Performance of visible and Near-infrared spectroscopy to predict the energetic properties of wood


  • Franciele Gmach Federal University of Paraná. Paraná, Brazil
  • Letícia Jacobowski Universidade do Contestado, Santa Catarina, Brazil
  • Eraldo Antonio Bonfatti Júnior Federal University of Paraná. Paraná, Brazil
  • Elaine Cristina Lengoeski Universidade Federal de Mato Grosso. Mato Grosso, Brazil
  • Rudson Silva Oliveira Federal University of Paraná. Paraná, Brazil
  • Dimas Agostinho Silva Federal University of Paraná. Paraná, Brazil
  • Silvana Nisgoski Federal University of Paraná. Paraná, Brazil
  • Lívia Viana Federal University of Tocantins. Tocantins, Brazil
  • Diego Martins Stangerlin Universidade Federal de Mato Grosso. Mato Grosso, Brazil



Biofuel, chemometrics, Eucalyptus, heating value, multivariate analysis, Near-infrared spectroscopy


Wood can be used for fuel by direct burning, or as a raw material for other fuels; however, it is necessary to evaluate the energy properties to ensure the optimal use of this material. The most relevant characteristics to be analyzed are the higher heating value, volatile material content, fixed carbon content, and ash content. Along with the traditional methods, there are also non-destructive evaluations that are optimized for speed and reliability. Among these methods, visible spectroscopy and near-infrared spectroscopy have been proven to be robust for the prediction of several wood properties. The aim of this study was to evaluate the potential of visible spectroscopy and near-infrared spectroscopy for species discrimination and prediction of higher heating value, volatile material content, fixed carbon content, and ash content for Eucalyptus saligna, Eucalyptus dunnii, and Eucalyptus benthamii woods. For this purpose, multivariate principal component analysis and partial least squares regression were applied to the collected spectra. The principal component analysis satisfactorily discriminated the three species, explaining 99% of the variance of the visible spectroscopy spectra and 73% of that of the near-infrared spectra. The estimation of energetic properties through partial least squares regression was satisfactory for both visible spectroscopy and near-infrared spectroscopies, which presented calibration R² values close to 1 and low errors for all properties studied.


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Amaral, E.A.; Santos, L.M.; Costa, E.V.S.; Trugilho, P.F.; Hein, P.R.G. 2020. Estimation of moisture in wood chips by Near Infrared Spectroscopy. Maderas. Ciencia y Tecnología 22:291-302.

ASTM. 2013. Standard Test Method for Gross Calorific Value of Coal and Coke. ASTM D5865-13. ASTM: West Conshohocken, PA, USA.

ASTM. 2017. Standard Practices for Infrared Multivariate Quantitative Analysis. ASTM E1655-17. ASTM: West Conshohocken, PA, USA.

ASTM. 2019. Standard Test Method for Analysis of Wood Fuels. ASTM E870-82. ASTM: West Conshohocken, PA, USA.

ASTM. 2019. Standard Test Method for Volatile Matter in the Analysis of Particulate Wood Fuels. ASTM E872-82. ASTM: West Conshohocken, PA, USA.

ASTM. 2021. Standard Test Method for Ash in Wood. ASTM D1102-84. ASTM: West Conshohocken, PA, USA.

Andrade, C.R.; Trugilho, P.F.; Hein, P.R.G.; Lima, J.T.; Napoli, A. 2012. Near infrared spectroscopy for estimating eucalyptus charcoal properties. Journal of Near Infrared Spectroscopy 20:657-666.

Bersch, A.P.; Brun, E.J.; Pereira, F.A.; Silva, D.A.; De Barba, Y.R.; Dorini Junior, J.R. 2018. Characterization energy of wood of three genetic materials of Eucalyptus sp. Floresta 48(1):87-92.

Bonfatti Júnior, E.A.; Lengowski, E.C.; Takahashi, V.M.; Adur, G.M.; Silva, D.A.; Klock, U.; De Andrade, A.S.; Venson, I.; de Muñiz, G.I.B. 2019. Briquetting of waste from mechanical and chemical processes of Pinus spp. Cadernos de Ciencia & Tecnologia 36:e26522.

Casson, A.; Beghi, R.; Giovenzana, V.; Fiorindo, I.; Tugnolo, A.; Guidetti, R. 2020. Environmental advantages of visible and near infrared spectroscopy for the prediction of intact olive ripeness. Biosystems Engineering 189:1-10.

Chang, S.; Yin, C.; Liang, S.; Lu, M.; Wang, P.; Li, Z. 2020. Confirmation of brand identification in infant formulas by using near-infrared spectroscopy fingerprints. Analytical Methods 12:2469-2475.

Chen, J.; Li, G. 2020. Prediction of moisture content of wood using Modified Random Frog and Vis-NIR hyperspectral imaging. Infrared Physics & Technology 105: e103225.

