Abstract
This study tested the use of near-infrared hyperspectral images to estimate moisture content (MC) and basic specific gravity (BSG) of thawed and frozen logs of three species: quaking aspen, balsam poplar, and black spruce. For each species, more than 90 small 4 cm cubic samples were prepared and subjected to drying steps in both frozen and thawed conditions. At each step, hypercube images and sample weights were recorded to determine MC and BSG of each sample. Partial least squares (PLS) models were calibrated by considering two factors: log state (thawed and frozen conditions) and species, and their combination. With respect to the species, the PLS model accuracy depends on the range of variation in the input data. The model accuracy was the best for black spruce samples that have the lowest range of variation for both MC and BSG, whereas the model accuracy was the lowest for balsam poplar samples that have the highest range of variation for both MC and BSG. With all the data, the accuracy of the MC model worsened, but the accuracy of the BSG model reached a maximum (\( R_{\text{Validation}}^{2} \) = 0.88). The best PLS model was then employed to produce 2D MC and BSG images over the whole log disks. PLS discriminant analysis was also applied to sort the samples according to three MC or BSG classes, the species, and the log state (frozen and thawed). The overall accuracy was higher than 72 % for both the MC and BSG sorting, 86 % for the species sorting, and 97 % for the log state sorting.
Similar content being viewed by others
References
Adedipe OE, Dawson-Andoh B, Slahor J, Osborn L (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(1):49–57
Alves A, Santos A, Rozenberg P, Paques 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
ASTM-Standard-D2395-07a (2009) Standard test methods for specific gravity of wood and wood-based materials. ASTM International, West Conshohocken, PA
ASTM-Standard-D4442-07 (2009) Standard test methods for direct moisture content measurement of wood and wood-base materials. ASTM International, West Conshohocken, PA
Bakuzis EV, Hansen HL (1965) Balsam fir: a monographic review. University of Minnesota Press, Minneapolis, p 445
Barnett JR, Jeronimidis G (2003) Wood quality and its biological basis. Blackwell, Oxford, Boca Raton, p 226 (Published in the USA/Canada by CRC Press)
Barnett J, Jeronimidis G (2009) Wood quality and its biological basis. Wiley, UK, p 226
Bowyer JL, Shmulsky R, Haygreen JG (2007) Forest products and wood science: an introduction. Wiley, UK, p 496
Burger T, Kuhn J, Caps R, Fricke J (1997) Quantitative determination of the scattering and absorption coefficients from diffuse reflectance and transmittance measurements: application to pharmaceutical powders. Appl Spectrosc 51(3):309–317
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(11):2150–2157
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(5):68–72
Denig J, Wengert EM, Simpson WT (2000) Drying hardwood lumber. U.S. Department of Agriculture, Forest Service, Forest Products Laboratory, Technical Report FPL-GTR-118, pp 1–138, Madison, WI, USA
Duncker P, Spiecker H (2009) Detection and classification of Norway spruce compression wood in reflected light by means of hyperspectral image analysis. IAWA J 30(1):59–70
Fernandes A, Lousada J, Morais J, Xavier J, Pereira J, Melo-Pinto P (2013a) Comparison between neural networks and partial least squares for intra-growth ring wood density measurement with hyperspectral imaging. Comput Electron Agric 94:71–81
Fernandes A, Lousada J, Morais J, Xavier J, Pereira J, Melo-Pinto P (2013b) Measurement of intra-ring wood density by means of imaging VIS/NIR spectroscopy (hyperspectral imaging). Holzforschung 67(1):59–65
FPL (2010) Wood handbook: wood as an engineering material. Department of Agriculture, Forest Service, Forest Products Laboratory, Madison, p 508
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
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
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
Haartveit EY, Flæte PO (2008) Near infrared spectroscopy (NIRS) as a tool for effective classification of wood. In: 51st international convention of society of wood science and technology. Concepción, CHILE, pp 1–9
Haddadi A, Burger J, Leblon B, Pirouz Z, Groves K, Nader J (2015a) Using near infrared hyperspectral images on subalpine fir board—part 1: moisture content estimation. Wood Mater Sci Eng 10(1):27–40
Haddadi A, Burger J, Leblon B, Pirouz Z, Groves K, Nader J (2015b) Using near infrared hyperspectral images on subalpine fir board—part 2: density and basic specific gravity estimation. Wood Mater Sci Eng 10(1):41–56
