Journal of Spectral Imaging,   Volume 3   Article ID a1   (2012)

Peer reviewed Paper

Monitoring the change in colour of meat: A comparison of traditional and kernel-based orthogonal transformations

  • Asger Nyman Christiansen  
  • Jens Michael Carstensen
  • Flemming Møller
  • Allan Aasbjerg Nielsen
Department of Informatics and Mathematical Modelling, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark

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DuPont Nutrition Biosciences ApS, Physical Food Science, Edwin Rahrs vej 38, 8220 Brabrand, Denmark

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National Space Institute, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark

 https://orcid.org/0000-0002-4837-9449
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 Corresponding Author
Department of Informatics and Mathematical Modelling, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
[email protected]
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Currently, no objective method exists for estimating the rate of change in the colour of meat. Consequently, the purpose of this work is to develop a procedure capable of monitoring the change in colour of meat over time, environment and ingredients. This provides a useful tool to determine which storage environments and ingredients a manufacturer should add to meat to reduce the rate of change in colour. The procedure consists of taking multi-spectral images of a piece of meat as a function of time, clustering the pixels of these images into categories, including several types of meat, and extracting colour information from each category. The focus has primarily been on achieving an accurate categorisation since this is crucial to develop a useful method. The categorisation is done by applying an orthogonal transformation followed by k-means clustering. The purpose of the orthogonal transformation is to reduce the noise and amount of data while enhancing the difference between the categories. The orthogonal transformations principal components analysis, minimum noise fraction analysis and kernel-based versions of these have been applied to test which produce the most accurate categorisation.

Keywords: multi-spectral imaging, categorisation, principal components analysis (PCA), minimum noise fraction (MNF) analysis, kernel-based orthogonal transformations, k-means clustering

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