ABSTRACT The objective of this study was to establish multivariate models for the prediction of wood basic density with reference to the values of average density of trees and near infrared (NIR) spectra measured in the breast height. The wood basic density of 39 Eucalyptus clones was determined in the laboratory by means of the mean longitudinal positions of 0%, 2%, 10%, 30%, 50% and 75% of the commercial height of the tree by the gravimetric method. NIR spectra were recorded using a spectrometer using optical fiber probe and integrating sphere directly on the transverse plane of the solid wood in disks collected from diameter at breast height and later in the sawdust. The performance of the NIR based models was evaluated according to the spectral acquisition method and sample preparation. The results showed that the best model for basic density estimation using indirect measurements was developed from the average spectra per clone measured in solid wood disks (R2cv of 0.77 and RMSEcv of 15 kg.m-³).

ABSTRACT The objective of this study was to evaluate the influence of particle size of charcoal samples on the predictive model statistics of charcoal chemical composition based on the NIR spectroscopy. Spectra of Acacia and of Eucalyptus charcoal were collected in the 100, 60 and 40 mesh granulometry, besides the powder remaining at the bottom of the sieves sets. They were subjected to principal component analysis and partial least square regression in order to estimate of volatile material (VMC), ash (AC) and fixed carbon content (FCC) values. The estimation of the FCC, VMC and AC of Eucalyptus based on NIR was more accurate using spectra of lower-particle-size powder. The models for Acacia charcoal were better using spectra measured at 40 mesh to predict FCC, 100 mesh for AC, and smaller size for VMC. NIR spectroscopy was efficient in estimating the immediate chemical composition of charcoal, except for AC.