Performance of visible and Near-infrared spectroscopy to predict the energetic properties of wood
DOI:
https://doi.org/10.22320/s0718221x/2024.11Keywords:
Biofuel, chemometrics, Eucalyptus, heating value, multivariate analysis, Near-infrared spectroscopyAbstract
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.
Downloads
References
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. https://doi.org/10.4067/S0718-221X2020005000304
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. https://doi.org/10.1255/jnirs.1028
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. https://doi.org/10.5380/rf.v48i1.51673
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. https://doi.org/10.35977/0104-1096.cct2019.v36.26522
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. https://doi.org/10.1016/j.biosystemseng.2019.11.003
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. https://doi.org/10.1039/D0AY00375A
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. https://doi.org/10.1016/j.infrared.2020.103225
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. https://doi.org/10.18671/scifor.v49n131.04
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. https://doi.org/10.1016/j.fuel.2019.116344
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. https://doi.org/10.1007/s00226-020-01232-y
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. https://doi.org/10.1016/j.scitotenv.2017.10.323
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. https://doi.org/10.1016/j.aca.2005.01.041
Fernandes, D.D.S.; Gomes, A.A.; da Costa, G.B.; 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. https://doi.org/10.1016/j.talanta.2011.09.025
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. https:/doi.org/10.1590/S0101-20612004000300005
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. https://doi.org/10.1016/j.joei.2016.02.002
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. https://doi.org/10.1515/hf-2012-0054
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. https://doi.org/10.5558/tfc2013-114
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. https://doi.org/10.1590/0100-67622016000200020
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. https://doi.org/10.4067/S0718-221X2018005041001
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. https://doi.org/10.1007/s00226-014-0648-x
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. https://doi.org/10.1016/j.saa.2020.118566
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. https://doi.org/10.3390/s18124306
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. https://doi.org/10.4067/S0718-221X2020005000201
Loureiro, B.A.; de Assis, M.R.; de Melo, I.C.N.A.; de Oliveira, A.F.F.; Trugilho, P. F. 2021. Rendimento gravimétrico da carbonização e caracterização qualitativa do carvão vegetal em clones de híbridos de Corymbia spp para uso industrial. Ciência Florestal 31(1): 214-232. https://doi.org/10.5902/1980509836120
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. https://doi.org/10.1038/s41598-019-45945-y
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. https://doi.org/10.1016/j.renene.2021.05.137
McKendry, P. 2002. Energy production from biomass (part 1): Overview of biomass. Bioresource Technology 83:37-46. https://doi.org/10.1016/S0960-8524(01)00118-3
Nisgoski, S.; de Muñiz, G.I.B.; 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 https://www.jstor.org/stable/44272915
Orellana, B.B.M.A.; do Vale, A.T.; 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. https://doi.org/10.17224/EnergAgric.2020v35n1p46-61
de Oliveira, M.G.; Christoforo, A.L.; Araujo, V.A.; Lahr, F.A.R. 2016. Química da Madeira no Contexto Energético. São Carlos: EESC/USP. https://www.researchgate.net/profile/Francisco-Rocco-Lahr/publication308890980_Quimica_da_Madeira_no_Contexto_Energeticolinks/57f52d4308ae280dd0b8d98d/Quimica-da-Madeira-no-Contexto-Energetico.pdf
de Pádua, F.A.; Tomeleri, J.O.P.; Franco, M.P.; da Silva, J.R.M.; Trugilho, P.F. 2019. Recommendation of non-destructive sampling method for density estimation of the Eucalyptus wood. Maderas. Ciencia y Tecnología 21:565-572. https://doi.org/10.4067S0718-221X2019005000412
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. https://doi.org/10.1016/j.fuel.2011.02.015
Pincelli, A.L.P.S.M.; de Queiroz, I.S. 2021. Parâmetros físico-químicos de diferentes resíduos agroindustriais para fins energéticos. Bioenergia em Revista Diálogos 11(2): 52-68. http://fatecpiracicaba.edu. br/revista/index.php/bioenergiaemrevista/article/view/441
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. https://doi.org/10.1016/j. fuel.2012.10.006
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. https://doi.org/10.1016/j.fuel.2020.118854
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. https://doi.org/10.1016/j.enconman.2009.11.039
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. https://doi.org/10.1139/x03-173
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. https://doi.org/10.1255/jnirs.955
da Silva, D.A.; Almeida, V.C.; Viana, L.C.; Klock, U.; de Muñiz, G.I.B. 2014. Avaliação das propriedades energéticas de resíduos de madeiras tropicais com uso da espectroscopia NIR. Floresta e Ambiente 21:561- 568. https://doi.org/10.1590/2179-8087.043414
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. https://imisrise.tappi.org/TAPPI/Products/01/T/0104T257.aspx
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. https://doi.org/10.1016/j.biombioe.2010.12.038
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. https://doi.org/10.1366/000370203322005364
Vassilev, S.V.; Vassileva, C.G. 2020. Contents and associations of rare earth elements and yttrium in biomass ashes. Fuel 262: e116525. https://doi.org/10.1016/j.fuel.2019.116525
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. https://doi.org/10.1255/jnirs.3
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Los autores/as conservarán sus derechos de autor y garantizarán a la revista el derecho de primera publicación de su obra, el cuál estará simultáneamente sujeto a la Licencia de Reconocimiento de Creative Commons CC-BY que permite a terceros compartir la obra siempre que se indique su autor y su primera publicación esta revista.
