Predictive capacity of some wood properties by near-infrared spectroscopy

References

• Acquah GE, Essien C, Via BK, Billor N, Eckhardt LG. 2018. Estimating the basic density and mechanical properties of elite loblolly pine families with near infrared spectroscopy. For Sci. 64(2):149–158.
   Google Scholar
• Alves A, Santos A, Rozenberg P, Pâques LE, Charpentier JP, Schwanninger M, Rodrigues J. 2012. A common near infrared-based partial least squares regression model for the prediction of wood density of Pinus pinaster and Larix× eurolepis. Wood Sci Technol. 46(1–3):157–175.
   Web of Science ®Google Scholar
• Andrade C, Trugilho P, Napoli A, Vieira R, Lima J, Sousa L. 2010. Estimation of the mechanical properties of wood from Eucalyptus urophylla using near infrared spectroscopy. Cerne. 16(3):291–298.
   Web of Science ®Google Scholar
• Anjos O, Pereira H, Rosa M. 2010. Tensile properties of cork in the tangential direction: variation with quality, porosity, density and radial position in the cork plank. Mater Des. 31(4):2085–2090.
   Web of Science ®Google Scholar
• ASTM D143-94. 2007. Standard test methods for small clear specimens of timber. West Conshohocken (PA): ASTM International.
   Google Scholar
• Bokobza L. 2002. Origin of near-infrared absorption bands. In: Siesler HW, Ozaki Y, Kawata S, Heise HM, editors. Near infrared spectroscopy. Weinheim: Wiley-VCH; p. 11.
   Google Scholar
• Costa EV, Rocha MF, Hein PR, Amaral EA, Santos LM, Brandão LE, et al. 2018. Influence of spectral acquisition technique and wood anisotropy on the statistics of predictive near infrared–based models for wood density. J Near Infrared Spectrosc. 26(2):106–116.
   Web of Science ®Google Scholar
• Dahlen J, Diaz I, Schimleck L, Jones PD. 2017. Near-infrared spectroscopy prediction of southern pine no. 2 lumber physical and mechanical properties. Wood Sci Technol. 51(2):309–322.
   Web of Science ®Google Scholar
• Defo M, Taylor AM, Bond B. 2007. Determination of moisture content and density of fresh-sawn red oak lumber by near infrared spectroscopy. For Prod J. 57:68–72.
   Web of Science ®Google Scholar
• Diaconu D, Wassenberg M, Spiecker H. 2016. Variability of European beech wood density as influenced by interactions between tree-ring growth and aspect. For Ecosyst. 3(1):6.
   Google Scholar
• dos Santos LM, Amaral EA, Nieri EM, Costa EV, Trugilho PF, Calegário N, et al. 2020. Estimating wood moisture by near infrared spectroscopy: testing acquisition methods and wood surfaces qualities. Wood Mater Sci Eng. 1–8.
   Web of Science ®Google Scholar
• Druckenmüller K, Günther K, Elbers G. 2018. Near-infrared spectroscopy (NIRS) as a tool to monitor exhaust air from poultry operations. Sci Total Environ. 630:536–543.
   PubMed Web of Science ®Google Scholar
• Everard CD, Fagan CC, McDonnell KP. 2012. Visible-near infrared spectral sensing coupled with chemometric analysis as a method for on-line prediction of milled biomass composition pre-pelletising. J Near Infrared Spectrosc. 20(3):361–369.
   Web of Science ®Google Scholar
• Forest Products Laboratory. 2010. Wood handbook – wood as an engineering material. Madison (WI): U.S. Department of Agriculture, Forest Service, Forest Products Laboratory. General technical report FPL-GTR-190.
   Google Scholar
• Fujimoto T, Kurata Y, Matsumoto K, Tsuchikawa S. 2008. Application of near infrared spectroscopy for estimating wood mechanical properties of small clear and full-length lumber specimens. J Near Infrared Spectrosc. 16(6):529–537.
