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Proceedings Paper

Application of machine vision to pup loaf bread evaluation
Author(s): Inna Y. Zayas; O. K. Chung
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Paper Abstract

Intrinsic end-use quality of hard winter wheat breeding lines is routinely evaluated at the USDA, ARS, USGMRL, Hard Winter Wheat Quality Laboratory. Experimental baking test of pup loaves is the ultimate test for evaluating hard wheat quality. Computer vision was applied to developing an objective methodology for bread quality evaluation for the 1994 and 1995 crop wheat breeding line samples. Computer extracted features for bread crumb grain were studied, using subimages (32 by 32 pixel) and features computed for the slices with different threshold settings. A subsampling grid was located with respect to the axis of symmetry of a slice to provide identical topological subimage information. Different ranking techniques were applied to the databases. Statistical analysis was run on the database with digital image and breadmaking features. Several ranking algorithms and data visualization techniques were employed to create a sensitive scale for porosity patterns of bread crumb. There were significant linear correlations between machine vision extracted features and breadmaking parameters. Crumb grain scores by human experts were correlated more highly with some image features than with breadmaking parameters.

Paper Details

Date Published: 18 December 1996
PDF: 12 pages
Proc. SPIE 2907, Optics in Agriculture, Forestry, and Biological Processing II, (18 December 1996); doi: 10.1117/12.262863
Show Author Affiliations
Inna Y. Zayas, USDA Agricultural Research Service (United States)
O. K. Chung, USDA Agricultural Research Service (United States)


Published in SPIE Proceedings Vol. 2907:
Optics in Agriculture, Forestry, and Biological Processing II
George E. Meyer; James A. DeShazer, Editor(s)

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