Chemical profiling of the plant cellwall through Raman microspectroscopy

This paper presents a computational framework for chemical profiling of the plant cell wall through the Raman spectroscopy. The system enables query of known spectral signatures and clustering of spectral data based on intrinsic properties. As a result, presence and relative concentration of specific chemical bonds can be quantified. The primary contribution of this paper is in representation of Raman profile in terms of fluorescence background and multiscale peak detection at each grid point (voxel). Such a representation allows efficient spatial segmentation based on the coupling between high-level salient properties and low-level symbolic representation at each voxel. The high-level salient properties refer to preferred peaks and their attributes for the entire image. The low-level symbolic representations are based on fluorescence background, spectral peak locations, and their attributes. We present results on a corn stover tissue section that is imaged through Raman microscopy, and the results are consistent with the literature. In addition, automatic clustering indicates several distinct layers of the cell walls with different spectral signatures.