Wood-based products usually have serious limitations concerning contact with water, both because wood is a hygroscopic material and because the commonly used binder has low moisture resistance. This paper studies the effect of panel moisture content (MC) on the physico-mechanical properties of medium density fiberboards (MDF). Several commercial MDF boards produced in Europe were stored at room temperature and relative humidity (RH) for 9 weeks (approx. range 15–20 °C and 50–85% RH). Every week, a strip of each MDF board was cut out, divided into 5 × 5 cm test pieces and its internal bond strength (IB) was measured. A strong influence of MDF moisture content on internal bond strength was observed and therefore IB test pieces were stored in a climatic chamber (either at 20 °C, 55% RH and at 20 °C, 70% RH). A decreasing linear relation was established between IB and MC. It was found that this effect is reversible: after drying, internal bond strength rises again (following a slight hysteresis). This work reinforces the importance of conditioned storage before board properties analysis, as described in European Standard EN 319.

Amino resins are produced by two main processes: the strong acid process and the alkaline-acid process. Both use formaldehyde and a base (e.g. sodium hydroxide) in their formulation. In this work, Forward Interval Partial Least Squares methodology was applied to create prediction models for the determination of the concentration of formaldehyde and residual methanol (that is present in the formaldehyde solution) used in the production of amino resins. Near infrared (NIR) spectra were acquired at two different temperatures: 18 and 35°C. A Partial Least Squares calibration models were established with the measured values from reference methods: namely, sodium sulfite (formaldehyde) and gas chromatography (methanol). The performances of the best models were compared using the root mean square error of cross validation (RMSECV) and coefficient of determination for prediction (r²). The best results obtained a r² above 0.994. The RMSECV values obtained were 0.063% (m/m) and 0.031% (m/m) for the formaldehyde and methanol concentration, respectively. External validation was performed using different formaldehyde solution samples. The NIR methodology presented in this work proved to be effective and enables a significant time reduction, when compared to the reference methods, in the measurement of formaldehyde and methanol concentrations.

To assure the quality control of industrial processes, it is important to adopt reproducible and efficient methodologies. Spectroscopic methods, such as near infrared (NIR), are a good option as they are fast and may be used to indirectly estimate multiple physicochemical properties. In this study, NIR spectra of key feedstock samples used in the production of formaldehyde-based resin and wood-based panels, namely urea, ammonium sulfate, ammonium nitrate, sodium hydroxide, and acetic acid, were acquired. Multivariate data analysis was applied to establish the correlation between the spectra and the properties being measured. Quantitative models were then created using partial least squares regression to predict the concentrations of feedstock samples. This study presents quantitative models that were created by combining spectra measured on two probes, which achieved similar prediction results as single-probe based models. The performances of the best models were compared with the reference methods for each of the evaluated samples. For the samples under study, the proposed approach is suitable for routine analysis across multiple equipment configurations using the same quantitative model. NIR spectroscopy combined with chemometric models could be a valuable complement to support in-line raw material monitoring and plant digitalization in the wood panels industry.