|HWANG, CHANSONG - Korean Rural Development Administration
|MO, CHANGYEUN - Kangwon National University
|SEO, YOUNGWOOK - Korean Rural Development Administration
|LIM, JONGGUK - Korean Rural Development Administration
|BAEK, INSUCK - Orise Fellow
Submitted to: Applied Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/30/2020
Publication Date: 1/5/2021
Citation: Hwang, C., Mo, C., Seo, Y., Lim, J., Baek, I., Kim, M.S. 2021. Development of fluorescence imaging technique to detect fresh-cut food organic residue on processing equipment surface. Applied Sciences. 11(1), 458. https://doi.org/10.3390/app11010458.
Interpretive Summary: Proper sanitation of stainless steel equipment surfaces in food processing plants is essential to mitigate potential cross-contamination of fresh-cut produce such as sliced fruit often packaged as ready-to-eat products. This study investigated a hyperspectral fluorescence imaging method to detect residues of diluted four fruit juices—honeydew melon, orange, apple, and watermelon—on the surfaces of 2B- and #4-finished stainless steel, which are commonly used for industrial food equipment. Image-based detection algorithms using a single waveband and a ratio image of two wavebands were developed that could detect over 95% of residue spots dried on stainless steel from droplets of raw juice and of juice diluted with water at ratios of 1:5, 1:10, 1:20, 1:50, and 1:100. The results show that hyperspectral fluorescence has the potential to fill a role in sanitation monitoring and management practices of fresh-cut produce processors to help them prevent cross-contamination of food and ensure safe ready-to-eat products for consumers.
Technical Abstract: With ever-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 food that can pose risks of foodborne illnesses. 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. Hyperspectral fluorescence images were obtained for stainless steel sheets to which droplets of honeydew, orange, apple, and watermelon juices at six concentrations were are applied and allowed to dry. The most significant wavelengths for detecting each dilution concentration was selected through ANOVA analysis. Algorithms using a single waveband and 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 over 95%, respectively, for the original juice up to the 1:20 diluted samples and for the more dilute 1:50 and 1:100 samples, respectively. The results of this study establish that using hyperspectral imaging, even a small residual quantity on the surface of food processing equipment can be detected, and that sanitation monitoring and management is possible.