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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 #331610
Research Project: Sensing Technologies for the Detection and Characterization of Microbial, Chemical, and Biological Contaminants in Foods

Location: Environmental Microbial & Food Safety Laboratory

Title: Fluorescence hyperspectral imaging technique for the foreign substance detection on fresh-cut lettuce

Author
item MO, CHANGYEUN - Korean Rural Development Administration
item KIM, GIYOUNG - Korean Rural Development Administration
item Kim, Moon
item LIM, JONGGUK - Korean Rural Development Administration
item CHO, HYUNJEONG - National Institute For Agricultural Engineering - Korea
item Barnaby, Jinyoung
item CHO, BYOUNG-KWAN - Chungnam National University

Submitted to: Journal of the Science of Food and Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/11/2017
Publication Date: 3/28/2017
Citation: Mo, C., Kim, G., Kim, M.S., Lim, J., Cho, H., Barnaby, J.Y., Cho, B. 2017. Fluorescence hyperspectral imaging technique for the foreign substance detection on fresh-cut lettuce. Journal of the Science of Food and Agriculture. 97(12):3985-3993.

Interpretive Summary: ARS researchers at Beltsville, MD, have pioneered applications of hyperspectral fluorescence imaging for rapid quality and safety assessments of a variety of agricultural products. In this research, a hyperspectral fluorescence imaging technique was used to develop a rapid multispectral algorithm to detect worms on the surfaces of fresh-cut lettuce. The resultant fluorescence image-based detection method demonstrated 99% accuracy in detecting worms on the surfaces of cut lettuce. This research benefits fresh-cut processing industries that can use the technology on processing lines for rapid online inspection of fresh-cut produce.

Technical Abstract: Nondestructive methods based on fluorescence hyperspectral imaging (HSI) techniques were developed in order to detect worms on fresh-cut lettuce. The optimal wavebands for detecting worms on fresh-cut lettuce were investigated using the one-way ANOVA analysis and correlation analysis. The worm detection imaging algorithms, RSI-I(492-626)/492, resulted in the prediction accuracy of 99.0%. The fluorescence HSI techniques showed that the spectral images with a pixel size of 1 × 1 mm had the best classification accuracy for worms. The overall results demonstrate that hyperspectral fluorescence imaging techniques have the potential to detect worm on fresh-cut lettuce. In the future, our research will focus on the developing a multispectral imaging system to detect the foreign substances such as worms, slug, and earthworms on fresh-cut lettuce.