The objective of
this study was to evaluate the potential of near-infrared
(near IR) spectroscopy associated with multivariate statistics to
distinguish charcoal produced from wood of planted and native forests
in Brazil. Timber forest species from the Cerrado (Cedrela sp., Aspidosperma sp., Jacaranda sp., and Apuleia sp.) and Eucalyptus clones from forestry companies (Vallourec steel producer and Cenibra
pulp producer) were pyrolyzed under well controlled laboratory scale
conditions at the final temperatures of 300 (573,15), 500 (773,15)
and 700 °C (973,15 K), respectively. Fifteen charcoals of each
species were produced for each temperature leading to heighten controlled
pyrolysis treatments and finally 270 charcoal samples (3 treatments
× 15 repetitions × 6 materials). Principal component analysis
(PCA) and partial least-squares regression (PLS-R) were carried out
in the spectra recorded from charcoal specimens. Near IR spectroscopy
associated with PCA was not able to differentiate the charcoals produced
from native and planted woods if the 270 samples were considered in
the same analysis. However, the separation of native and planted charcoal
was achieved when the samples were analyzed separately by final pyrolysis
temperature. Thus, the prediction of native or planted classes by
PLS-R presented better performance for samples pyrolyzed at 300 °C,
followed by those at 500 °C, 700 °C, and all together.
Aim of study: To verify how well near infrared (NIR) spectroscopy is able to discriminate wood specimens from natural and planted forests. This study was carried out using tropical trees from Brazil.Area of study: Wood specimens coming from Lavras (21°10′S, 44°54′W), Paraopeba (19°16′S, 44°24′W) and Belo Oriente (19°17′S, 42°23′W) cities, Minas Gerais state, southeastern Brazil were insvetigated.Material and methods: NIR spectra were recorded in the radial surface of wood specimens of four native species (Cedrela sp., Apuleia sp., Aspidosperma sp. and Jacaranda sp.) and two commercial clones (Eucalyptus for bioenergy and pulp & paper).Main results: The principal component analysis (PCA) of spectral information revealed that it is possible to distinguish wood from planted and native forests. The dispersion of scores in the graphic formed by the first and second principal component formed two groups allowing differentiating very clearly the Eucalyptus clones from the native woods. The partial least squares discriminant analysis (PLS-DA) allowed the prediction of group of species with a high degree of correct classification. The PLS-DA models performed from untreated NIR spectra obtained 86 to 100% accuracy for the natural wood species.Research highlights: From PLS-DA of treated NIR spectra, no Eucalyptus wood sample was classified as a natural forest species and vice versa. NIR technique associated with multivariate statistics are promising to discriminate wood specimens from native or planted forests and thus identify frauds.
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