JOURNAL OF LIGHT INDUSTRY

CN 41-1437/TS  ISSN 2096-1553

Volume 39 Issue 5
October 2024
Article Contents
LI Yankun, ZHANG Wei and LIU Yanling. Research and application progress of data fusion strategy in authenticity identification of edible oil[J]. Journal of Light Industry, 2024, 39(5): 50-59. doi: 10.12187/2024.05.006
Citation: LI Yankun, ZHANG Wei and LIU Yanling. Research and application progress of data fusion strategy in authenticity identification of edible oil[J]. Journal of Light Industry, 2024, 39(5): 50-59. doi: 10.12187/2024.05.006 shu

Research and application progress of data fusion strategy in authenticity identification of edible oil

  • Received Date: 2023-11-15
    Accepted Date: 2024-01-18
    Available Online: 2024-10-15
  • An overview of data fusion strategies based on spectroscopy, mass spectrometry, chromatography and other detection technologies and their current research and application in authenticity identification of edible oils was presented, pointing out that: at present, detection technologies widely used for authenticity identification of edible oils including spectroscopy, chromatography, mass spectrometry and electronic sensors. However, a single detection technique often focused only on a specific data or index, which could not fully eliminate the superposition effect, baseline drift and noise when the ingredients contained in edible oils were more complex. Data fusion strategies were categorized into three types: data layer fusion, feature layer fusion and decision layer fusion. Combined with chemometrics methods, the data obtained by different detection technologies could be integrated to obtain and extract richer data feature information, thus improving the authenticity identification of edible oils. Data fusion between various novel detection technologies, or between new and traditional spectroscopy, mass spectrometry, chromatography and other detection technologies, which could quickly and accurately achieved the identification of adulteration of edible oils, variety classification and origin traceability. In the future, in-depth research could be carried out on the improvement of the existing analytical methods, the development of new fusion algorithms combined with deep learning algorithms, and the introduction of cloud computing to improve real-time edible oil identification, so as to promote the development of data fusion strategy in the field of edible oil authenticity identification.
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