Hans G, Leblon B, Stirling R, Nader J, LaRocque A, Cooper P (2013) Monitoring of moisture content and basic specific gravity in black spruce logs using a handheld MEMS-based near-infrared spectrometer. For Chron 89(5):605–618
Hans G, Leblon B, Cooper P, LaRocque A, Nader J, Stirling R (2015) Determination of moisture content and basic specific gravity of Populus tremuloides (Michx.) and Populus balsamifera (L.) logs using a portable near-infrared spectrometer. Wood Mater Sci Eng 10(1):3–16
Hein PRG (2012) Estimating shrinkage, microfibril angle and density of Eucalyptus wood using near infrared spectroscopy. J Near Infrared Spectrosc 20(4):427–436
Hein PRG, Campos ACM, Trugilho PF, Lima JT, Chaix G (2009) Near infrared spectroscopy for estimating wood basic density in Eucalyptus urophylla and Eucalyptus grandis. Cerne 15(2):133–141
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
Inagaki T, Schwanninger M, Kato R, Kurata Y, Thanapase W, Puthson P, Tsuchikawa S (2012) Eucalyptus camaldulensis density and fiber length estimated by near-infrared spectroscopy. Wood Sci Technol 46(1–3):143–155
Isaksson T, Naes T (1988) The effect of multiplicative scatter correction (Msc) and linearity improvement in NIR spectroscopy. Appl Spectrosc 42(7):1273–1284
Jones PD, Schimleck LR, Peter GF, Daniels RF, Clark A (2005) Nondestructive estimation of Pinus taeda L. wood properties for samples from a wide range of sites in Georgia. Can J For Res 35(1):85–92
Jones PD, Schimleck LR, So CL, Clark A, Daniels RF (2007) High resolution scanning of radial strips cut from increment cores by near infrared spectroscopy. IAWA J 28(4):473–484
Kennedy EI (1965) Strength and related properties of woods grown in Canada, Cat. no. Fo 57-1104, working paper 2006-16, Department of Forestry and Urban Development, Forestry Branch, Ottawa, Ontario, Canada, pp 1–51
Kobori H, Gorretta N, Rabatel G, Bellon-Maurel V, Chaix G, Roger JM, Tsuchikawa S (2013) Applicability of VIS–NIR hyperspectral imaging for monitoring wood moisture content (MC). Holzforschung 67(3):307–314
Krilek J, Kováč J, Kučera M (2014) Wood crosscutting process analysis for circular saws. Crosscutt Anal BioResour 9(1):417–1429
Kroll RE, Ritter DC, Gertjejansen RO, Au KC (1992) Anatomical and physical-properties of balsam poplar (Populus balsamifera L.) in Minnesota. Wood Fiber Sci 24(1):13–24
Laurikkala J, Juhola M, Kentala E (2000) Informal identification of outliers in medical data. In: 14th European conference on artificial intelligence and 5th international workshop on intelligent data analysis in medicine and pharmacology IDAMAP-2000. Berlin, Germany, pp 20–24
Leblon B, Adedipe O, Hans G, Haddadi A, Tsuchikawa S, Burger J, Stirling R, Pirouz Z, Groves K, Nader J, LaRocque A (2013) A review of near-infrared spectroscopy for monitoring moisture content and density of solid wood. Forest Chron 89(5):595–606
Libnau FO, Toft J, Christy AA, Kvalheim OM (1994) Structure of liquid water determined from infrared temperature profiling and evolutionary curve resolution. J Am Chem Soc 116(18):8311–8316
Lobell DB, Asner GP (2002) Moisture effects on soil reflectance. Soil Sci Soc Am J 66(3):722–727
Meder R, Meglen RR (2012) Near infrared spectroscopic and hyperspectral imaging of compression wood in Pinus radiata D. Don. J Near Infrared Spectrosc 20(5):583–589
Meder R, Marston D, Ebdon N, Evans R (2010) Spatially-resolved radial scanning of tree increment cores for near infrared prediction of microfibril angle and chemical composition. J Near Infrared Spectrosc 18(6):499–505
Micko MM (1987) Alberta aspen vs. black poplar wood quality differences, M. o. f. S. a. S. Canada, Fo 42-91/28-1988E, Canadian Forestry Service, Edmonton, Alberta, pp 1–38
Minasny B, McBratney A (2013) Why you don’t need to use RPD. Pedometron 33:14–15
Mora CR, Schimleck LR, Clark A, Daniels RF (2011a) Determination of basic density and moisture content of merchantable loblolly pine logs by near infrared spectroscopy. J Near Infrared Spectrosc 19(5):391–399
Mora CR, Schimleck LR, Yoon SC, Thai CN (2011b) Determination of basic density and moisture content of loblolly pine wood disks using a near infrared hyperspectral imaging system. J Near Infrared Spectrosc 19(5):401–409
Nystrom J, Hagman O (1999) Real-time spectral classification of compression wood in Picea abies. J Wood Sci 45(1):30–37
Pang X-F (2013) Water: molecular structure and properties. World Scientific, Hackensack, p 472
Russ A, Fiserova M, Gigac J (2009) Preliminary study of wood species identification by NIR spectroscopy. Wood Res 54(4):23–32
Schimleck LR, Evans R (2003) Estimation of air-dry density of increment cores by near infrared spectroscopy. Appita J 56(4):312–317
Schimleck LR, Michell AJ, Raymond CA, Muneri A (1999) Estimation of basic density of Eucalyptus globulus using near-infrared spectroscopy. Can J For Res 29:194–201
