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Proceedings Paper

Fast and accurate image recognition algorithms for fresh produce food safety sensing
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Paper Abstract

This research developed and evaluated the multispectral algorithms derived from hyperspectral line-scan fluorescence imaging under violet LED excitation for detection of fecal contamination on Golden Delicious apples. The algorithms utilized the fluorescence intensities at four wavebands, 680 nm, 684 nm, 720 nm, and 780 nm, for computation of simple functions for effective detection of contamination spots created on the apple surfaces using four concentrations of aqueous fecal dilutions. The algorithms detected more than 99% of the fecal spots. The effective detection of feces showed that a simple multispectral fluorescence imaging algorithm based on violet LED excitation may be appropriate to detect fecal contamination on fast-speed apple processing lines.

Paper Details

Date Published: 2 June 2011
PDF: 12 pages
Proc. SPIE 8027, Sensing for Agriculture and Food Quality and Safety III, 80270G (2 June 2011); doi: 10.1117/12.884804
Show Author Affiliations
Chun-Chieh Yang, U.S.D.A. Agricultural Research Service (United States)
Moon S. Kim, U.S.D.A. Agricultural Research Service (United States)
Kuanglin Chao, U.S.D.A. Agricultural Research Service (United States)
Sukwon Kang, National Academy of Agricultural Science (Korea, Republic of)
Alan M. Lefcourt, U.S.D.A. Agricultural Research Service (United States)


Published in SPIE Proceedings Vol. 8027:
Sensing for Agriculture and Food Quality and Safety III
Moon S. Kim; Shu-I Tu; Kaunglin Chao, Editor(s)

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