@ARTICLE\{IMM2013-06769,
    author       = "B. S. Dissing and O. S. Papandopoulou and C. Tassou and B. K. Ersb{\o}ll and J. Carstensen and E. Z. Panagou and G. Nychas",
    title        = "Using Multispectral Imaging for Spoilage Detection of Pork Meat",
    year         = "2013",
    keywords     = "Multispectral imaging, Meat spoilage, Chemometrics, Computational biology, Meat quality, Non-invasive methods, Converging technologies, Predictive modelling",
    pages        = "2268-2279",
    journal      = "Food and Bioprocess Technology",
    volume       = "6",
    editor       = "",
    number       = "9",
    publisher    = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/6769-full.html",
    abstract     = "The quality of stored minced pork meat was monitored using a rapid multispectral imaging device to quantify the degree of spoilage. Bacterial counts of a total of 155 meat samples stored for up to 580 h have been measured using conventional laboratory methods. Meat samples were maintained under two different storage conditions: aerobic and modified atmosphere packages as well as under different temperatures. Besides bacterial counts, a sensory panel has judged the spoilage degree of all meat samples into one of three classes. Results showed that the multispectral imaging device was able to classify 76.13 \% of the meat samples correctly according to the defined sensory scale. Furthermore, the multispectral camera device was able to predict total viable counts with a standard error of prediction of 7.47 \%. It is concluded that there is a good possibility that a setup like the one investigated will be successful for the detection of spoilage degree in minced pork meat."
}