Wood is a renewable and valuable resource for a variety of end-use application areas. However, rapid and reliable assessments are needed to identify the quality of the tree, timber or wood product at all stages of production and processing. The ideal technology for assessing wood and wood products must provide reliable data, be user-friendly, cost-competitive and provide a rapid analysis. The ultimate application of near infrared (NIR) spectroscopy of wood or wood products is to substitute for costly and time-consuming reference measurements in order to aid process optimisation or determine properties and genetic traits on large numbers of individual samples. Increased interest in the application of NIR spectroscopy in various research fields including wood is observed nowadays. A vast number of publications highlight the potential of NIR spectroscopy for the characterisation of wood in a broad area of uses. The Journal of Near Infrared Spectroscopy has published two special issues on the application of NIR to forestry and wood research in 2010 and 2011 and a recent literature search yielded in excess of 556,000 results which can be easily found by using the search terms “NIR” and “wood”. This mass of published data may suggest that the technique of NIR spectroscopy is widely understood and broadly adopted by the timber industry, but even in recent papers it is evident that there is still a need to better understand the fundamental issues regarding sample selection and preparation, instrument choice, correct measurement and spectral interpretation. In this paper we draw on more than 40 years of collective experience and summarise state-of-the-art knowledge regarding instrumentation, spectral acquisition and data mining in regard to wood science and technology. The goal of this tutorial is two-fold: first, to inform early career wood scientists of the critical steps in utilising NIR spectroscopy to assess the quality of wood. Second, to alert managers to the level of operator skill required for the successful adoption of NIR technology. Some basic information is presented here, but due to the limited size of the manuscript, reference to more specific and detailed literature is provided in each section.

Pleating is an optimal way to increase bendability of wood used in diverse industrial applications. It results in the excessive buckling of cell walls and modifications of constitutive polymers. However, thoughtful understanding of the physical–chemical mechanisms of that modification process is very limited. The main purpose of the present study was to identify changes in functional groups of wood polymers induced by longitudinal compression. Four types of wood samples prepared from beech and sessile oak (untreated, steamed, longitudinally compressed and fixated for 1 min as well as longitudinally compressed and fixated for 18 h) were assessed by infrared spectroscopy. The spectra interpretation revealed that changes can be observed in hydroxyl as well as in carbon–oxygen single and carbon-hydrogen functional groups of polysaccharides and lignin. Beech wood seems to be more susceptible to investigated modification processes as compared to oak. Detailed interpretation of infrared spectra allows identification of changes in the hygroscopicity of wood as well as alterations in the linkage between structural elements in the polymer matrix of wood induced by the applied treatments.Graphic Abstract

Density functional theory (DFT) and molecular dynamics (MD) simulations were employed to investigate the interaction of cellulose and lignin with acetic anhydride for explaining the wood modification process. Cellulose was modelled with a cellobiose unit and dibenzodioxocin was used to represent the lignin model. Results obtained from both methods revealed that acetic anhydride interacted substantially more with the cellobiose model than the lignin model. The interaction energy of cellobiose–acetic anhydride was higher (about 20 kJ mol⁻¹) than that of lignin-acetic anhydride. DFT results on hydrogen bonding indicated that the hydroxyl group from cellobiose and the aromatic hydroxyl group from lignin models have similar energy values, which explain the equal strength of hydrogen bond interaction. The same trend was also obtained for the substitution of acetyl group in the hydroxyl group. MD results have also predicted that acetic anhydride forms a stronger interaction with cellobiose than with the lignin model, and these findings were in agreement with the DFT results.