 180 or 360 degrees). In µCT, unlike medical CT, the specimen is usually rotated, rather than the X-ray source and

Hence, X-ray microtomography is fast becoming a very useful tool to aid in the study of food microstructure and is an important development in imaging technology that has eliminated some of the drawbacks of traditional imaging and enabled noninvasive characterization of microstructure food in three dimensions (Flannery et al*.*1987; Sasov and

337

As stated above, the fat content in meat products is a very important compound and nowadays, lots of meat products with different fat contents and different physical and chemical features (protein network, moisture content, ingredients, additives and so on) are being manufactured. Consumers of today, require some of these information e.g. total fat content, types fat, ingredients, additives etc. to be stated Therefore total fat content of meat products (e.g. salami, steak etc) is an important quantity used in numerous studies. Thus, reliable methods for the quantitative analysis of fat from this type of food products are of critical importance. There are several methods to analyze fat content quantitatively (Monin 1998), although the method (AOAC, 1995) that is commonly used is based on chemical analysis, it is quite expensive and time consuming. Furthermore this technique is destructive to the sample, as a result, the same sample cannot be measured more than once and sometimes uses harmful, flammable solvents with health and environmental hazards. With regards to bread, characteristics such as cell wall thickness, cell size, and uniformity of cell size affect the texture of bread crumb (Kamman, 1970) and also the appearance, taste perception and stability of the final product (Autio and Laurikainen, 1997). Crumb elasticity can be predicted from its specific volume and is strongly affected by the amylose-rich regions joining partially gelatinized starch granules in the crumb cell walls (Scanlon and Liu, 2003). Although, microstructure parameters like size and number density of air cells and their contribution to mechanical properties of solid food foams have been studied before (Barrett and Peleg 1992; Barrett et al. 1994a; Van Hecke et al*.* 1995; Gao and Tan 1996), the underlying mechanism relating cellular structure to the mechanics of these products is still not well understood. The limitations of traditional imaging techniques like scanning electron microscopy (SEM) and optical microscopy, which are two dimensional (2-D) and destructive in nature and also provide poor contrast, make it difcult to characterize cellular structure accurately. For the last few years, X-ray tomography has been proven to be particularly well suited for the 3D investigation of cellular materials. These studies have shown that tomography images allow describing accurately in three dimensions the complexity of the morphology of cellular food products. In addition, this technique enables dynamic studies. From a mechanical point of view, structure-property relationships of heterogeneous materials are often addressed through theories incorporating more or less realistic microstructural information. In order to understand the relation between microstructure and mechanical structure, mathematical models can be developed. For these models a number of free parameters are needed *e.g.* initial and final moisture content, structural parameters and type of diffusion. Microstructural parameters can be derived from the 3-D structure visualized by µCT and mechanical structure parameters can be derived

A µCT image is typically called a slice and corresponds to a certain thickness of the object being scanned. Therefore, whereas a typical digital image is composed of pixels (picture elements), a µCT slice image is composed of voxels (volume elements). An X-ray shadow image corresponds to a two-dimensional projection from the three- dimensional object. In the simplest case, it can be described as a parallel X-ray illumination. In this approximation,

Van Dyck, 1998).

from dynamical mechanical analysis.

detector. If the projections are taken through a single plane in the specimen, it is possible to reconstruct a cross sectional image of that plane. In most µCT scanners today, 2D images are recorded, making it possible to reconstruct a complete 3D map of X-ray attenuation. In such cases, because of the divergence of the X-ray beam, it is necessary to use a conebeam reconstruction algorithm. This generally gives only an approximate reconstruction, with errors increasing with distance from the central plane (normal to the rotation axis). By using a spiral locus (translating the specimen along the rotation axis as it rotates) an exact (barring artefacts) reconstruction is possible, but this requires complex reconstruction algorithms, which are currently impracticable for large data sets. In the ideal case, each voxel of data represents the X-ray linear attenuation coefficient (LAC) of the corresponding volume in the specimen only. This is related to the composition and density of the material within that volume. Thus µCT studies can be used both for pure geometric studies, where the LAC is used only to determine the presence or absence of a phase, and quantitative studies where the LAC is used to determine density or concentration. The latter generally requires a higher signal to noise ratio, requiring high dynamic range detection and long X-ray exposures.

