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

Publication: Research - peer-reviewJournal article – Annual report year: 2012

Documents

DOI

View graph of relations

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.
Original languageEnglish
JournalJournal of Spectral Imaging
Publication date2012
Volume3
Journal number1
ISSN2040-4565
DOIs
StatePublished
CitationsWeb of Science® Times Cited: No match on DOI

Keywords

  • Multi-spectral imaging, Categorisation, Principal components analysis (PCA), Minimum noise fraction (MNF) analysis, Kernel-based orthogonal transformations, k-means clustering
Download as:
Download as PDF
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
PDF
Download as HTML
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
HTML
Download as Word
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
Word

Download statistics

No data available

ID: 51205616