| Literature DB >> 30155475 |
H A Rathnayake1, S B Navaratne1, C M Navaratne2.
Abstract
Quality evaluation of the porous crumb structure of leavened baked goods, especially bread, has become a vast study area of which various research studies have been carried out up to date. Here is a brief review focusing on those studies with six main parts including porous crumb structure development, crumb cellular structure analysis, application of fractal dimension for evaluating crumb cellular structure, mechanical and sensorial properties of crumb structure, changes of porous crumb structure with staling, and modifications to obtain a well-developed porous crumb structure and retard staling. Development of the porous crumb structure mainly depends on dough ingredients and processing conditions. Hence, certain modifications for those factors (incorporating food hydrocolloids, emulsifiers, improvers, etc.) have been conducted by cereal sciences for obtaining well-developed porous crumb structure and retard staling. Several image analysis methods are available for analyzing microstructural features of porous crumb structure, which can directly affect the mechanical and sensorial properties of the final product. A product with a well-developed porous crumb structure may contain the property of higher gas retention capacity which results in a product with increased volume and reduced crumb hardness with appealing sensorial properties.Entities:
Year: 2018 PMID: 30155475 PMCID: PMC6098858 DOI: 10.1155/2018/8187318
Source DB: PubMed Journal: Int J Food Sci ISSN: 2314-5765
Figure 1X-ray microtomography 2D reconstructed cross section images of cake samples [11].
Figure 2Normalized magnetic resonance images (MRI) of bread acquired during baking. That is, during baking, the cold colors convert to warm colors corresponding to low to high signal intensities, respectively [65].
Figure 3Original (2D) Gray Image (a) and segmented (b) gas cells where black pixels represent bubbles and white pixels represent the porous structure [18].
List of examples of image processing software.
| Image processing software | Detail | References |
|---|---|---|
| ImageJ | version 1.29, Natl. Inst. of Health, Bethseda, Md., U.S.A | Lassoued et al., [ |
| Bajd and Serša, [ | ||
| Tlapale-Valdivia et al., [ | ||
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| Pérez-Nieto et al., [ | |
| Curic et al., [ | ||
| Scheuer et al., [ | ||
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| SigmaScan®Pro | Version:5.50.4522.1800IC by Drug discovery Online | Romano et al., [ |
| | Angioloni and Collar [ | |
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| MATLAB | The MathWorks Inc., | Gonzales-Barron and Butler [ |
| Bajd and Serša, [ | ||
| Natick, MA, USA | Shehzad et al.,[ | |
| Rouillé et al.,[ | ||
| | Verdú et al., [ | |
| Eduardo, Svanberg and Ahrné [ | ||
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| Gebäckanalyse | Ver 1.3c 1997/98 program (Hochschule Ostwestfalen Lippe, Germany | Onyango, Unbehend and Lindhauer [ |
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| Labview | Vision Assistant 2009, National Instruments, USA | Che Pa et al., [ |
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| UTHSCSA | Version 2.0, University of Texas Health Science Centre, San Antonio, Texas | Skendi et al., [ |
| ImageTool programme | ||
Figure 4Original 2D Gray Image (a) and segmented (b) gas cells using K-means algorithm [44].
Figure 5Instrumental texture profile analysis obtained with a TA-XT2 Texturometer [72].