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Please use this identifier to cite or link to this item: http://hdl.handle.net/11019/57

Title: Image Processing of Outer-Product matrices - a new way to classify samples: Examples using visible/NIR/MIR spectral data.
Authors: Jaillais, B.
Morrin, V.
Downey, Gerard
Keywords: infrared spectroscopy
hierarchical classification ascendant
food analysis
chemometrics
outer product analysis
Issue Date: 2007
Publisher: Elsevier
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.
Description: peer-reviewed
NOTICE: this is the author’s version of a work that was accepted for publication in Chemometrics and Intelligent Laboratory Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, volume 86(2), April 2007. DOI:10.1016/j.chemolab.2006.06.014
URI: http://hdl.handle.net/11019/57
http://www.sciencedirect.com/science/article/pii/S0169743906001420
ISSN: 0169-7439
Appears in Collections:Food Chemistry & Technology

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