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Optical Engineering

Visual detection of particulates in x-ray images of processed meat products
Author(s): Thomas F. Schatzki; Richard Young; Ron P. Haff; J. Eye; G. Wright
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Paper Abstract

A study was conducted to test the efficacy of detecting particulate contaminants in processed meat samples by visual observation of line-scanned x-ray images. Six hundred field-collected processedproduct samples were scanned at 230 cm2/s using 0.5 X 0.5-mm resolution and 50 kV, 13 mA excitation. The x-ray images were image corrected, digitally stored, and inspected off-line, using interactive image enhancement. Forty percent of the samples were spiked with added contaminants to establish the visual recognition of contaminants as a function of sample thickness (1 to 10 cm), texture of the x-ray image (smooth/ textured), spike composition (wood/bone/glass), size (0.1 to 0.4 cm), and shape (splinter/round). The results were analyzed using a maximum likelihood logistic regression method. In packages less than 6 cm thick, 0.2-cm-thick bone chips were easily recognized, 0.1-cm glass splinters were recognized with some difficulty, while 0.4-cm-thick wood was generally missed. Operational feasibility in a time-constrained setting was confirmed. One half percent of the samples arriving from the field contained bone slivers >1 cm long, 1/2% contained metallic material, while 4% contained particulates exceeding 0.3 cm in size. All of the latter appeared to be bone fragments.

Paper Details

Date Published: 1 August 1996
PDF: 6 pages
Opt. Eng. 35(8) doi: 10.1117/1.601010
Published in: Optical Engineering Volume 35, Issue 8
Show Author Affiliations
Thomas F. Schatzki, USDA Agricultural Research Service (United States)
Richard Young, U.S. Dept. of Agriculture (United States)
Ron P. Haff, USDA Agricultural Research Service (United States)
J. Eye, USDA FSIS Midwestern Lab. (United States)
G. Wright, USDA FSIS Midwestern Lab. (United States)

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