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Detection of Rice Prolamin and Glutelin Content Using Hyperspectral Imaging Combined with Feature Selection

Chu Zhang1, Zhongjie Tang1, Xiaojing Tan2

  • 1School of Information Engineering, Huzhou University, Huzhou 313000, China.

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Hyperspectral imaging accurately detects rice prolamin and glutelin content using advanced regression models and feature selection. This non-destructive method aids in precise rice quality assessment.

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deep learningfeature selectionglutelinhyperspectral imagingprolamin

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Area of Science:

  • Agricultural Science
  • Analytical Chemistry
  • Spectroscopy

Background:

  • Prolamin and glutelin are key rice protein components.
  • Accurate rice quality assessment relies on protein content analysis.
  • Rapid, non-destructive methods are needed for protein detection.

Purpose of the Study:

  • To develop a hyperspectral imaging method for detecting rice prolamin and glutelin content.
  • To compare the performance of different regression models and feature selection techniques.
  • To establish a reliable method for rice protein analysis.

Main Methods:

  • Hyperspectral imaging was utilized for data acquisition.
  • Feature wavelength selection was performed using SPA, CARS, and CNN-based GradCAM++.
  • Regression models including PLSR, SVR, BPNN, and CNN were employed.
  • Model performance was evaluated using full spectra and selected feature wavelengths.

Main Results:

  • Back-propagation neural network (BPNN) models demonstrated superior prediction performance for both prolamin and glutelin.
  • Optimal BPNN models achieved a correlation coefficient (r) exceeding 0.8 for both proteins.
  • The study compared the effectiveness of feature wavelengths versus full spectra in predictive models.
  • GradCAM++ was used to optimize feature wavelength selection by comparing different threshold values.

Conclusions:

  • Hyperspectral imaging combined with multivariate data analysis is a feasible approach for predicting rice prolamin and glutelin content.
  • The developed methodology offers a valuable reference for detecting various rice protein types.
  • This non-destructive technique enhances the accuracy of rice quality assessment.