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0 presente trabalho teve como objetivo obter modelos de predição da densidade básica da madeira de árvores de Eucalyptus grandis, plantadas em espaçamento de 3 x 2m, fertilizadas com K e Na, submetidas ao estresse hidrico. Foram selecionadas 54 e 48 árvores, com 5 e 6 anos, respectivamente, cortando seções transversais do lenho na base e DAP do seu tronco. Foram analisadas a seção transversal de 1.222 corpos de prova de madeira, no espectrômetro NIR MPA da Bruker, nos módulos (i) esfera de integração (12800-3600cm·1) e (ii) sonda de fibra óptica (12800-4000cm-l ), comn resolução espectral 8 cm·1, media de 32 leituras. Posteriormente, foi determinada a densidade básica dos corpos de prova pelo método do máximo teor de umidade. Os dados foram tratados no software The Unscrambler®, sendo simuladas regressões com minimos quadrados parciais (PLS), baseadas nas informaões espectrais e na densidade básica, considerando 6 pré-tratamentos aplicados aos dados espectrais de validav cruzada e independente. Dos 36 modelos, os que apresentam os pârametros mais adequados segundo a literatura foram (i) esfera de integração: pré-tratamento Derivative 1 ;R'cv:0,69;RMSECV: 0,021 g.cm-3, LV: 9; R'p: 0,68; RMSEP: 0,022 g.cm-3, RPD: 1,76; (ii) fibra óptica: sem pré-tratamento;R'cv:0,65;RMSECV: 0,022 g.cm-3, LV: 5; R'p: 0,61; RMSEP: 0,024 g.cm-3, RPD: 1,62. Demonstrou-se, desta forma, que as regressões PLS que correlacionam as informações espectrais e a densidade básica da madeira de E. grandis, apresentam estatisticas satisfatórias, com coeficientes de correlação medios, baixos valores de erro padrão da predição, e RPD próximos a 2.

Wood basic density (BD) is mainly controlled by the ratio of fibre and the cell-wall thickness which is related to pulp yield and most physical and mechanical properties influencing wood end-using. Accordingly, BD is an important criterion for selection in tree breeding program which requires screening of large number of trees. Associated to non-destructive sampling as cores, NIRS can be used to estimate the BD. In this objective, we built global NIRS models for basic density by using Eucalyptus multispecies datasets, issued of Brazilian wood samples representing different end-using purposes (pulp and paper, timber, charcoal, pole). We tested moisture content (MC) variability during the NIRS measurement to incorporate this parameter in the model, and built plastic NIRS model. We collected wood disks on 8 to 25 year-old Corymbia maculata, E. grandis, E. resinifera, and E. cloeziana trees (10 per species). Diametric bands cut from the disks were divided in 15 x 20 x 20 mm samples. They were theoretically stabilized at 8, 10, 12, 14, 16, and 18% of MC. At each MC, we measured spectra on the same sanded longitudinal face. Spectra were measured under diffuse reflection using integration sphere of a Bruker MPA spectrometer, each spectrum constituted of 32 scans. Spectral analysis was performed within the 9,000–3,500 cm-1 range at 8 cm-1 resolution. BD, the mass of oven-dry wood per unit of volume of green wood, is expressed in grams per cubic centimeter. Corrected MC was calculated by taking into account the dry weight data. We used Unscrambler software 10.3 (Camo, Norway) for PLS regression of BD at different MC, for all species or separately. Cross-validations with 5 groups of random samples were performed to compare models. Thanks to the low number of group-validation, the RMSECV and RMSEP were very close. Here, the RPD is the ratio between the standard deviation and RMSECV. The BD range was comprised between 0.390 and 0.898 g/cm3. The corrected MC d varied from 9 to 22 %, showing bias with the theoretical MC, according to the species. Global models including the 4 species and MC variability, showed RMSECV = 0.041 g/cm3 and RPD = 3.86. As expected, models developed with spectra of theoretical MC were less efficient than the model developed with spectra selected at corrected MC. For example at 12 % for MC, values of RMSECV and RPD for multispecies model were respectively 0.040 g/cm3 and 3.94 for theoretical MC, and 0.035 and 5.07 for corrected MC. For the BD model of E. grandis, considered separately because of the lowest BD compared to the other species, we obtained RMSECV and RPD of 0.013 g/cm3 and 4.08 including all MC. The values obtained with the other three species were 0.015 g/cm and 3.46. The “local” models were generally more reliable than the global model in term of RMSECV, even if the last showed a higher RPD. It is the same principle and results than for the local model procedure for which the regression is based on the most similar spectral neighbors. (Texte intégral)

Introduction: MC (moisture content) of wood is crucial for the transformation sector. MC vary widely because of its highly anisotropic characteristics which has a direct influence on physical and mechanical properties, both as a raw material and when utilized in various forest products as lumber, paper, charcoal and composite panels. The MC determination can be estimated by NIRS associated to non-destructive sampling. Hence, with this objective, we built global NIRS models for MC by using Eucalyptus multispecies datasets from Brazil. We tested MC variability and NIRS to build robust MC model. Experience: We collected wood disks on 8 to 25 year-old Corymbia maculata, Eeucalyptus grandis, E. resinifera, and E. cloeziana trees (10 per species). Diametric bands cut from the disks were divided in 15 x 20 x 20 mm samples. They were theoretically stabilized at 8, 10, 12, 14, 16, and 18% of MC. At each MC, we measured spectra on the same sanded longitudinal face and their weight. Spectra were measured under diffuse reflection using integration sphere of a Bruker MPA spectrometer, each spectrum constituted of 32 scans. Spectral analysis was performed within the 9,000–3,500 cm-1 range at 8 cm-1 resolution. The mass of oven-dry wood per unit of volume of green wood is expressed in grams per cubic centimeter. Real MC was calculated by taking into account the dry weight data. We used Unscrambler software 10.3 (Camo, Norway) for PLS regression for MC, for all species or separately. Cross-validations with 5 groups of random samples were performed to build the models for each species and for the global model. Then, we used test set validations randomly selected along the MC variability and compare model performances. Finally, we compared on validation test set for the 4 species, the predicted values both by specific models and global model. Results and Discussions: The real MC varied from 9 to 22%, showing bias with the expected MC, according to the species. Global models for MC including the 4 species, showed RPD = 3.3 with SEP value of 0.7%. Depending the species, the specific models showed a RPD from 3.5 to 4.3 with lower SEP from 0.4 to 0.5%. As expected, models developed with spectra of theoretical MC were less efficient than the model developed with spectra selected at corrected MC. The local models (or specific models) were more reliable in terms of prediction error than the global model in term of error of prediction because the local method appears to cope with the non-linearity and non-homogeneity associated with a large multispecies dataset. This is consistent with approaches developed with local models. The comparison between predicted values by local and global models showed r²from 0.89 to 0.98 with standard error 0.3 to 0.6%. (Texte intégral)