Predictive models were developed for estimating wood density from NIR spectra. Averaged wood density by trees were associated with NIR spectra measured in the wood of breast height. More reliable predictions were obtained using mean values per clone in calibration set. The best model for predicting wood density presented R²cv of 0.77 and RMSEcv of 15 kg. m-³

The chemical composition of wood is important to assess the quality of this raw material for the industry of cellulosic pulp production. The purpose of this work was to determine the chemical composition of Eucalyptus spp. grown for cellulosic pulp production. Ten Eucalyptus spp. clones with six years of age, located in the municipality of Itamarandiba, Minas Gerais, Brazil, were used. Quantification was obtained for extractives, monosaccharides, uronic acids, acetates, lignin, ash and the phenolic composition of the extracts. In average, clones showed around 2.7% extractives, with a predominance of polar compounds soluble in ethanol and water; 27.7% lignin and 0.3% ash. Glucose was the main sugar detected (64.2%), followed by xylose (19.3%). The main components of the extractives were steroids, fatty acids and aromatic acids, followed by smaller amounts of substituted alkanoic acids, fatty alcohols, glycerol derivatives and triterpenes. The ethanol–water extracts presented total phenol contents ranging from 321.4 to 586.6 mg EAG/g of extract, tannins from 28.1 to 65.1 mg catechin/g of extract and flavonoids from 73.6 to 256.9 mg catechin/g of extract. Clones with a higher holocellulose amount and a lower lignin content showed a higher potential for cellulosic pulp production. These findings are important for the development of high-quality wood based on important traits for the pulp and paper sector.

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.