Rapid discrimination of wood species from native forest and plantations using near infrared spectroscopy

Fernanda M. G. Ramalho, Jéssica M. Andrade, Paulo R. G. Hein

Abstract


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.


Keywords


illegal logging; forest exploitation; wood identification; timber classification

Full Text:

PDF HTML XML

References


AACC, 1999. Method 39-00, 15. American Association of Cereal Chemists.

Adedipe OE, Dawson-Andoh AB, Slahor J, Osborn AL, 2008. Classification of red oak (Quercus rubra) and white oak (Quercus alba) wood using a near infrared spectrometer and soft independent modelling of class analogies. J Near Infrared Spectrosc 16: 49-57. https://doi.org/10.1255/jnirs.760

Brereton RG, 2003. Chemometrics: data analysis for the laboratory and chemical plant. J. Wiley, Chichester, England. https://doi.org/10.1002/0470863242

Brito B, Barreto P, 2006. Enforcement against illegal logging in the Brazilian Amazon. 4th IUCN Academy of Environmental Law Colloquium. IUCN. 27 pp.

Brunner M, Eugster R, Trenka E, Berganmin-Strotz L, 1996. FT-NIR spectroscopy and wood identification. Holzforschung 50: 130-134. https://doi.org/10.1515/hfsg.1996.50.2.130

Cooper PA, Jeremic D, Radivojevic S, Ung YT, Leblon B, 2011. Potential of near-infrared spectroscopy to characterize wood products. Can J For Res 41: 2150-2157. https://doi.org/10.1139/x11-088

Costa EVS, Rocha MFV, Hein PRG, Amaral E, Santos LM, Brandao LEVS, Trugilho PF, 2018. Influence of spectral acquisition technique and wood anisotropy on the statistics of predictive NIR-based models for wood density. J Near Infrared Spectrosc 26: 1. https://doi.org/10.1177/0967033518757070

Espinoza JA, Hodge GR, Dvorak WS, 2012. The potential use of near infrared spectroscopy to discriminate between different pine species and their hybrids. J Near Infrared Spectrosc 20: 437-447. https://doi.org/10.1255/jnirs.1006

Gierlinger N, Schwanninger M, Wimmer R, 2004. Characteristics and classification of Fourier-transform near infrared spectra of the heartwood of different larch species (Larix sp.). J Near Infrared Spectrosc 12: 113-119. https://doi.org/10.1255/jnirs.415

Hein PRG, Lima JT, Chaix G, 2009. Robustness of models based on near infrared spectra to predict the basic density in Eucalyptus urophylla wood. J Near Infrared Spectrosc 17: 141-150. https://doi.org/10.1255/jnirs.833

Kollmann FR, Coté WA, 1968. Principles of wood science and technology. Springer-Verlag, Berlin. https://doi.org/10.1007/978-3-642-87928-9

Michell AJ, Schimleck LR, 1998. Further classification of eucalypt pulp woods using principal components analysis of near-infrared spectra. Appita J 51: 127-131.

Nunes CA, Freitas MP, Pinheiro ACM, Bastos SC, 2012. Chemoface: A novel free user-friendly interface for chemometrics. J Braz Chem Soc 23: 2003-2010. https://doi.org/10.1590/S0103-50532012005000073

Pastore TCM, Braga JWB, Coradin VTR, Magalhães WLE, Okino EYA, Camargos JAA, De Muniz GIB, Bressan OA, Davrieux F, 2011. Near infrared spectroscopy (NIRS) as a potential tool for monitoring trade of similar woods: Discrimination of true mahogany, cedar, andiroba, and curupixá. Holzforschung 65: 73-80. https://doi.org/10.1515/hf.2011.010

Ramalho FMG, Hein, PRG, Andrade JM, Napoli A, 2017. Potential of near infrared spectroscopy for distinguishing charcoal produced from planted and native wood for energy purpose. Energy Fuels 31: 1593-1599. https://doi.org/10.1021/acs.energyfuels.6b02446

Russ A, Fiserova M, Gigac J, 2009. Preliminary study of wood species identification by NIR spectroscopy. Wood Res 54: 23-32.

Savitzky A, Golay MJE, 1964. Smoothing and differentiation of data by simplified least-squares procedures. Anal Chem 36: 1627-1639. https://doi.org/10.1021/ac60214a047

Schimleck LR, Michell AJ, Vinden P, 1996. Eucalypt wood classification by NIR spectroscopy and principal components analysis. Appita J 49: 319-324.

Schwanninger M, Rodrigues JC, Fackler K, 2011. A review of band assignments in near infrared spectra of wood and wood components. J Near Infrared Spectrosc 19: 287-308. https://doi.org/10.1255/jnirs.955

Tsuchikawa S, 2007. A review of recent near infrared research for wood and paper. Appl Spectrosc Rev 42: 43-71. https://doi.org/10.1080/05704920601036707

Tsuchikawa S, Kobori H, 2015. A review of recent application of near infrared spectroscopy to wood science and technology. J Wood Sci 61: 213-220. https://doi.org/10.1007/s10086-015-1467-x

Westad F, Martens H, 2000. Variable selection in near infrared spectroscopy based on significance testing in partial least square regression. J Near Infrared Spectrosc 8: 117-124. https://doi.org/10.1255/jnirs.271

Yang Z, Jiang ZH, Lu B, 2012. Investigation of near infrared spectroscopy of rosewood. Spectrosc Spectr Anal 32: 2405-2408.

Yang Z, Liu Y, Pang X, Li K, 2015. Preliminary investigation into the identification of wood species from different locations by near infrared spectroscopy. BioResources 10: 8505-8517. https://doi.org/10.15376/biores.10.4.8505-8517




DOI: 10.5424/fs/2018272-12075

Webpage: www.inia.es/Forestsystems