A complete µCT analysis is normally made by acquiring a number of radiographs (typically about 1000) of the same sample under different viewing angles (one orientation for each radiograph). A series of 2D X-ray images are obtained as a sample is rotated. A final computed reconstruction step is required to produce a three-dimensional map of the linear attenuation coefficients in the material. This three-dimensional map indirectly gives a picture of the structure density. In µCT, the X-ray source and the detector are placed at the opposite sides of the sample. The spatial resolution of the attenuation map depends on the characteristics of both the detector and the number of X-ray projections. Differences in the linear attenuation coefficients within a material are responsible for the X-ray image contrast. The main contrast formation in µCT is due to absorption contrast. Manipulation and analyses of µCT data using special software also allows reconstruction of cross-sections at depth increments as low as 15 micrometer, and along any desired orientation of the plane of cut. A series of non-invasive µCT slices of the same sample in any direction can provide much more information than just one Scanning Electron Microscopy or optical imaging picture for example. The true 3-D shape of the cells can also be visualized from its 2-D slices (Trater et al, 2005). This technique has been successfully used to observe the stability of gas bubbles in dough during the bread making process (Whitworth and Alava, 2002), the microstructure of foams (Lim and Barigou, 2004.) and ice crystals within frozen foods (Mousavi et. al, 2005).

X-ray microtomography has also proven to be a very useful technique for the non-invasive visualization and measurement of the internal microstructure of cellular food products, such as porous rice kernels and whipped cream (van Dalen et al., 2003), aerated chocolate and muffins (Lim et al., 2004), bread (Falcone et al., 2004; 2005; Lassoued et al., 2007), cornflakes (Chaunier et al., 2007), dough (Mousavi et al., 2005), extruded starches (Babin et al., 2007), French fries (Miri et al., 2006) and biopolymer foams (Trater et al., 2005). X-ray microtomography is non-destructive and provides in-depth information on the microstructure of the food product being tested; therefore a better understanding of the physical structure of the product and from an engineering perspective, knowledge about the microstructure of foods can be used to identify the important processing parameters that affect the quality of a product. Processes are no longer designed from a macroscopic level; knowing the properties of foods on the micro scale determines the process specification.

detector. If the projections are taken through a single plane in the specimen, it is possible to reconstruct a cross sectional image of that plane. In most µCT scanners today, 2D images are recorded, making it possible to reconstruct a complete 3D map of X-ray attenuation. In such cases, because of the divergence of the X-ray beam, it is necessary to use a conebeam reconstruction algorithm. This generally gives only an approximate reconstruction, with errors increasing with distance from the central plane (normal to the rotation axis). By using a spiral locus (translating the specimen along the rotation axis as it rotates) an exact (barring artefacts) reconstruction is possible, but this requires complex reconstruction algorithms, which are currently impracticable for large data sets. In the ideal case, each voxel of data represents the X-ray linear attenuation coefficient (LAC) of the corresponding volume in the specimen only. This is related to the composition and density of the material within that volume. Thus µCT studies can be used both for pure geometric studies, where the LAC is used only to determine the presence or absence of a phase, and quantitative studies where the LAC is used to determine density or concentration. The latter generally requires a higher signal to noise ratio, requiring high dynamic range detection and long X-ray exposures. A complete µCT analysis is normally made by acquiring a number of radiographs (typically about 1000) of the same sample under different viewing angles (one orientation for each radiograph). A series of 2D X-ray images are obtained as a sample is rotated. A final computed reconstruction step is required to produce a three-dimensional map of the linear attenuation coefficients in the material. This three-dimensional map indirectly gives a picture of the structure density. In µCT, the X-ray source and the detector are placed at the opposite sides of the sample. The spatial resolution of the attenuation map depends on the characteristics of both the detector and the number of X-ray projections. Differences in the linear attenuation coefficients within a material are responsible for the X-ray image contrast. The main contrast formation in µCT is due to absorption contrast. Manipulation and analyses of µCT data using special software also allows reconstruction of cross-sections at depth increments as low as 15 micrometer, and along any desired orientation of the plane of cut. A series of non-invasive µCT slices of the same sample in any direction can provide much more information than just one Scanning Electron Microscopy or optical imaging picture for example. The true 3-D shape of the cells can also be visualized from its 2-D slices (Trater et al, 2005). This technique has been successfully used to observe the stability of gas bubbles in dough during the bread making process (Whitworth and Alava, 2002), the microstructure of foams (Lim and Barigou, 2004.) and ice crystals within frozen foods

