Hyperspectral Near-Infrared Image Assessment of Surface-Acetylated Solid Wood

Muhammad Awais*, Michael Altgen, Mikko Mäkelä, Daniela Altgen, Lauri Rautkari

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

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 nearinfrared 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, thinwalled 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.
Original languageEnglish
Pages (from-to)5223-5232
Number of pages10
JournalACS Applied Bio Materials
Volume3
Issue number8
DOIs
Publication statusE-pub ahead of print - 9 Jul 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • acetylation
  • hyperspectral imaging
  • surface modification
  • confocal Raman imaging
  • partial least-squares regression

Fingerprint Dive into the research topics of 'Hyperspectral Near-Infrared Image Assessment of Surface-Acetylated Solid Wood'. Together they form a unique fingerprint.

  • Projects

    CERES: Competence Center for the materials Bioeconomy: A Flagship for our Sustainable Future

    Mäkelä, K.

    01/05/201831/12/2022

    Project: Academy of Finland: Other research funding

    Cite this