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 ). Classification accuracy using the Mahalanobis distance was between these groups.
Highlights
Osteoarthritis (OA) is a degenerative joint disease that carries substantial personal and societal burden in its later stages
The development of OA involves a changes across the whole joint, with the loss of structural integrity of articular cartilage a central factor in the early disease process
Spectroscopy-based techniques have been widely applied to characterise biological tissues, with Raman and mid-infrared spectroscopy commonly used [1]
Summary
Near-infrared spectroscopy is a widely adopted technique for characterising biological tissues. Any further distribution of this work must maintain present a second-derivative Beer’s law-based technique aimed at projecting spectral data onto a lower attribution to the dimension feature space characterised by the constituents of the target tissue type. This is intended as a author(s) and the title of the work, journal citation 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. Classification accuracy using the Mahalanobis distance was >98% between these groups
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