X-ray microtomography has also proven to be a very useful technique for the non-invasive visualization and measurement of the internal microstructure of cellular food products, such as porous rice kernels and whipped cream (van Dalen et al., 2003), aerated chocolate and muffins (Lim et al., 2004), bread (Falcone et al., 2004; 2005; Lassoued et al., 2007), cornflakes (Chaunier et al., 2007), dough (Mousavi et al., 2005), extruded starches (Babin et al., 2007), French fries (Miri et al., 2006) and biopolymer foams (Trater et al., 2005). X-ray microtomography is non-destructive and provides in-depth information on the microstructure of the food product being tested; therefore a better understanding of the physical structure of the product and from an engineering perspective, knowledge about the microstructure of foods can be used to identify the important processing parameters that affect the quality of a product. Processes are no longer designed from a macroscopic level; knowing the properties of foods on the micro scale determines the process specification.

(Mousavi et. al, 2005).

Hence, X-ray microtomography is fast becoming a very useful tool to aid in the study of food microstructure and is an important development in imaging technology that has eliminated some of the drawbacks of traditional imaging and enabled noninvasive characterization of microstructure food in three dimensions (Flannery et al*.*1987; Sasov and Van Dyck, 1998).

As stated above, the fat content in meat products is a very important compound and nowadays, lots of meat products with different fat contents and different physical and chemical features (protein network, moisture content, ingredients, additives and so on) are being manufactured. Consumers of today, require some of these information e.g. total fat content, types fat, ingredients, additives etc. to be stated Therefore total fat content of meat products (e.g. salami, steak etc) is an important quantity used in numerous studies. Thus, reliable methods for the quantitative analysis of fat from this type of food products are of critical importance. There are several methods to analyze fat content quantitatively (Monin 1998), although the method (AOAC, 1995) that is commonly used is based on chemical analysis, it is quite expensive and time consuming. Furthermore this technique is destructive to the sample, as a result, the same sample cannot be measured more than once and sometimes uses harmful, flammable solvents with health and environmental hazards.

With regards to bread, characteristics such as cell wall thickness, cell size, and uniformity of cell size affect the texture of bread crumb (Kamman, 1970) and also the appearance, taste perception and stability of the final product (Autio and Laurikainen, 1997). Crumb elasticity can be predicted from its specific volume and is strongly affected by the amylose-rich regions joining partially gelatinized starch granules in the crumb cell walls (Scanlon and Liu, 2003). Although, microstructure parameters like size and number density of air cells and their contribution to mechanical properties of solid food foams have been studied before (Barrett and Peleg 1992; Barrett et al. 1994a; Van Hecke et al*.* 1995; Gao and Tan 1996), the underlying mechanism relating cellular structure to the mechanics of these products is still not well understood. The limitations of traditional imaging techniques like scanning electron microscopy (SEM) and optical microscopy, which are two dimensional (2-D) and destructive in nature and also provide poor contrast, make it difcult to characterize cellular structure accurately. For the last few years, X-ray tomography has been proven to be particularly well suited for the 3D investigation of cellular materials. These studies have shown that tomography images allow describing accurately in three dimensions the complexity of the morphology of cellular food products. In addition, this technique enables dynamic studies. From a mechanical point of view, structure-property relationships of heterogeneous materials are often addressed through theories incorporating more or less realistic microstructural information. In order to understand the relation between microstructure and mechanical structure, mathematical models can be developed. For these models a number of free parameters are needed *e.g.* initial and final moisture content, structural parameters and type of diffusion. Microstructural parameters can be derived from the 3-D structure visualized by µCT and mechanical structure parameters can be derived from dynamical mechanical analysis.