Cortez, L.A.B.; Loura, E. S.; Gómes, E.O. 2014. Biomassa para bioenergia. 3rd ed. Campinas: UNICAMP.

Costa Junior, S.; Silva, D.A.; Behling, A.; Koehler, H.S.; Simon, A.A.; Costa, A. 2021. Propriedades energéticas da biomassa de Acacia mearnsii De Wild. em diferentes idades e locais de cultivo. Scientia Forestalis 49(131): e3406.

Cunha, C.L.; Torres, A.R.; Luna, A.S. 2020. Multivariate regression models obtained from near-infrared spectroscopy data for prediction of the physical properties of biodiesel and its blends. Fuel 261:e116344.

de Abreu Neto, R.; Ramalho, F.M.G.; Costa, L.R.; Hein, P.R.G. 2020. Estimating hardness and density of wood and charcoal by near-infrared spectroscopy. Wood Science and Technology 55: 215-230.

Douglas, R.K.; Nawar, S.; Alamar, M.C.; Mouazen, A.M.; Coulon, F. 2018. Rapid prediction of total petroleum hydrocarbons concentration in contaminated soil using vis-NIR spectroscopy and regression techniques. Science of The Total Environment (616-617):147-155.

Friedl, A.; Padouvas, E.; Rotter, H.; Varmuza, K. 2005. Prediction of heating values of biomass fuel from elemental composition. Analytica Chimica Acta 544: 191-198.

Fernandes, D.D.S.; Gomes, A.A.; Costa, G.B. da; da Silva, G.W.B.; Véras, G. 2011. Determination of biodiesel content in biodiesel/diesel blends using NIR and visible spectroscopy with variable selection. Talanta 87:30-34.

Ferrão, M.F.; Carvalho, C.W.; Müller, E.I.; Davanzo, C.U. 2004. Simultaneous determination of ash content and protein in wheat flour using infrared reflection techniques and partial least-squares regression (PLS). Food Science and Technology 24:333-340.

Garcia-Maraver, A.; Mata-Sanchez, J.; Carpio, M.; Perez-Jimenez, J.A. 2017. Critical review of predictive coefficients for biomass ash deposition tendency. Journal of the Energy Institute 90:214-228.

Kobori, H.; Gorretta, N.; Rabatel, G.; Bellon-Maurel, V.; Chaix, G.; Roger, J.M.; Tsuchikawa, S. 2013. Applicability of Vis-NIR hyperspectral imaging for monitoring wood moisture content (MC). Holzforschung 67:307-314.

Kobori, H.; Kojima, M.; Yamamoto, H.; Sasaki, Y.; Yamaji, F.M.; Tsuchikawa, S. 2013. Vis-NIR spectroscopy for the on-site prediction of wood properties. The Forestry Chronicle 89: 631-638.

Lana, A.Q.; Salles, T.T.; Carneiro, A. de C.O.; Cardoso, M.T.; Teixeira, R.U. 2016. Comparison of procedures for immediate chemical analysis of charcoal. Revista Árvore 40:371-376.

Lengowski, E.C.; Muñiz, G.I.B. de; Klock, U.; Nisgoski, S. 2018. Potential use of nir and visible spectroscopy to analyze chemical properties of thermally treated wood. Maderas. Ciencia y Tecnología 20: 627-640.

Liu, Z.; Fei, B.; Jiang, Z.; Cai, Z.; Liu, X. 2014. Important properties of bamboo pellets to be used as commercial solid fuel in China. Wood Science and Technology 48:903-917.

Li, Y.; Via, B.K.; Li, Y. 2020. Lifting wavelet transform for Vis-NIR spectral data optimization to predict wood density. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 240: e118566.

Li, Y.; Via, B.; Cheng, Q.; Li, Y. 2018. Lifting Wavelet Transform De-noising for Model Optimization of Vis-NIR Spectroscopy to Predict Wood Tracheid Length in Trees. Sensors 18(12): e4306.

Llana, D.F.; Íñiguez-González, G.; Díez, M.R.; Arriaga, F. 2020. Nondestructive testing used on timber in Spain: A literature review. Maderas. Ciencia y Tecnología 22:133-156.

Loureiro, B.A.; Assis,; Melo, I.C.N.A. de; Oliveira, A.F.F. de; Trugilho, P. F. 2021. Rendimento gravimétrico da carbonização e caracterização qualitativa do carvão vegetal em clones de híbridos de Corymbia spp para uso industrial. Ciênc. Ciência Florestal 31(1): 214-232.

Ma, L.; Peng, Y.; Pei, Y.; Zeng, J.; Shen, H.; Cao, J.; Qiao, Y.; Wu, Z. 2019. Systematic discovery about NIR spectral assignment from chemical structural property to natural chemical compounds. Scientific Reports 9: e9503.

