Journal of Biosystems Engineering

Korean Society for Agricultural Machinery (한국농업기계학회)
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Hyperspectral Imaging and Partial Least Square Discriminant Analysis for Geographical Origin Discrimination of White Rice

  • Mo, Changyeun (National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Lim, Jongguk (National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Kwon, Sung Won (Research Institute of Pharmaceutical Sciences and College of Pharmacy, Seoul National University) ;
  • Lim, Dong Kyu (Research Institute of Pharmaceutical Sciences and College of Pharmacy, Seoul National University) ;
  • Kim, Moon S. (Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, US Department of Agriculture) ;
  • Kim, Giyoung (National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Kang, Jungsook (National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Kwon, Kyung-Do (National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Cho, Byoung-Kwan (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University)
  • Received : 2017.10.20
  • Accepted : 2017.11.08
  • Published : 2017.12.01
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Abstract

Purpose: This study aims to propose a method for fast geographical origin discrimination between domestic and imported rice using a visible/near-infrared (VNIR) hyperspectral imaging technique. Methods: Hyperspectral reflectance images of South Korean and Chinese rice samples were obtained in the range of 400 nm to 1000 nm. Partial least square discriminant analysis (PLS-DA) models were developed and applied to the acquired images to determine the geographical origin of the rice samples. Results: The optimal pixel dimensions and spectral pretreatment conditions for the hyperspectral images were identified to improve the discrimination accuracy. The results revealed that the highest accuracy was achieved when the hyperspectral image's pixel dimension was $3.0mm{\times}3.0mm$. Furthermore, the geographical origin discrimination models achieved a discrimination accuracy of over 99.99% upon application of a first-order derivative, second-order derivative, maximum normalization, or baseline pretreatment. Conclusions: The results demonstrated that the VNIR hyperspectral imaging technique can be used to discriminate geographical origins of rice.

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References

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