A µCT image is typically called a slice and corresponds to a certain thickness of the object being scanned. Therefore, whereas a typical digital image is composed of pixels (picture elements), a µCT slice image is composed of voxels (volume elements). An X-ray shadow image corresponds to a two-dimensional projection from the three- dimensional object. In the simplest case, it can be described as a parallel X-ray illumination. In this approximation,

**Application of µCT technique** 

Table 1 shows the average values obtained for the following six parameters using the CTAn software (Skyscan): Percent object volume (POV), Object surface/volume ratio (OSVR), Fragmentation index (FI), Structure thickness (ST), Structure separation (SS) and Degree of anisotropy (DA) and the results of the statistical analysis carried out as reported below.

339

 POV OSVR FI ST SS DA milano 22.70a1.45 1,42c0.08 -0,041a0.04 4,23ab0.19 8,48b0.69 0,15a0.05 modena 27.65b4.64 0,92a0.10 -0,004a0.30 5,10c0.41 8,23bc0.35 0,21a0.04 napoli 33.10a1.32 1,28b0.07 -0,195a0.21 4,15a0.25 10,88c1.64 0,21a0.03 norcinetto 32.32c1.80 0,84a0.02 -0,287a0.18 4,94c0.18 10,43c1.25 0,21a0.03 ungherese 21.34c0.98 1,23b0.04 -0,309a0.05 4,65bc0.19 6,53a0.31 0,15a0.06 \* All parameters obtained were submitted to one-way analysis of variance (ANOVA) and Duncan's test

Where FI is the index of connectivity and is a measure of relative convexity or concavity of the total solid surface, based on the principle that concavity indicates connectivity, and convexity indicates isolated disconnected structures (Lim et al., 2004). A lower FI signifies better-connected solid lattices and has a negative index while on the other hand a higher FI indicates a more disconnected solid structure and has a positive index. As the fat in salami is considered generally to be of a concave structure, it can be noted from the table that the FI is negative for all samples and there are no statistically significant differences among the samples for this geometric parameter. The degree of anisotropy (DA) is a measure of the 3D structural symmetry, i.e. in this case it indicates the presence or absence of preferential alignment of the fat along a particular direction (Lim et al., 2004). A value of 0 would correspond to total isotropy, whereas a value of 1 would indicate total anisotropy. According to the results obtained for DA (see table 1), the fat present in all samples have a fairly good degree of isotropy and there are no statistical differences among the samples. POV is the percentage of the total fat content present in the sample as proven above. It can be seen that the POV for the Ungherese salami and the Norcinetto salami are statistically equal and are also the highest. There are also no statistical differences in the POV values between the Milano salami and the Napoli salami. OSVR indicates the fat globule size distribution within the sample, the higher the value, the more finely distributed is the fat present in the sample. It can be seen from the table that the Milano salami has the highest value hence its fat content is more finely distributed. Whilst, the Modena salami and the Norcinetto salami are statistically equal i.e. having approximately the same type of fat globule distribution, their low values indicate that the fat globules are more largely distributed with respect to the Milano salami. The results also show that the Napoli salami and the Ungherse salami are statistically equal, hence they have a similar structure with respect to the fat globule size distribution. ST is the average thickness of fat present and SS is the average distant between the fat globules in the samples. The results for ST from table 1 show that Modena, Norcinetto and Ungherese salami are statistically equal, whilst for SS,

(p<0.05) through the statistic package Statistica for Windows (Statsoft, Tulsa, USA).

Table 1. Values for the geometric parameters for the salami samples.

each point on the shadow image contains the integration of absorption information inside the three-dimensional object in the corresponding partial X-ray beam. The X-rays that are transmitted through the object are scattered and/or absorbed. The gray levels in a slice correspond to the X-ray attenuation, which reflects the proportion of X-rays absorbed or scattered as they pass through each voxel. X-ray attenuation is a function of X-ray energy and the density and atomic number of the material being imaged. Directing X-rays through the slice plane from multiple orientations and measuring their resultant decrease in intensity creates a µCT image. A specialized algorithm is used to reconstruct the distribution of X-ray attenuation in the slice plane. By acquiring a stacked, contiguous series of µCT images
