A constituent-based preprocessing approach for characterising cartilage using NIR absorbance measurements
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Brown_2016_Constitutent-based_ ...
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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):017002Publisher
IOP Publishing LtdJournal
Biomedical Physics & Engineering ExpressDOI
10.1088/2057-1976/2/1/017002Additional Links
http://stacks.iop.org/2057-1976/2/i=1/a=017002?key=crossref.a9c2b1246de3ca375bb8c10e13707e61Type
ArticleLanguage
enISSN
2057-1976ae974a485f413a2113503eed53cd6c53
10.1088/2057-1976/2/1/017002