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    Image Processing of Outer-Product matrices - a new way to classify samples: Examples using visible/NIR/MIR spectral data.

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    IPOPcorrBJSGDcorrBJSGD.doc
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    Author
    Jaillais, B.
    Morrin, V.
    Downey, Gerard
    Keyword
    infrared spectroscopy
    hierarchical classification ascendant
    food analysis
    chemometrics
    outer product analysis
    Date
    2007
    
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    URI
    http://hdl.handle.net/11019/57
    Citation
    B. Jaillais, V. Morrin, G. Downey. Image Processing of Outer-Product matrices - a new way to classify samples: Examples using visible/NIR/MIR spectral data. Chemometrics and Intelligent Laboratory Systems. 2007, 86, 179-188.
    Abstract
    A chemometric analysis has been developed to emphasise the discrimination power of spectroscopic techniques such as near infrared, mid-infrared and visible spectroscopy. The combination of two spectral domains using outer product analysis (OPA) leads to the calculation of an outer product (OP) matrix. The representation of this matrix is called the "analytical fingerprint" of the samples and their classification is performed in the following steps. First, two different techniques are tested by subtracting the images one-by-one and the sum of all the elements of the resulting difference matrix gives a scalar, characteristic of the distance between the two images. Combining chemical analysis with image processing techniques provides an original approach to study butters and margarines in relation to their fat content. Best results were obtained with the OP matrix built from NIR and visible signals following the use of city block distance and average linkage. Samples were arranged in four groups: 100 %, 82-75 %, 70-59 % and 38-25 % w/w fat. The cophenetic correlation coefficient (validity of the cluster information generated by the linkage function) associated with these spectral data has a value of 0.973. Similar results were obtained using Ward's algorithm which generated four groups and a cophenetic correlation coefficient equal to 0.959.
    ae974a485f413a2113503eed53cd6c53
    http://dx.doi.org/10.1016/j.chemolab.2006.06.014
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    Food Chemistry & Technology
    Food Chemistry & Technology
    Food Quality & Sensory Science

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