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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 #377127

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

Location: Environmental Microbial & Food Safety Laboratory

Title: Geographical origin discrimination of white rice based on image pixel size using hyperspectral fluorescence imaging analysis

Author
item KIM, MIN-JIE - KANGWON NATIONAL UNIVERSITY
item LIM, JONGGUK - RURAL DEVELOPMENT ADMINISTRATION - KOREA
item KWON, SUNG - RURAL DEVELOPMENT ADMINISTRATION - KOREA
item KIM, GIYOUNG - KOREAN RURAL DEVELOPMENT ADMINISTRATION
item Kim, Moon
item CHO, BYOUNG-KWAN - CHUNGNAM NATIONAL UNIVERSITY
item BAEK, INSUCK - ORISE FELLOW
item LEE, SEUNG - SEOUL NATIONAL UNIVERSITY
item SEO, YOUNGWOOK - RURAL DEVELOPMENT ADMINISTRATION - KOREA
item MO, CHANGYEUN - KANGWON NATIONAL UNIVERSITY

Submitted to: Applied Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/19/2020
Publication Date: 8/21/2020
Citation: Kim, M., Lim, J., Kwon, S.W., Kim, G., Kim, M.S., Cho, B., Baek, I., Lee, S.H., Seo, Y., Mo, C. 2020. Geographical origin discrimination of white rice based on image pixel size using hyperspectral fluorescence imaging analysis. Applied Sciences. 10(17), 6794. https://doi.org/doi:10.3390/app10175794.
DOI: https://doi.org/10.3390/app10175794

Interpretive Summary: The geographical origin of food is often associated with specific attributes related to food quality and usability as well as to fair marketing and distribution. Significant price differences between domestic and imported agricultural products can be motivators for profit by fraud via false representation of less costly imported products. Such fraud may not only reduce consumer confidence in product quality and distribution, but can also lead to potential food safety problems. This study developed and tested a non-destructive spectral imaging method to differentiate between white rice samples of South Korean origin and of Chinese origin, using rice grown in eight areas of South Korea and four areas of northern China between 2014 and 2016. Hyperspectral fluorescence images acquired for the surfaces of 50-gram rice samples presented in square sample cells (5 cm x 5 cm) were used to develop classification models to identify country of origin which demonstrated over 98% accuracy. Results showed that this nondestructive hyperspectral fluorescence imaging method can effectively identify white rice imported from China to help detect fraudulent distribution in South Korea. Further research to develop the method for more comprehensive use with rice originating elsewhere, such as regions of the United States and Southeast Asia, would benefit rice producers worldwide and help ensure and regulate food safety and quality standards for this major staple crop.

Technical Abstract: Geographical origin discrimination of white rice is an important endeavor in preventing illegal distribution of white rice and regulating and standardizing food safety and quality assurance. The aim of this study was to develop a method for geographical origin discrimination between South Korean and Chinese rice using a hyperspectral fluorescence imaging technique and multivariate analysis. Hyperspectral fluorescence images of South Korean and Chinese rice samples were obtained in the range of 420 nm to 780 nm using 365-nm-wavelength UV-A excitation light. Partial least squares discriminant analysis models were developed and applied to the acquired image to determine the geographical origins of the rice samples. In addition, various pre-processing techniques were applied to improve the discrimination accuracy. Accordingly, the pixel size of the hyperspectral image was determined. The results revealed that the optimum pixel size of the hyperspectral image that was above 7 mm × 7 mm showed a high discrimination accuracy. Moreover, the geographical origin discrimination model that applied the first-order derivative achieved a high discrimination accuracy of 98.89%. The results of this study showed that hyperspectral fluorescence imaging technology can be used to quickly and accurately discriminate the geographical origins of white rice.