Schimleck LR, Evans R, Ilic J (2001a) Application of near infrared spectroscopy to a diverse range of species demonstrating wide density and stiffness variation. IAWA J 22(4):415–429
Schimleck LR, Evans R, Ilic J (2001b) Estimation of Eucalyptus delegatensis wood properties by near infrared spectroscopy. Can J For Res 31(10):1671–1675
Schimleck LR, Evans R, Ilic J, Matheson AC (2002a) Estimation of wood stiffness of increment cores by near-infrared spectroscopy. Can J For Res 32(1):129–135
Schimleck LR, Evans R, Matheson AC (2002b) Estimation of Pinus radiata D. Don clear wood properties by near-infrared spectroscopy. J Wood Sci 48(2):132–137
Schimleck L, Evans R, Ilic J (2003a) Application of near infrared spectroscopy to the extracted wood of a diverse range of species. IAWA J 24(4):429–438
Schimleck LR, Mora C, Daniels RF (2003b) Estimation of the physical wood properties of green Pinus taeda radial samples by near infrared spectroscopy. Can J For Res 33(12):2297–2305
Schimleck LR, Jones PD, Clark A, Daniels RF, Peter GF (2005a) Near infrared spectroscopy for the nondestructive estimation of clear wood properties of Pinus taeda L. from the southern United States. For Prod J 55(12):21–28
Schimleck LR, Sturzenbecher R, Mora C, Jones PD, Daniels RF (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(2):214–218
Schimleck LR, Rezende GDSR, Demuner BJ, Downes GM (2006) Estimation of whole-tree wood quality traits using near infrared spectra from increment cores. Appita J 59(3):231–236
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
Smith I, Landis E, Gong M (2003) Fracture and fatigue in wood. Wiley, Chichester, p 234
So CL, Via BK, Groom LH, Schimleck LR, Shupe TF, Kelley SS, Rials TG (2004) Near infrared spectroscopy in the forest products industry. For Prod J 54(3):6–16
Sykes R, Li B, Hodge G, Goldfarb B, Kadla J, H-m Chang (2005) Prediction of loblolly pine wood properties using transmittance near-infrared spectroscopy. Can J For Res 35:2423–2431
Thumm A, Riddell M, Nanayakkara B, Harrington J, Meder R (2010) Near infrared hyperspectral imaging applied to mapping chemical composition in wood samples. J Near Infrared Spectrosc 18(6):507–515
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 2:127–135
Thygesen LG, Lundqvist SO (2000a) NIR measurement of moisture content in wood under unstable temperature conditions. Part 1. Thermal effects in near infrared spectra of wood. J Near Infrared Spectrosc 8(3):183–189
Thygesen LG, Lundqvist SO (2000b) NIR measurement of moisture content in wood under unstable temperature conditions. Part 2. Handling temperature fluctuations. J Near Infrared Spectrosc 8(3):191–199
Tsuchikawa S, Hayashi K, Tsutsumi S (1996) Nondestructive measurement of the subsurface structure of biological material having cellular structure by using near-infrared spectroscopy. Appl Spectrosc 50(9):1117–1124
Tsuchikawa S, Torii M, Tsutsumi S (2001) Directional characteristics of near infrared light reflected from wood. Holzforschung 55(5):534–540
Tsuchikawa S, Inoue K, Noma J, Hayashi K (2003) Application of near-infrared spectroscopy to wood discrimination. J Wood Sci 49(1):29–35
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
Via BK, So CL, Shupe TF, Stine M, Groom LH (2005) Ability of near infrared spectroscopy to monitor air-dry density distribution and variation of wood. Wood Fiber Sci 37(3):394–402
Watanabe K, Mansfield SD, Avramidis S (2011) Application of near-infrared spectroscopy for moisture-based sorting of green hem-fir timber. J Wood Sci 57(4):288–294
Williamson GB, Wiemann MC (2010) Measuring wood specific gravity… correctly. Am J Bot 97(3):519–524
Xu QH, Qin MH, Ni YH, Defo M, Dalpke B, Sherson G (2011) Predictions of wood density and module of elasticity of balsam fir (Abies balsamea) and black spruce (Picea mariana) from near infrared spectral analyses. Can J For Res 41(2):352–358
Zhu XR, Shan Y, Li GY, Huang AM, Zhang ZY (2009) Prediction of wood property in Chinese Fir based on visible/near-infrared spectroscopy and least square-support vector machine. Spectrochim Acta Part A Mol Biomol Spectrosc 74(2):344–348
Acknowledgments
The authors thank G. Chow from FPInnovations, as well as G. Hans (UNB) and K. Phung (UNB) for their help during the experiments. The study was supported by an NSERC Strategic grant and a New Brunswick Innovation Foundation grant awarded to B. Leblon. This paper was written in memoriam of Dr. James Burger, who suddenly died on September 6, 2014.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Haddadi, A., Leblon, B., Pirouz, Z. et al. 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, 221–243 (2016). https://doi.org/10.1007/s00226-015-0767-z
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00226-015-0767-z