Monitoring the change in colour of meat: A comparison between traditional and kernel based orthogonal transformations 
 Abstract  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 multispectral 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 kmeans 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 kernelbased versions of these have been applied to test which produce the most accurate categorisation.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 multispectral 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 kmeans 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 kernelbased versions of these have been applied to test which produce the most accurate categorisation.  Keywords  Multispectral imaging, categorisation, principal components analysis (PCA), minimum noise fraction (MNF) analysis, kernel based orthogonal transformations, kmeans clustering  Type  Journal paper [With referee]  Journal  Journal of Spectral Imaging  Year  2012 Month November Vol. 3 No. 1  ISBN / ISSN  DOI:10.1255/jsi.2012.a1  Electronic version(s)  [pdf]  BibTeX data  [bibtex]  IMM Group(s)  Image Analysis & Computer Graphics, Geoinformatics 
