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Sensors 2017, 17(10), 2188; https://doi.org/10.3390/s17102188

Raman Hyperspectral Imaging for Detection of Watermelon Seeds Infected with Acidovorax citrulli

1
Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Powder Mill Rd. Bldg. 303, BARC-East, Beltsville, MD 20705, USA
2
Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea
3
Department of Horticultural Biotechnology and Institute of Life Science and Resources, Kyung Hee University, Yongin 441-701, Korea
*
Author to whom correspondence should be addressed.
Received: 19 July 2017 / Revised: 22 August 2017 / Accepted: 18 September 2017 / Published: 23 September 2017
(This article belongs to the Section Biosensors)
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Abstract

The bacterial infection of seeds is one of the most important quality factors affecting yield. Conventional detection methods for bacteria-infected seeds, such as biological, serological, and molecular tests, are not feasible since they require expensive equipment, and furthermore, the testing processes are also time-consuming. In this study, we use the Raman hyperspectral imaging technique to distinguish bacteria-infected seeds from healthy seeds as a rapid, accurate, and non-destructive detection tool. We utilize Raman hyperspectral imaging data in the spectral range of 400–1800 cm−1 to determine the optimal band-ratio for the discrimination of watermelon seeds infected by the bacteria Acidovorax citrulli using ANOVA. Two bands at 1076.8 cm−1 and 437 cm−1 are selected as the optimal Raman peaks for the detection of bacteria-infected seeds. The results demonstrate that the Raman hyperspectral imaging technique has a good potential for the detection of bacteria-infected watermelon seeds and that it could form a suitable alternative to conventional methods. View Full-Text
Keywords: Raman hyperspectral imaging; spectral analysis; image processing; seed quality Raman hyperspectral imaging; spectral analysis; image processing; seed quality
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Lee, H.; Kim, M.S.; Qin, J.; Park, E.; Song, Y.-R.; Oh, C.-S.; Cho, B.-K. Raman Hyperspectral Imaging for Detection of Watermelon Seeds Infected with Acidovorax citrulli. Sensors 2017, 17, 2188.

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