Hyperspectral imaging is a powerful analytical technique that provides spatial and structural information of analytes into the substrate by transforming each pixel into a sampling cell. The large images set of information can be evaluated with large population statistics and multivariate data analysis. We report the spatial distribution of the confine solid wood surface modification in the tangential face of Scots pine samples using near-infrared hyperspectral imaging. Multivariate data analysis methods provide a robust distribution of acetic anhydride in wood samples. A principal component analysis (PCA) model was used to decompose the high-dimensional data into orthogonal components. Moreover, a partial least square discriminant analysis (PLS-DA) model has been developed for the classification of modified and unmodified wood. The model was optimized and validated with an external test dataset. The receiver operation characteristics (ROC) were compared at different latent variables. Confocal Raman spectroscopy was used to complement the acquired results. It was concluded that the acetic anhydride had higher accessibility in earlywood compared to the latewood. The PLS-DA model investigates spectral information and visualizes the degree of acetylation into the wood surface. It enhances the practical applicability of hyperspectral imaging (NIR) in the field of renewable materials and wood material sciences.

Hyperspectral near-infrared imaging assessment of confine surface anhydride modification of solid wood

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.