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Article

Development of Fluorescence Imaging Technique to Detect Fresh-Cut Food Organic Residue on Processing Equipment Surface

1
Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, Agricultural Research Service, USDA, 10300 Baltimore Avenue, Beltsville, MD 20705, USA
2
Department of Biosystems Engineering, College of Agricultural and Life Sciences, Kangwon National University, 1 Gangwon Daehakgil, Chuncheon-Si, Gangwon-Do 24341, Korea
3
National Institute of Agricultural Sciences, Rural Development Administration, 310 Nonsaengmyeong-ro, Wansan-gu, Jeonju-si, Jeollabuk-do 54875, Korea
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2021, 11(1), 458; https://doi.org/10.3390/app11010458
Received: 12 November 2020 / Revised: 24 December 2020 / Accepted: 30 December 2020 / Published: 5 January 2021
With increasing public demand for ready-to-eat fresh-cut food products, proper sanitation of food-processing equipment surfaces is essential to mitigate potential contamination of these products to ensure safe consumption. This study presents a sanitation monitoring technique using hyperspectral fluorescence images to detect fruit residues on food-processing equipment surfaces. An algorithm to detect residues on the surfaces of 2B-finished and #4-finished stainless-steel, both commonly used in food processing equipment, was developed. Honeydew, orange, apple, and watermelon were selected as representatives since they are mainly used as fresh-cut fruits. Hyperspectral fluorescence images were obtained for stainless steel sheets to which droplets of selected fruit juices at six concentrations were applied and allowed to dry. The most significant wavelengths for detecting juice at each concentration were selected through ANOVA analysis. Algorithms using a single waveband and using a ratio of two wavebands were developed for each sample and for all the samples combined. Results showed that detection accuracies were better for the samples with higher concentrations. The integrated algorithm had a detection accuracy of 100% and above 95%, respectively, for the original juice up to the 1:20 diluted samples and for the more dilute 1:50 to 1:100 samples, respectively. The results of this study establish that using hyperspectral imaging, even a small residual quantity that may exist on the surface of food processing equipment can be detected and that sanitation monitoring and management is possible. View Full-Text
Keywords: fresh-cut food; hyperspectral fluorescence; stainless steel; organic residue; detection fresh-cut food; hyperspectral fluorescence; stainless steel; organic residue; detection
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MDPI and ACS Style

Hwang, C.; Mo, C.; Seo, Y.; Lim, J.; Baek, I.; Kim, M.S. Development of Fluorescence Imaging Technique to Detect Fresh-Cut Food Organic Residue on Processing Equipment Surface. Appl. Sci. 2021, 11, 458. https://doi.org/10.3390/app11010458

AMA Style

Hwang C, Mo C, Seo Y, Lim J, Baek I, Kim MS. Development of Fluorescence Imaging Technique to Detect Fresh-Cut Food Organic Residue on Processing Equipment Surface. Applied Sciences. 2021; 11(1):458. https://doi.org/10.3390/app11010458

Chicago/Turabian Style

Hwang, Chansong, Changyeun Mo, Youngwook Seo, Jongguk Lim, Insuck Baek, and Moon S. Kim 2021. "Development of Fluorescence Imaging Technique to Detect Fresh-Cut Food Organic Residue on Processing Equipment Surface" Applied Sciences 11, no. 1: 458. https://doi.org/10.3390/app11010458

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