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Sensors 2015, 15(11), 29511-29534; doi:10.3390/s151129511

Detection of Lettuce Discoloration Using Hyperspectral Reflectance Imaging

1
National Institute of Agricultural Science, Rural Development Administration, 310 Nonsaengmyeong-ro, Wansan-gu, Jeonju-si, Jeollabuk-do 54875, Korea
2
Environmental Microbiology and Food Safety Laboratory, BARC-East, Agricultural Research Service, US Department of Agriculture, 10300 Baltimore Avenue, Beltsville, MD 20705, USA
3
Experiment & Research Institute, National Agricultural Products Quality Management Service, 141 Yongjeon-ro, Gimcheon-si, Gyeongsangbuk-do 39660, Korea
4
Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Gonzalo Pajares Martinsanz
Received: 10 July 2015 / Revised: 11 November 2015 / Accepted: 17 November 2015 / Published: 20 November 2015
View Full-Text   |   Download PDF [2931 KB, uploaded 20 November 2015]   |  

Abstract

Rapid visible/near-infrared (VNIR) hyperspectral imaging methods, employing both a single waveband algorithm and multi-spectral algorithms, were developed in order to discrimination between sound and discolored lettuce. Reflectance spectra for sound and discolored lettuce surfaces were extracted from hyperspectral reflectance images obtained in the 400–1000 nm wavelength range. The optimal wavebands for discriminating between discolored and sound lettuce surfaces were determined using one-way analysis of variance. Multi-spectral imaging algorithms developed using ratio and subtraction functions resulted in enhanced classification accuracy of above 99.9% for discolored and sound areas on both adaxial and abaxial lettuce surfaces. Ratio imaging (RI) and subtraction imaging (SI) algorithms at wavelengths of 552/701 nm and 557–701 nm, respectively, exhibited better classification performances compared to results obtained for all possible two-waveband combinations. These results suggest that hyperspectral reflectance imaging techniques can potentially be used to discriminate between discolored and sound fresh-cut lettuce. View Full-Text
Keywords: hyperspectral imaging; multispectral imaging; lettuce; discoloration; image processing hyperspectral imaging; multispectral imaging; lettuce; discoloration; image processing
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Mo, C.; Kim, G.; Lim, J.; Kim, M.S.; Cho, H.; Cho, B.-K. Detection of Lettuce Discoloration Using Hyperspectral Reflectance Imaging. Sensors 2015, 15, 29511-29534.

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