Mancini, M.; Rinnan, Å. 2021. Near infrared technique as a tool for the rapid assessment of waste wood quality for energy applications. Renewable Energy 177:113-123.

McKendry, P. 2002. Energy production from biomass (part 1): Overview of biomass. Bioresource Technology 83:37-46.

Nisgoski, S.; Muñiz, G.I.B. de; Gonçalves, T.; Ballarin, A. 2017. Use of visible and near-infrared spectroscopy for discrimination of eucalypt species by examination of solid samples. Journal of Tropical Forest Science 29:371-379

Orellana, B.B.M.A.; Vale, A.T. do; Orellana, J.B.P.; Chaves, B.S.; Moreira, A.C. de O. 2020. Characterization of agricultural residues from Federal District region for energy purposes. Energia na Agricultura 35:46-61.

Oliveira, M.G. de; Christoforo, A.L.; Araujo, V.A.; Lahr, F.A.R. 2016. Química da Madeira no Contexto Energético. São Carlos: EESC/USP.

Pádua, F.A. de; Tomeleri, J.O.P.; Franco, M.P.; Silva, J.R.M. da; Trugilho, P.F. 2019. Recommendation of non-destructive sampling method for density estimation of the Eucalyptus wood. Maderas. Ciencia y Tecnología 21:565-572.

Pilar Dorado, M.; Pinzi, S.; de Haro, A.; Font, R.; Garcia-Olmo, J. 2011. Visible and NIR Spectroscopy to assess biodiesel quality: Determination of alcohol and glycerol traces. Fuel 90: 2321-2325.

Pincelli, A.L.P.S.M.; Queiroz, I.S. de. 2021. Parâmetros físico-químicos de diferentes resíduos agroindustriais para fins energéticos. Bioenergia em Revista Diálogos 11(2): 52-68.

Preece, S.L.M.; Auvermann, B.W.; MacDonald, J.C.; Morgan, C.L.S. 2013. Predicting the heating value of solid manure with visible and near-infrared spectroscopy. Fuel 106:712-717.

Rumble, J.R. 2020. CRC Handbook of Chemistry and Physics. 101st ed. Boca Raton: CRC Press.

Santos, F.D.; Santos, L.P.; Cunha, P.H.P.; Borghi, F.T.; Romão, W.; de Castro, E.V.R.; de Oliveira, E.C.; Filguerias, P.R. 2021. Discrimination of oils and fuels using a portable NIR spectrometer. Fuel 283: e118854.

Shen, J.; Zhu, S.; Liu, X.; Zhang, H.; Tan, J. 2010. The prediction of elemental composition of biomass based on proximate analysis. Energy Conversion and Management 51: 983-987.

Schimleck, L.R.; Mora, C.; Daniels, R.F. 2003. Estimation of the physical wood properties of green Pinus taeda radial samples by near infrared spectroscopy. Canadian Journal of Forest Research 33:2297-2305.

Schwanninger, M.; Rodrigues, J.C.; Fackler, K. 2011. A review of band assignments in near infrared spectra of wood and wood components. Journal of Near Infrared Spectroscopy 19: 287-308.

Silva, D.A. da; Almeida, V.C.; Viana, L.C.; Klock, U.; Muñiz, G.I.B. de. 2014. Avaliação das propriedades energéticas de resíduos de madeiras tropicais com uso da espectroscopia NIR. Floresta e Ambiente 21:561-568.

Siesler, R.W.; Ozaki, Y.; Kawata, S.; Heise, H.M. 2002. Near infrared spectroscopy: principles, instruments, applications. New York: Wiley.

TAPPI. 2021. Sampling and preparing wood for analysis. TAPPI T257 sp-21. TAPPI Standard Method: Atlanta, USA.

Telmo, C.; Lousada, J. 2011. The explained variation by lignin and extractive contents on higher heating value of wood. Biomass and Bioenergy 35:1663-1667.

Tsuchikawa, S.; Siesler, H.W. 2003. Near-infrared spectroscopic monitoring of the diffusion process of deuterium-labeled molecules in wood. Part I: Softwood. Applied Spectroscopy 57:667-674.

Vassilev, S.V.; Vassileva, C.G. 2020. Contents and associations of rare earth elements and yttrium in biomass ashes. Fuel 262: e116525.

Williams, P.C.; Sobering, D.C. 1993. Comparison of commercial near infrared transmittance and reflectance instruments for analysis of whole grains and seeds. Journal of Near Infrared Spectroscopy 1: 25-32.




How to Cite

Gmach, F. ., Jacobowski , L. ., Bonfatti Júnior, E. A., Lengoeski, E. C., Silva Oliveira, R. ., Silva, D. A., Nisgoski, S. ., Viana, L. ., & Martins Stangerlin, D. . (2023). Performance of visible and Near-infrared spectroscopy to predict the energetic properties of wood. Maderas-Cienc Tecnol, 26.




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