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Moisture is one of the most important wood properties because its variation directly influences the material strength and density. Thus, the aim of this study was to develop near infrared (NIR) spe...

Background: Near infrared (NIR) spectroscopy has been successfully applied to estimate the chemical, physical and mechanical properties of various biological materials, including wood. This study aimed to evaluate basic density calibrations based on NIR spectra collected from three wood faces and subject to different mathematical treatments. Methods: Diffuse reflectance NIR spectra were recorded using an integrating sphere on the transverse, radial and tangential surfaces of 278 wood specimens of Eucalyptus urophylla x Eucalyptus grandis. Basic density of the wood specimens was determined in the laboratory by the immersion method and correlated with NIR spectra by Partial Least Squares regression. Different statistical treatments were then applied to the data, including Standard Normal Variate, Multiplicative Scatter Correction, First and Second Derivatives, Normalization, Autoscale and MeanCenter transformations. Results: The predictive model based on NIR spectra measured on the transverse surface performed the best (R²cv = 0.85 and RMSE = 25.5 kg/m³) while the model developed from the NIR spectra measured on the tangential surface had the poorest performance (R²cv = 0.53 and RMSE = 46.8 kg/m³). The difference in performance between models based on original (untreated) and mathematically-treated spectra was minimal. Conclusions: Multivariate models fitted to NIR spectra were found to be efficient for predicting the basic density of Eucalyptus wood, especially when based on spectra measured on the transversal surface. For this data set, models based on the original spectra and mathematically treated spectra had similar performance. The reported findings show that mathematical transformations are not always able to extract more information from the spectra in the NIR.

Wood density is an important criterion for material classification, as it is directly related to quality of wood for structural use. Several studies have shown promising results for the estimation of wood density by near infrared spectroscopy. However, the optimal conditions for spectral acquisition need to be investigated in order to develop predictive models and to understand how anisotropy and surface roughness affect the statistics of predictive partial least square regression models. The aim of this study was to evaluate how the spectral acquisition technique, wood surface, and the surface quality influence the ability of partial least square–based models to estimate wood density. Near infrared spectra were recorded using an integrating sphere and fiber-optic probe on the tangential, radial, and transverse surfaces machined by circular and band saws in 278 wood specimens of six-year-old Eucalyptus hybrids. The basic density values determined by the conventional method were then correlated with near infrared spectra acquired using an integrating sphere and fiber-optic probe on the wood surfaces by means of partial least square regressions. The most promising models for predicting wood density were generated from near infrared spectra obtained from the transverse surface machined by the bandsaw, via an integrating sphere ([Formula: see text], RMSEP = 23 kg m−3 and RPD = 3.0) as well as for the optic fiber ([Formula: see text], RMSEP = 35 kg m−3 and RPD = 2.1). Surface quality affected the spectral information and robustness of predictive models with a rougher surface, caused by band sawing, showing better results.

In order to assess the moisture content of wood chips on an industrial scale, readily applicable techniques are required. Thus, near infrared (NIR) spectroscopy was used to estimate moisture in wood chips by means of partial least squares regressions. NIR spectra were obtained in spectrometer with an integrating sphere and optical fiber probe, on the longitudinal and transverse surface of Eucalyptus wood chips. The specimens had their masses and NIR spectra measured in 10 steps during drying from saturated to anhydrous condition. Principal Component Analysis was performed to explore the effect of moisture of wood chip on NIR signatures. The values of moisture content of chips were associated with the respective NIR spectra by Partial Least Squares Regression (PLS-R) and Partial Least Squares Discriminant Analysis (PLS-DA) to estimate the moisture content of wood chips and its moisture classes, respectively. Model developed from spectra recorded on the longitudinal face by the integrating sphere method presented statistics slightly better (R²cv = 0,96; RMSEcv = 7,15 %) than model based on optical fiber probe (R²cv = 0,90; RMSEcv = 11,86 %). This study suggests that for calibration of robust predictive model for estimating moisture content in chips the spectra should be recorded on the longitudinal surface of wood using the integrating sphere acquisition method.

The aim of this study was to evaluate the structural and compositional changes in eucalyptus wood chips subjected to dry torrefaction. The experiment was conducted in a semi-continuous screw reactor with indirect heating system. Wood chips samples of three initial moisture contents (0, 15 and 30% dry basis) were torrefied up to three final temperatures (220, 260 and 300 °C) and at three heating times (10, 15 and 20 min). The effect of these variables was evaluated through the analysis of chemical composition, thermogravimetry and mechanical durability of the torrefied wood chips. The increase at temperature and time of torrefaction, as well as the drop in the initial moisture content, promoted significant structural and compositional changes in the wood chips. The torrefied samples present a higher resistance to thermal degradation, more friable structure, higher lignin and lower polysaccharides contents than the untreated samples. These changes made it possible to increase the quality of the eucalyptus wood chips to be use as a solid fuel.

The combination of pelletization and torrefaction allows for obtaining pellets with higher energetic density. Thus, biomass becomes more attractive and usable for energetic purposes, and therefore, knowing the chemical properties becomes important to guarantee the quality of the torrefied pellets for fuel. The objective of this study was to evaluate the effect of torrefaction of Pinus sp. pellets on their chemical properties. The wood was previously transformed into particles and dried to 16% moisture content for further pelleting. For the torrefaction, the pellets were dried to 0% moisture content and subjected to heat treatment in a screw type reactor at the temperatures of 210, 250, and 290 °C and the times of 10, 20, and 30 minutes. In order to determine the chemical properties, the structural chemical composition, immediate chemical composition, elemental composition, and thermogravimetric analysis were performed. The torrefaction promoted significant changes in the chemical properties of Pinus sp. pellets, especially regarding the proportional increase of carbon content in detriment to oxygen reduction, increase of the thermal stability, and proportional increase of the lignin content by the reduction of the hemicelluloses. This improves the energetic quality of the biomass and turns it into a more competitive fuel when compared to fossil fuels.