Image Analysis

To obtain microstructural information from microscopic or other imaging techniques, data need to be collected and analyzed. The first step would require a computer rendering of the image, which may be accomplished by photographing and digitizing the data. In the case of LSCM, this would be done automatically. Once microstructural data are collected, subjective analysis may not be sufficient. Quantification of objects observed in an image may be as simple as measuring its length with an eyepiece micrometer or counting individual components. These measurements, if performed in a large number of samples, could be extremely time-consuming; therefore, automated image analysis techniques may be preferable. However, the automated system used is required to recognize the desired object(s), and differentiate it (them) from artifacts such as overlapping or touching cells, debris, and dirt, so as to exclude them from the measurements (7). Regardless of the method used, analysts need to be sure that their observations are not due to artifacts arising from sample preparation, such as fixing and embedding, or instrumental limitations such as the optics used.

Various objects display irregular shapes, which are difficult to characterize quantitatively. Fractal analysis can be employed to estimate the degree of irregularity in a sample. Unlike Euclidean geometry in which dimensions are expressed in whole numbers (eg, 2 for area and 3 for volume), fractal dimensions consist of fractional numbers (such as 1.3, etc). However, for true fractal objects they must exhibit self-similarity, at least over the range being described (69). This technique can be used to follow changes in the irregularity of a sample due to processing. For example, the extent of agglomeration of coffee particles can be quantified and related to their dispersibility in a liquid (69). The difficulty with fractal analysis lies with the ability to obtain accurate data from imaging technique, such as the edge pixels in a digitized image.

These techniques represent a few, but not all, of the methods available to obtain microstructural information of food products. It is imperative to understand the limitation of each technique, especially when interpreting the results. Proper microstructural analysis can be extremely benefi cial in characterizing changes occurring in food products, which manifest themselves in a macroscopic level.

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