Image processing methods and fractal analysis for quantitative evaluation of size, shape, structure and microstructure in food materials

Abstract

In recent years, image analysis methods have been applied for quantitative evaluation of morphology, structure and microstructure of foodstuffs. Image processing techniques usually consist of five steps (Castleman, 1996; Pedreschi et al., 2004; Du and Sun, 2004), which are: 1) image capture, 2) pre-processing, 3) image segmentation, 4) feature extraction and 5) classification. In food engineering applications, some or all of these steps have been used to extract information from food images captured with different acquisition systems. The extracted information is useful to translate the food system complexity to numeric data that shall be analyzed to improve the understanding of structure-function relationships of complex systems, such as food and biological materials. On the other hand, fractal analysis has been successfully applied for quantitative evaluation of irregular surfaces and textures of biological materials (Quevedo et al. 2002; Chanona et al., 2003; Villalobos et. al, 2005), and also to characterize ruggedness and geometric complexities of different food particles, such as instant coffee, skim milk, potato starch powder, maltodextrin particles and others (Peleg and Normand, 1985; Barletta and Barbosa, 1993; Shafiur, 1997; Alamilla et al., 2005). The key to quantifying the irregularity of the contours and surfaces in food materials is to evaluate the apparent fractal dimension (FD) by extracting it from the images of structural and microstructural features. Results from fractal analysis are important in examining the architecture and structure-functionality properties of food products. The objective of this contribution was to provide a brief description of diverse methods for image processing and fractal analysis of acquired data in our laboratory for quantitative evaluation of size, shape, structure and microstructure in different biological/food materials.