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Raman Hyperspectral Imaging for Detection of Watermelon Seeds Infected with Acidovorax citrulli.
Sensors (Basel). 2017 Sep 23; 17(10)S

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.

Authors+Show Affiliations

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. hoonsoo.lee@ars.usda.gov. Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea. hoonsoo.lee@ars.usda.gov.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. moon.kim@ars.usda.gov.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. jianwei.qin@ars.usda.gov.Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea. besoo12@cnu.ac.kr.Department of Horticultural Biotechnology and Institute of Life Science and Resources, Kyung Hee University, Yongin 441-701, Korea. yulimy@khu.ac.kr.Department of Horticultural Biotechnology and Institute of Life Science and Resources, Kyung Hee University, Yongin 441-701, Korea. co35@khu.ac.kr.Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea. chobk@cnu.ac.kr.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

28946608

Citation

Lee, Hoonsoo, et al. "Raman Hyperspectral Imaging for Detection of Watermelon Seeds Infected With Acidovorax Citrulli." Sensors (Basel, Switzerland), vol. 17, no. 10, 2017.
Lee H, Kim MS, Qin J, et al. Raman Hyperspectral Imaging for Detection of Watermelon Seeds Infected with Acidovorax citrulli. Sensors (Basel). 2017;17(10).
Lee, H., Kim, M. S., Qin, J., Park, E., Song, Y. R., Oh, C. S., & Cho, B. K. (2017). Raman Hyperspectral Imaging for Detection of Watermelon Seeds Infected with Acidovorax citrulli. Sensors (Basel, Switzerland), 17(10). https://doi.org/10.3390/s17102188
Lee H, et al. Raman Hyperspectral Imaging for Detection of Watermelon Seeds Infected With Acidovorax Citrulli. Sensors (Basel). 2017 Sep 23;17(10) PubMed PMID: 28946608.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Raman Hyperspectral Imaging for Detection of Watermelon Seeds Infected with Acidovorax citrulli. AU - Lee,Hoonsoo, AU - Kim,Moon S, AU - Qin,Jianwei, AU - Park,Eunsoo, AU - Song,Yu-Rim, AU - Oh,Chang-Sik, AU - Cho,Byoung-Kwan, Y1 - 2017/09/23/ PY - 2017/07/19/received PY - 2017/08/22/revised PY - 2017/09/18/accepted PY - 2017/9/27/entrez PY - 2017/9/28/pubmed PY - 2018/6/2/medline KW - Raman hyperspectral imaging KW - image processing KW - seed quality KW - spectral analysis JF - Sensors (Basel, Switzerland) JO - Sensors (Basel) VL - 17 IS - 10 N2 - 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. SN - 1424-8220 UR - https://www.unboundmedicine.com/medline/citation/28946608/Raman_Hyperspectral_Imaging_for_Detection_of_Watermelon_Seeds_Infected_with_Acidovorax_citrulli_ L2 - https://www.mdpi.com/resolver?pii=s17102188 DB - PRIME DP - Unbound Medicine ER -