A constituent-based preprocessing approach for characterising cartilage using NIR absorbance measurements
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AbstractNear-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.
CitationBrown, C. and Chen, M. (2016) 'A constituent-based preprocessing approach for characterising cartilage using NIR absorbance measurements', Biomedical Physics & Engineering Express, 2 (1):017002
PublisherIOP Publishing Ltd
JournalBiomedical Physics & Engineering Express