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

Chun-Chieh Yang; Moon S. Kim; Kuanglin Chao; Sukwon Kang; Alan M. Lefcourt

doi:10.1117/12.884804

2 June 2011 Fast and accurate image recognition algorithms for fresh produce food safety sensing

Chun-Chieh Yang, Moon S. Kim, Kuanglin Chao, Sukwon Kang, Alan M. Lefcourt

Proceedings Volume 8027, Sensing for Agriculture and Food Quality and Safety III; 80270G (2011) https://doi.org/10.1117/12.884804
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

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.

Citation Download Citation

Chun-Chieh Yang, Moon S. Kim, Kuanglin Chao, Sukwon Kang, and Alan M. Lefcourt "Fast and accurate image recognition algorithms for fresh produce food safety sensing", Proc. SPIE 8027, Sensing for Agriculture and Food Quality and Safety III, 80270G (2 June 2011); https://doi.org/10.1117/12.884804

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