   Web of Science ®Google Scholar
• Fujimoto T, Yamamoto H, Tsuchikawa S. 2007. Estimation of wood stiffness and strength properties of hybrid larch by near-infrared spectroscopy. Appl Spectrosc. 61(8):882–888.
   PubMed Web of Science ®Google Scholar
• Galvão RK, Araujo MC, José GE, Pontes MJ, Silva EC, Saldanha TC. 2005. A method for calibration and validation subset partitioning. Talanta. 67(4):736–740.
   PubMed Web of Science ®Google Scholar
• Gaspar F, Lopes J, Cruz H, Schwanninger M, Rodrigues J. 2009. Application of near infrared spectroscopy and multivariate data analysis for the evaluation of glue lines of untreated and copper azole treated laminated timber before and after ageing. Polym Degrad Stab. 94(7):1061–1071.
   Web of Science ®Google Scholar
• Gindl W, Teischinger A. 2002. The potential of Vis-and NIR-spectroscopy for the non-destructive evaluation of grain-angle in wood. Wood Fiber Sci. 34(4):651–656.
   Web of Science ®Google Scholar
• Gindl W, Teischinger A, Schwanninger M, Hinterstoisser B. 2001. The relationship between near infrared spectra of radial wood surfaces and wood mechanical properties. J Near-Infrared Spectrosc. 9(4):255–261.
   Web of Science ®Google Scholar
• Haartveit EY, Flæte PO. 2006. Rapid prediction of basic wood properties by near infrared spectroscopy. N Z J For Sci. 36(2/3):393.
   Google Scholar
• Haddadi A, Leblon B, Pirouz Z, Nader J, Groves K. 2016. Prediction of wood properties for thawed and frozen logs of quaking aspen, balsam poplar, and black spruce from near-infrared hyperspectral images. Wood Sci Technol. 50(2):221–243.
   Web of Science ®Google Scholar
• Hans G. 2014. Real-time and non-destructive characterization of wood log properties using near-infrared and ground penetrating radar sensors [PhD thesis]. University of News Brunswick, Canada. https://unbscholar.lib.unb.ca/islandora/object/unbscholar%3A9453.
   Google Scholar
• Hedrick SE, Bennett RM, Kelley SS, Rials TG. 2004. Determination of wood properties using near infrared spectroscopy. In: Structures 2004: building on the past, securing the future; p. 1–8.
   Google Scholar
• Hein PR. 2010. Multivariate regression methods for estimating basic density in Eucalyptus wood from near infrared spectroscopic data. Cerne. 16:90–96.
   Google Scholar
• Hein PR, Campos AC, Mendes RF, Mendes LM, Chaix G. 2011. Estimation of physical and mechanical properties of agro-based particleboards by near infrared spectroscopy. Eur J Wood Wood Prod. 69(3):431–442.
   Web of Science ®Google Scholar
• Hoffmeyer P, Pedersen JG. 1995. Evaluation of density and strength of Norway spruce wood by near infrared reflectance spectroscopy. Holz Roh-Werkst. 53(3):165–170.
   Google Scholar
• Johnson GR, Gartner BL. 2006. Genetic variation in basic density and modulus of elasticity of coastal Douglas-fir. Tree Genet Genomes. 3(1):25–33.
   Web of Science ®Google Scholar
• Jones PD, Schimleck LR, So CL, Clark A III, Daniels RF. 2007. High resolution scanning of radial strips cut from increment cores by near infrared spectroscopy. IAWA J. 28(4):473–484.
   Web of Science ®Google Scholar
• Jozsa LA, Kellogg RM. 1986. An exploratory study of the density and annual ring weight trends in fast-growth coniferous woods in British Columbia. Forintek Canada Corporation.
   Google Scholar
• Kelley S, Rials GT, Groom LR, So CL. 2004a. Use of near infrared spectroscopy to predict the mechanical properties of six softwoods. Holzforschung. 58(3):252–260.
