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Nondestructive Testing for Wheat Quality with Sensor Technology Based on Big Data.

Yan-Ge Tian1, Zheng-Nan Zhang2, Shuang-Qi Tian1

  • 1Henan University of Technology, High-tech Development Zone, Zhengzhou 450001, China.

Journal of Analytical Methods in Chemistry
|December 4, 2020
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Summary
This summary is machine-generated.

Nondestructive sensor technology offers a fast, low-cost method for analyzing wheat quality. Future research should focus on big data to enhance spectral data mining and model optimization for improved wheat analysis.

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Area of Science:

  • Agricultural Science
  • Analytical Chemistry
  • Sensor Technology

Background:

  • Nondestructive testing (NDT) using sensor technology is a rapidly advancing field for wheat quality assessment.
  • This technology integrates advancements from computer science, optics, mathematics, chemistry, and chemometrics.
  • Key advantages include simplicity, speed, low cost, environmental friendliness, and non-contact operation.

Purpose of the Study:

  • To review the application of NDT sensor technology in wheat quality analysis over the last decade.
  • To cover various techniques like hyperspectral, Raman, and near-infrared spectroscopy.
  • To assess the analysis of mechanical, storage, and physicochemical properties of wheat.

Main Methods:

  • Review of hyperspectral imaging technology.
  • Analysis of Raman spectroscopy applications.
  • Examination of near-infrared (NIR) spectroscopy techniques.
  • Inclusion of mechanical modeling approaches.

Main Results:

  • NDT sensor technologies effectively analyze wheat mechanical, storage, and physicochemical properties (moisture, ash, protein, starch).
  • Hyperspectral, Raman, and NIR techniques are prominent in wheat quality assessment.
  • The past decade has seen significant progress in applying these sensor technologies.

Conclusions:

  • Big data technology presents opportunities for spectral data mining and improved modeling algorithms.
  • Further research is needed in big data applications for robust wheat quality models.
  • Enhanced data support and methodological references are crucial for future NDT sensor applications in wheat quality research.