The natural durability of larch wood is described as being highly variable, ranging from non-durable to durable. In this study, FT-NIR spectroscopy was investigated for its ability to predict the natural durability of commercially available larch wood. Samples originated from approximately 60 European and 25 Siberian larch trees, provided by three Austrian-based wood industries. Natural durability tests were performed using the test fungi Gloeophyllum trabeum and Coniophora puteana. FT-NIR spectra were recorded and average spectra were calculated for calibration modelling. The models were strong [R² (CV)=70.7–95.19, RMSEP=0.063–0.026] and showed better suitability with spectra acquired from radial surfaces compared to those from axial surfaces. The prediction model was valid across larch species (European and Siberian), so that exact knowledge of the growth and site conditions was not required.

This study investigates the effect of the enzymatic polymerization of lignosulfonate for the formulation of a lignosulfonate-based adhesive. For this, beech lamellas were glued together and tested according to the EN 302-1 standard. The results showed that the laccase-polymerized lignosulfonate-based wood adhesives (LS-p) had similar mechanical properties as a standard carpenter’s glue (PVAc-based D3 class white glue), as no significant difference in tensile shear strength between these two adhesive types was found. However, carpenter’s glue showed almost 100% wood failure, while with the lignosulfonate-based wood glue, the samples failed, mainly in the glueline. Pre-polymerization of LS-p is the most critical factor to achieve the required viscosity, which is also connected to the wetting properties and the resulting tensile shear strength. The longer the pre-polymerization, the higher the viscosity of the LS-p adhesive, with the tensile shear strength reaching a plateau. The presented data show the potential of using enzymatically pre-polymerized lignosulfonate as a well-performing wood adhesive. Further development and optimization of the pre-polymerization process is required, which is also important to push towards upscaling and practical applications.