Abstract
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.
Highlights
Moisture is not an intrinsic characteristic of wood, it is among its most important properties, because its variation affects the behavior of the material during the industrial processing and application phases (Tsuchikawa and Schwanninger 2013)
The results showed that the roughness of the surface of the wood can influence in the statistics to estimate the properties of the wood from near infrared (NIR) spectroscopy
The longitudinal face through the integrating sphere presented the best model in the Partial Least Squares Regression (PLS-R) analysis, so it was divided into three ranges of moisture (0 % to 40 %, 40 % to 80 %, and > 80 % moisture) for to generate models capable of predicting wood moisture by classification using Partial Least Squares Discriminant Analysis (PLS-DA)
Summary
Moisture is not an intrinsic characteristic of wood, it is among its most important properties, because its variation affects the behavior of the material during the industrial processing and application phases (Tsuchikawa and Schwanninger 2013). The selection was performed according to the wood chips that presented better conditions on their surfaces for the acquisition of the spectra.
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