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Study on Rice Origin and Quality Identification Based on Fluorescence Spectral Features

Author

Listed:
  • Yixin Qiu

    (School of Physics, Changchun University of Science and Technology, Changchun 130022, China)

  • Yong Tan

    (School of Physics, Changchun University of Science and Technology, Changchun 130022, China)

  • Yingying Zhou

    (School of Physics, Changchun University of Science and Technology, Changchun 130022, China)

  • Zhipeng Li

    (School of Physics, Changchun University of Science and Technology, Changchun 130022, China)

  • Zhuang Miao

    (School of Physics, Changchun University of Science and Technology, Changchun 130022, China)

  • Changming Li

    (School of Physics, Changchun University of Science and Technology, Changchun 130022, China)

  • Xitian Mei

    (School of Physics, Changchun University of Science and Technology, Changchun 130022, China)

  • Chunyu Liu

    (School of Physics, Changchun University of Science and Technology, Changchun 130022, China)

  • Xing Teng

    (Jilin Academy of Agricultural Sciences (Northeast Agricultural Research Center of China), Changchun 130000, China)

Abstract

The origin of agricultural products significantly influences their quality and safety. Fluorescence spectroscopy was used to analyse Japonica rice 830, grown in different areas of Jilin Province, by examining rice seed, brown rice, and rice flour from 12 origins. Fluorescence spectra were pre-processed through normalisation and smoothing to remove noise. These processed spectra were input into decision trees, support vector machines (SVMs), K-nearest neighbour (KNN), and neural network models for classification. The analysis revealed that the combined four models achieved an average classification accuracy of 98.05% with a computation time of 180 s, while the reduced-scale models improved accuracy to 98.36% and reduced computation time to 11.25 s. Additionally, prediction models using standard rice starch content values across different states achieved R² values over 0.8. This method provides a rapid, precise approach for assessing rice quality and origin, demonstrating significant potential for application in rice analysis.

Suggested Citation

  • Yixin Qiu & Yong Tan & Yingying Zhou & Zhipeng Li & Zhuang Miao & Changming Li & Xitian Mei & Chunyu Liu & Xing Teng, 2024. "Study on Rice Origin and Quality Identification Based on Fluorescence Spectral Features," Agriculture, MDPI, vol. 14(10), pages 1-17, October.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:10:p:1763-:d:1492940
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