   Web of Science ®Google Scholar
• Kelley S, Rials TG, Snell R, Groom LH, Sluiter A. 2004b. Use of near infrared spectroscopy to measure the chemical and mechanical properties of solid wood. Wood Sci Technol. 38(4):257–276.
   Web of Science ®Google Scholar
• Kobori H, Inagaki T, Fujimoto T, Okura T, Tsuchikawa S. 2015. Fast online NIR technique to predict MOE and moisture content of sawn lumber. Holzforschung. 69(3):329–335.
   Web of Science ®Google Scholar
• Koman S, Feher S, Abraham J, Taschner R. 2013. Effect of knots on the bending strength and the modulus of elasticity of wood. Wood Res. 58(4):617–626.
   Web of Science ®Google Scholar
• Kothiyal V, Raturi A. 2011. Estimating mechanical properties and specific gravity for five-year-old Eucalyptus tereticornis having broad moisture content range by NIR spectroscopy. Holzforschung. 65(5):757–762.
   Web of Science ®Google Scholar
• Kothiyal V, Raturi A, Dubey YM. 2014. Enhancing the applicability of near infrared spectroscopy for estimating specific gravity of green timber from Eucalyptus tereticornis by developing composite calibration using both radial and tangential face of wood. Eur J Wood Wood Prod. 72(1):11–20.
   Web of Science ®Google Scholar
• Leblon B, Adedipe O, Guillaume H, Haddadi A, Tsuchikawa S, Burger J, et al. 2013. A review of near-infrared spectroscopy for monitoring moisture content and density of solid wood. For Chron. 89(5):595–606.
   Google Scholar
• Leinonen A, Harju AM, Venäläinen M, Saranpää P, Laakso T. 2008. FT-NIR spectroscopy in predicting the decay resistance related characteristics of solid Scots pine (Pinus sylvestris L.) heartwood. Holzforschung. 62(3):284–288.
   Web of Science ®Google Scholar
• Liu Z, Liu Y, Yu H, Juan J. 2006. Measurement of the dynamic modulus of elasticity of wood panels. Front For China. 1(4):425–430.
   Google Scholar
• Mitsui K, Inagaki T, Tsuchikawa S. 2008. Monitoring of hydroxyl groups in wood during heat treatment using NIR spectroscopy. Biomacromolecules. 9(1):286–288.
   PubMed Web of Science ®Google Scholar
• Mouazen AM, Saeys W, Xing J, De Baerdemaeker J, Ramon H. 2005. Near infrared spectroscopy for agricultural materials: an instrument comparison. J Near Infrared Spectrosc. 13(2):87–97.
   Web of Science ®Google Scholar
• Mousavi SY. 2016. Performing different mechanical tests on Douglas-fir wood. http://works.bepress.com/seyyedyahya-mousavi/6/.
   Google Scholar
• Nourian S. 2018. Thermal modification of Western hemlock (Tsuga heterophylla) [MSc thesis]. The University of British Columbia. https://open.library.ubc.ca/cIRcle/collections/ubctheses/24/items/1.0368548.
   Google Scholar
• Osborne NL, Høibø ØA, Maguire DA. 2016. Estimating the density of coast Douglas-fir wood samples at different moisture contents using medical X-ray computed tomography. Comput Electron Agric. 127:50–55.
   Web of Science ®Google Scholar
• Pastore TC, Braga JW, Coradin VT, Magalhães WL, Okino EY, Camargos JA, et al. 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(1):73–80.
   Web of Science ®Google Scholar
• Rohrbach K. 2008. Schedule and post-drying storage effects on Western Hemlock squares quality [MSc dissertation]. Vancouver (BC): The University of British Columbia.
   Google Scholar
• Sandak J, Sandak A, Meder R. 2016. Assessing trees, wood and derived products with NIR spectroscopy: Hints and tips. J Near Infrared Spectrosc. 24:485–505.
