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Proceedings Paper

Hyperspectral imaging for differentiation of foreign materials from pinto beans
Author(s): Mehrube Mehrubeoglu; Michael Zemlan; Sam Henry
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Paper Abstract

Food safety and quality in packaged products are paramount in the food processing industry. To ensure that packaged products are free of foreign materials, such as debris and pests, unwanted materials mixed with the targeted products must be detected before packaging. A portable hyperspectral imaging system in the visible-to-NIR range has been used to acquire hyperspectral data cubes from pinto beans that have been mixed with foreign matter. Bands and band ratios have been identified as effective features to develop a classification scheme for detection of foreign materials in pinto beans. A support vector machine has been implemented with a quadratic kernel to separate pinto beans and background (Class 1) from all other materials (Class 2) in each scene. After creating a binary classification map for the scene, further analysis of these binary images allows separation of false positives from true positives for proper removal action during packaging.

Paper Details

Date Published: 1 September 2015
PDF: 8 pages
Proc. SPIE 9611, Imaging Spectrometry XX, 96110A (1 September 2015); doi: 10.1117/12.2207797
Show Author Affiliations
Mehrube Mehrubeoglu, Texas A&M Univ. Corpus Christi (United States)
Michael Zemlan, Surface Optics Corp. (United States)
Sam Henry, Surface Optics Corp. (United States)

Published in SPIE Proceedings Vol. 9611:
Imaging Spectrometry XX
Thomas S. Pagano; John F. Silny, Editor(s)

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