Method for automatically identifying spectra of different wood cell wall layers in Raman imaging data set.

The technique of 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. On the basis of the acquired Raman imaging data set, it is 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 results in difficulties accurately and quickly extracting all of the spectra within a specific morphological region of wood cell walls. To address this issue, the authors propose a new method to automatically identify Raman spectra of different cell wall layers on the basis of principal component analysis (PCA) and cluster analysis. A Raman imaging data set collected from a 55.5 μm × 47.5 μm cross-section of poplar tension wood was analyzed. Several thousand spectra were successfully classified into five groups in accordance with different morphological regions, namely, cell corner (CC), compound middle lamella (CML), secondary wall (SW), gelatinous layer (G-layer), and cell lumen. Their corresponding average spectra were also calculated. In addition, the relationship between different characteristic peaks in the obtained Raman spectra was estimated and it was found that the peak at 1331 cm(-1) is more related to lignin rather than cellulose. Not only can this novel 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 in wood cell walls.