Gilles Chaix1,2, Sophie Nourissier3, Armelle Soutiras1, Mario Tomazello2, Samara Dilio Franzol2, Lucas Sene Oste2,
Mariana Pires Franco2, Andriambelo Radonirina Razafimahatratra4, Garel Chrissy Ekomono Makouanzi5,
Tahiana Ramananantoandro4, José Carlos Rodrigues6, Zo Elia Mevanarivo4, Anne Clément-Vidal1
Materiel and methods
Results and discussions
•
More than 3,000 wood samples were collected for different eucalyptus species and hybrids from different ages of plantation (5-30 years old), and locations (Congo, Senegal, Madagascar, Brazil). NIR spectra of grounded samples stabilized at 12% of moisture content were measured in the diffuse reflectance mode with a Bruker spectrometer. More than 200 samples well-representing species, age diversity, and location were selected and Mahalanobis distance based on spectral data was used for largest sample set. Samples were subjected to extraction in a soxhlet apparatus with ethanol and then water to deduce extractive content. The Klason lignin, acid soluble lignin, alphacellulose and holocellulose contents were determined by adapted Tappy standard methods or by pyrolysis analysis for lignin and SG ratio depending of samples. The basic density and moisture content were measured on solid samples from Brazilian samples according to standard methods. Partial Least Square regressions were done and the models were tested by cross-validation with few groups (4-6).Contact: gilles.chaix@cirad.fr
Conception: Cirad,
Martine Duportal,,
March 2016
- © photo: G. Chaix
Eucalyptus wood phenotyping by Near Infrared
Spectroscopy for chemical compounds,
basic density and moisture content
Wood phenotyping by NIR spectroscopy is particularly well-suited for breeding
programmes where huge numbers of samples must be analysed or to screen
unknown sample sets before wet chemistry analysis. Our objective here was to
develop eucalyptus multispecies NIR calibrations for wood properties.
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Based on “Standard Error of Prediction” and “Ratio Performance Deviation” criteria our NIR calibrations showed good fits for all properties especially extractives, lignin, basic density, moisture content. According to William (2014), most of the model were classified from fair to excellent.•
Even if monospecific models are more accurate usually, our results suggest that multispecies calibrations could be useful to predict wood properties for different eucalyptus species including origins and age variabilities. Moreover, these could be useful to evaluate wood properties for new species not included in our calibrations and select unknown samples which could be added or to build monospecific calibrations.1 CIRAD, Montpellier, France 2 ESALQ-USP, Piracicaba, Brazil
3 CIRAD, Kourou, France
4 ESSA-Forêts, Antanananrivo, Madagascar
5 CRDPI, Pointe Noire, Congo
6 University of Lisboa, Lisboa, Portugal
R² = 0.83 25 30 35 40 45 25 30 35 40 45 Predicted cellulose (%) Measured cellulose (%) RMSEP = 1.45 % RPD = 2.4 R² = 0.82 1 2 3 4 5 6 7 1 2 3 4 5 6 7
Predicted acid soluble lignin
(%)
Measured acid soluble lignin (%)
RMSEP = 0.39 % RPD = 2.4 R² = 0.90 0 5 10 15 20 0 5 10 15 20
Predicted total extrac:ves (%)
Measured total extrac:ves (%)
RMSEP = 1.27 % RPD = 3.2 R² = 0.84 45 50 55 60 65 70 75 80 45 50 55 60 65 70 75 80 Predicted holocellulose (%) Measured holocellulose (%) RMSEP = 2.40 % RPD = 2.5 R² = 0.92 20 25 30 35 40 20 25 30 35
Predicted Kalson lignin (%)
Measured Klason lignin (%)
RMSEP = 1.07 % RPD = 3.6
Figure 2: Results of multispecies NIR models for extractives, holocellulose, Klason lignin, cellulose, and acid soluble lignin contents (Eucalyptus grandis,
E. urophylla x E. grandis, E. camaldulensis,
E. robusta) - Comparison of measured values from
the lab and predicted values (cross-validation) by NIR from grounded wood spectra.
Table I: Results of NIR models by cross validation for Eucalyptus wood properties
(MC: moisture content, BD: basic density, TE: total extractives, HO: holocellulose, CE: alpha-cellulose, ASL: acid soluble lignin, PL: py-lignin, PSG: py-Syringyl/Guiacyl ratio, RMSECV: Root Mean Square of Cross-Validation, R²CV: Coefficient of determination,
Rank: number of factors, RPDCV: Ratio of performance deviation, CV: cross validation,
py: pyrolysis analysis).
R² = 0.94 8.0 12.0 16.0 20.0 8.0 12.0 16.0 20.0
Predicted moisture content (%)
Measured moisture content (%)
RMSEP = 0.63% RPD = 4.0 R² = 0.90 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Predicted basic density (g/cm
3 )
Measured basic density (g/cm3)
RMSEP = 0.035 g/cm3 RPD = 7.3 R² = 0.88 25 27 29 31 33 35 25 27 29 31 33 35 Predicted py-lignin (%) Measured py-lignin(%) RMSEP = 0.55 % RPD = 2.8 R² = 0.82 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 Predicted py-SG ra !o
Measured py-SG ra!o
RMSEP = 0.10 RPD = 2.3
Figure 1: Results of multispecies NIR models for moisture content and basic density (Eucalyptus
grandis, E. resinifera, E. cloeziana and Corymbia maculata) - Comparison of measured values
from the lab and predicted values (cross-validation) by NIR from solid wood spectra.
Figure 3: Results of NIR models for py-lignin and py-SG ratio (E. urophylla x E. grandis) -
Comparison of measured values from the lab and predicted values (cross-validation) by NIR from grounded wood spectra.
Reference
William P. 2014. The RPD statistic: a tutorial note. NIRS News. 25:1.Acknowledgements. This research has received funding by Agropolis Fondation under the reference ID 1203-003 through the « Investissements d’avenir » programme
(Labex Agro/ANR-10-LABX-0001-01), FAPESP (2013/25642-5, 09/53951-7), and from Bureau Océan Indien de l'AUF, through SPIRMADBOIS project (S0194COV705HA).
Nb specie Proper!es N SD Mean min max RMSECV R²CV Rank RPDCV
4 MC 1142 2.5 13.5 8.9 21.4 0.63 0.93 5 4.0 10 BD 1389 0.110 0.492 0.330 0.888 0.035 0.90 6 3.2 5 TE 185 4.1 7.6 2.7 20.6 1.3 0.90 3 3.2 6 HO 168 6.0 63.6 41.4 75.6 2.4 0.84 4 2.5 6 KL 187 3.8 29.2 22.8 40.2 1.1 0.92 4 3.6 6 ASL 157 0.9 4.1 1.6 6.2 0.4 0.82 10 2.4 6 CE 150 3.5 35.5 26.7 42.6 1.5 0.83 4 2.4 1 PL 104 1.6 29.2 26.5 32.7 0.6 0.88 4 2.8 1 PSG 109 0.2 1.7 1.7 2.2 0.1 0.82 5 2.3
