Hyperspectral imaging for food applications

Stephen Marshall, Timothy Kelman, Tong Qiao, Paul Murray, Jaime Zabalza

Research output: Contribution to conferencePaperpeer-review

18 Citations (Scopus)
89 Downloads (Pure)


Food quality analysis is a key area where reliable, nondestructive and accurate measures are required. Hyperspectral imaging is a technology which meets all of these requirements but only if appropriate signal processing techniques are implemented. In this paper, a discussion of some of these state-of-the-art processing techniques is followed by an explanation of four different applications of hyperspectral imaging for food quality analysis: shelf life estimation of baked sponges; beef quality prediction; classification of Chinese tea leaves; and classification of rice grains. The first two of these topics investigate the use of hyperspectral imaging to produce an objective measure about the quality of the food sample. The final two studies are classification problems, where an unknown sample is assigned to one of a previously defined set of classes.
Original languageEnglish
Pages2854 - 2858
Number of pages5
Publication statusPublished - 1 Sep 2015
Event23rd European Signal Processing Conference, 2015 (EUSIPCO 2015) - Nice, France
Duration: 31 Aug 20154 Sep 2015


Conference23rd European Signal Processing Conference, 2015 (EUSIPCO 2015)
Abbreviated titleEUSIPCO 2015


  • signal processing
  • image processing
  • image classification
  • spectral imaging
  • hyperspectral imaging
  • food safety
  • feature extraction
  • covariance matrices


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