   Web of Science ®Google Scholar
• Schimleck L, Doran J, Rimbawanto A. 2003. Near Infrared Spectroscopy for cost Effective screening of Foliar Oil characteristics in a Melaleuca cajuputi breeding population. J Agric Food Chem. 51(9):2433–2437.
   PubMed Web of Science ®Google Scholar
• Schimleck LR, Evans R, Matheson AC. 2002. Estimation of Pinus radiata D. Don clear wood properties by near-infrared spectroscopy. J Wood Sci. 48(2):132–137.
   Web of Science ®Google Scholar
• Schimleck LR, Jones PD, Clark A, Daniels RF, Peter GF. 2005a. Near infrared spectroscopy for the non-destructive estimation of clear wood properties of Pinus taeda L. from the Southern USA. For Prod J. 55(12):21–28.
   Web of Science ®Google Scholar
• Schimleck L, Matos J, Trianoski R, Prata J. 2018. Comparison of Methods for Estimating Mechanical Properties of Wood by NIR Spectroscopy. J spectrosc
   PubMedGoogle Scholar
• Schimleck LR, Michell AJ, Raymond CA, Muneri A. 1999. Estimation of basic density of Eucalyptus globulus using near-infrared spectroscopy. Can J For Res. 29(2):194–201.
   Web of Science ®Google Scholar
• Schimleck LR, Stürzenbecher R, Mora C, Jones P, Daniels R. 2005b. Comparison of Pinus taeda L. wood property calibrations based on NIR spectra from the radial-longitudinal and radial-transverse faces of wooden strips. Holzforschung. 59:214–218.
   Web of Science ®Google Scholar
• Schimleck LR, Wright PJ, Michell AJ, Wallis AA. 1997. Near-infrared spectra and chemical compositions of E. globulus and E. nitens plantation woods. Appita J. 50(1):40–46.
   Web of Science ®Google Scholar
• 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(5):287–308.
   Web of Science ®Google Scholar
• Thumm A, Meder R. 2001. Stiffness prediction of radiata pine clearwood test pieces using near infrared spectroscopy. J Near Infrared Spectrosc. 9(2):117–122.
   Web of Science ®Google Scholar
• Thygesen LG. 1994. Determination of dry matter content and basic density of Norway spruce by near infrared reflectance and transmittance spectroscopy. J Near Infrared Spectrosc. 2(3):127–135.
   Google Scholar
• Todorovic N, Popovic Z, Milic G. 2015. Estimation of quality of thermally modified beech wood with red heartwood by FT-NIR spectroscopy. Wood Sci Technol. 49(3):527–549.
   Web of Science ®Google Scholar
• Via BK, Shupe TF, Groom LH, Stine M, So CL. 2003. Multivariate modelling of density, strength and stiffness from near infrared spectra for mature, juvenile and pith wood of longleaf pine (Pinus palustris). J Near Infrared Spectrosc. 11(5):365–378.
   Web of Science ®Google Scholar
• Weyer L, Lo SC. 2002. Handbook of vibrational spectroscopy, vol. 3. Chalmers JM, Griffiths PR, editors. Chichester: John Wiley; p. 1817.
   Google Scholar
• Yu H, Zhao R, Fu F, Fei B, Jiang Z. 2009. Prediction of mechanical properties of Chinese fir wood by near infrared spectroscopy. Front For China. 4(3):368–373.
   Google Scholar
• Zhao RJ, Huo XM, Zhang L. 2009. Estimation of modulus of elasticity of Eucalyptus pellita wood by near infrared spectroscopy. Spectrosc Spectr Anal. 29(9):2392–2395.
   Web of Science ®Google Scholar
• Zobel BJ, van Buijtenen JP. 1989. Wood variation. Its causes and control. Heidelberg: Springer; p. 363.
   Google Scholar