Visualization of acetic anhydride flow and its heterogeneity within the wood block necessitates the development of a reliable and robust analytical method. Hyperspectral imaging has the potential to acquire a continuous spectrum of chemical analytes at different spectral channels in terms of pixels. The large set of chemical data (3-dimensional) can be expanded into relevant information in a multivariate fashion. We quantified gradients in acetylation degree over cross sections of Scots pine sapwood caused by a one-sided flow of acetic anhydride into wood blocks using near-infrared hyperspectral imaging. A principal component analysis (PCA) model was used to decompose the high-dimensional data into orthogonal components. Moreover, a partial least-squares (PLS) hyperspectral image regression model was developed to quantify heterogeneity in acetylation degree that was affected by the flow of acetic anhydride through wood blocks and into the tracheid cell walls. The model was validated and optimized with an external test data set and a prediction map using the root-mean-squared error of an individual predicted pixel. The model performance parameters are well suited, and prediction of the acetylation degree at the image level was complemented with confocal Raman imaging of selected areas on the microlevel. NIR image regression showed that the acetylation degree was determined not only by the time-dependent flow of the acetic anhydride through the wood macropores but also by the diffusion of the anhydride into the wood cell walls. Thereby, thin-walled earlywood sections were acetylated faster than the thick-walled latewood sections. Our results demonstrate the suitability of near-infrared imaging as a tool for quality control and process optimization at the industrial scale.
- confocal Raman imaging
- hyperspectral imaging
- partial least-squares regression
- surface modification