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Deep Fluorescence Observation in Rice Shoots via Clearing Technology
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Rice origin traceability using mid-infrared and fluorescence spectral data fusion.

Changming Li1,2, Yong Tan1, Chunyu Liu1

  • 1School of Physics, Changchun University of Science and Technology, Changchun, China.

Frontiers in Plant Science
|December 5, 2025
PubMed
Summary

This study developed an intelligent system using combined mid-infrared and fluorescence spectroscopy with machine learning to accurately determine rice geographic origin. The advanced feature fusion technique achieved 95.55% accuracy, improving agricultural product traceability.

Keywords:
data fusiondata preprocessingmachine learningorigin discriminationspectrometry

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Area of Science:

  • Agricultural Science
  • Analytical Chemistry
  • Machine Learning

Background:

  • Traditional single-spectroscopy methods have limitations in accurately identifying the geographic origin of agricultural products like rice.
  • Distinguishing rice varieties based on geographic origin is crucial for quality control and preventing food fraud.
  • Integrating multiple spectral data types offers complementary information for enhanced discrimination.

Purpose of the Study:

  • To develop an intelligent discrimination system for rice geographic origin by fusing mid-infrared (MIR) and fluorescence (FLU) spectral data.
  • To enhance spectral feature extraction and selection using machine learning algorithms.
  • To evaluate the performance of different data fusion strategies and machine learning models for accurate origin identification.

Main Methods:

  • Acquisition of Fourier transform infrared (FTIR) and fluorescence spectral data from the 'Zhongke Fa 5' rice variety across eight production regions.
  • Application of a 'Normalization-Smoothing-Multiplicative Scatter Correction' preprocessing framework to improve spectral quality.
  • Utilized the successive projections algorithm (SPA) for feature extraction and logistic regression (LR) for classification, comparing with Support Vector Machine (SVM) and gradient boosting.

Main Results:

  • The proposed preprocessing framework significantly improved spectral signal-to-noise ratio and feature separability.
  • Feature-level fusion optimized by SPA achieved a high test set accuracy of 95.55%.
  • The logistic regression model combined with SPA-selected features (LR-SPA) demonstrated superior precision (93.05%) and robustness compared to SVM and gradient boosting.

Conclusions:

  • The integration of MIR and FLU spectral data with machine learning provides a powerful and accurate method for determining rice geographic origin.
  • Feature-level fusion and the LR-SPA model offer significant advantages in discrimination accuracy and robustness.
  • This intelligent system presents a revolutionary approach for agricultural product quality and safety supervision with substantial practical value.