Raman spectroscopic imaging is finding ever-increasing applications in the field of wood science for its ability to provide spatial and spectral information about the sample. The acquired Raman imaging data set makes it possible to determine the distribution of chemical components in various wood cell wall layers. However, the Raman imaging data set often contains thousands of spectra measured at hundreds or even thousands of individual frequencies, which leads to difficulties in accurately and quickly extracting all of the spectra within a specific morphological region of wood cell walls. To address this issue, a PCA-based method is proposed to identify Raman spectra of different cell wall layers. Several thousand spectra collected from poplar (hardwood) and Chinese fir (softwood) were classified into groups in accordance with distinct morphological regions, e.g. cell corner (CC), compound middle lamella (CML), secondary wall (SW), and cell lumen. This result allowed us to calculate corresponding average Raman spectra, which were more accurate and reproducible than those obtained by conventional approaches. By analyzing the relationship between numerous characteristic peaks in classified spectra, it was found that the Raman signal at 1331 cm<sup>-1</sup> was more related to lignin rather than cellulose once thought. Moreover, we present algorithms for background subtraction and peak intensity normalization on the basis of this method. Not only can this method provide a convenient and accurate procedure for identifying the spectra of different cell wall layers in a Raman imaging data set, but it also can bring new insights into studying the morphology and topochemistry of lignocellulosic cell walls.

PCA-based method for identifying spectra of different wood cell wall layers in Raman imaging data set and its applications

251st National Meeting (2016)

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