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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Environmental Microbial & Food Safety Laboratory » Research » Publications at this Location » Publication #344683

Research Project: Sensing Technologies for the Detection and Characterization of Microbial, Chemical, and Biological Contaminants in Foods

Location: Environmental Microbial & Food Safety Laboratory

Title: Raman hyperspectral imaging for detection of watermelon seeds infected with acidovorax avenae subsp. citrulli

item LEE, HOONSOO - Us Forest Service (FS)
item Kim, Moon
item Qin, Jianwei - Tony Qin
item PARK, EUNSOO - Chungnam National University
item SONG, YU-RIM - Kyung Hee University
item OH, CHANGSIK - Kyung Hee University
item CHO, BYOUNGKWAN - Chungnam National University

Submitted to: Sensors
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/18/2017
Publication Date: 9/23/2017
Citation: Lee, H., Kim, M.S., Qin, J., Park, E., Song, Y., Oh, C., Cho, B. 2017. Raman hyperspectral imaging for detection of watermelon seeds infected with acidovorax avenae subsp. citrulli. Sensors. 17(10):2188.

Interpretive Summary: Bacterial infection is particularly important seed quality factor that can greatly reduce crop yields, especially when infections not only affect the eventual seedlings but also can spread to other nearby seedlings in fields or greenhouses. In addition to requiring significant time and expensive equipment, conventional methods for sampling tests to detect infected seeds usually are sample destructive or require seedling grow-out, unfortunately rendering them unsuited for screening higher volumes of seeds. This study analyzed Raman hyperspectral images acquired of healthy watermelon seeds and seeds infected with Acidovorax avenae subsp. citrulli, a bacteria that can devastate melons and also affects others in the cucurbitaceae family such as pumpkins, cucumbers, and zucchini, to select imaging wavebands useful for infection detection. The results showed a two-waveband ratio image using 1076.8 cm-1 and 437 cm-1 could effectively separate healthy and infected seeds rapidly and accurately. The method developed and demonstrated in this research can be applied to the development of non-destructive seed quality inspection systems that will help producers to reduce crop losses, maximize yields, and improve supply and quality for consumers.

Technical Abstract: The bacterial infection of seeds forms 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 further, the testing processes are also time-consuming. In this study, we use the Raman hyperspectral imaging technique to discriminate bacteria-infected seeds from healthy seeds as a rapid, accurate, and nondestructive 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 avenae subsp. 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 exhibits good potential in the detection of bacteria-infected watermelon seeds and that it could form a suitable alternative to conventional methods.