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    A constituent-based preprocessing approach for characterising cartilage using NIR absorbance measurements

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    Authors
    Brown, Cameron P.
    Chen, Minsi
    Affiliation
    University of Oxford
    University of Derby
    Issue Date
    2016-01-18
    
    Metadata
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    Abstract
    Near-infrared spectroscopy is a widely adopted technique for characterising biological tissues. The high dimensionality of spectral data, however, presents a major challenge for analysis. Here, we present a second-derivative Beer's law-based technique aimed at projecting spectral data onto a lower dimension feature space characterised by the constituents of the target tissue type. This is intended as a preprocessing step to provide a physically-based, low dimensionality input to predictive models. Testing the proposed technique on an experimental set of 145 bovine cartilage samples before and after enzymatic degradation, produced a clear visual separation between the normal and degraded groups. Reduced proteoglycan and collagen concentrations, and increased water concentrations were predicted by simple linear fitting following degradation (all $p\ll 0.05$). Classification accuracy using the Mahalanobis distance was $\gt 98\%$ between these groups.
    Citation
    Brown, C. and Chen, M. (2016) 'A constituent-based preprocessing approach for characterising cartilage using NIR absorbance measurements', Biomedical Physics & Engineering Express, 2 (1):017002
    Publisher
    IOP Publishing Ltd
    Journal
    Biomedical Physics & Engineering Express
    URI
    http://hdl.handle.net/10545/620880
    DOI
    10.1088/2057-1976/2/1/017002
    Additional Links
    http://stacks.iop.org/2057-1976/2/i=1/a=017002?key=crossref.a9c2b1246de3ca375bb8c10e13707e61
    Type
    Article
    Language
    en
    ISSN
    2057-1976
    ae974a485f413a2113503eed53cd6c53
    10.1088/2057-1976/2/1/017002
    Scopus Count
    Collections
    Department of Electronics, Computing & Maths

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