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Proceedings Paper

Characterizing fiber formation in meat analogs using an anisotropic photon migration model
Author(s): J. Ranasinghesagara; F. Hsieh; G. Yao
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Paper Abstract

Animal meat products may not be the best choice for many people in the world due to various reasons such as cost, health problems, or religious restrictions. High moisture (40-80%) extrusion technology shows a great promise for texturizing vegetable proteins into fibrous meat alternatives. Soy protein which is healthy, highly nutritious, low in both fat and carbohydrate has been used in high moisture extrusion process to produce meat analogs with well formed fiber that resemble chicken or turkey breast meat. Assessing fiber formation in extruded products is important for controlling extrusion quality in manufacturing process. Although several methods have been studied for quantifying fiber formation in extrudates, their applications for real time quality control in manufacturing process have been challenging. We explored the possibility of applying a nondestructive method based on backscattered reflectance to measure the fiber formation of extruded soy proteins. An image processing method was developed to extract the light reflectance profile at the extrudates' surface. We applied the anisotropic continuous time random walk (CTRW) theory to quantitatively describe the fiber formation in extrudates based on extracted surface reflectance profiles. This method has a potential to be used as a non-destructive, fast, real time quality control tool for products with fibrous structures.

Paper Details

Date Published: 23 October 2006
PDF: 8 pages
Proc. SPIE 6381, Optics for Natural Resources, Agriculture, and Foods, 63810D (23 October 2006); doi: 10.1117/12.684747
Show Author Affiliations
J. Ranasinghesagara, Univ. of Missouri, Columbia (United States)
F. Hsieh, Univ. of Missouri, Columbia (United States)
G. Yao, Univ. of Missouri, Columbia (United States)


Published in SPIE Proceedings Vol. 6381:
Optics for Natural Resources, Agriculture, and Foods
Yud-Ren Chen; George E. Meyer; Shu-I Tu, Editor(s)

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