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A novel detection method for wheat aging based on the delayed luminescence.

Gong Yue-Hong1,2, Liu Yu-Kun3, Gong Zhi-le4

  • 1School of Software, Pingdingshan University, Pingdingshan, 467000, China.

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|January 11, 2024
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A new nondestructive method using delayed luminescence (DL) and a Walsh-coded bidirectional long short-term memory (Bi-LSTM) network accurately detects wheat aging. This green approach achieves 94% accuracy, offering a faster, sample-saving alternative to traditional methods.

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Area of Science:

  • Agricultural Science
  • Spectroscopy
  • Machine Learning

Background:

  • Assessing wheat aging is crucial for storage quality and processing.
  • Conventional chemical methods are slow and destructive.
  • Existing physical methods have limited practical application.

Purpose of the Study:

  • To develop a novel, nondestructive method for detecting wheat aging.
  • To utilize delayed luminescence (DL) signals for wheat quality assessment.
  • To improve the accuracy and efficiency of wheat aging detection.

Main Methods:

  • Fitting delayed luminescence (DL) signals with a hyperbolic function to extract four decay trend features.
  • Extracting an additional six features to form a comprehensive input vector.
  • Proposing a Walsh-coded bidirectional long short-term memory (Walsh-Bi-LSTM) network for enhanced error correction and detection.

Main Results:

  • The proposed Walsh-Bi-LSTM model achieved 94.00% accuracy on the testing dataset.
  • The model effectively utilizes DL signal features for aging detection.
  • The method demonstrates superior performance compared to conventional techniques.

Conclusions:

  • The developed delayed luminescence-based Walsh-Bi-LSTM model offers a green and nondestructive solution for timely wheat aging detection.
  • This approach significantly improves upon the limitations of traditional chemical and physical detection methods.
  • The model shows high accuracy and practical applicability for assessing storage wheat quality.