Skip to main content
Log in

On-line species identification of green hem-fir timber mix based on near infrared spectroscopy and chemometrics

  • Original
  • Published:
European Journal of Wood and Wood Products Aims and scope Submit manuscript

Abstract

In this study, a method suitable for on-line rapid species classification of western hemlock and amabilis fir (hem-fir) green mix of timber was developed and tested with the use of near infrared spectroscopy (NIRS) and chemometrics. The spectra of 600 wood specimens obtained from each species were collected over the wavelength range of 350–2500 nm. They were thereafter pretreated by smoothing; first derivative, second derivative and standard normal variate and calibration models were developed using the wavelength range of 800–1800 nm by partial least squares-linear discriminant analysis (PLS-LDA) and least squares-support vector machines (LS-SVM). The effects of wood surface (transverse, tangential and radial), wood zone (heartwood and sapwood) and sample moving speed (0, 0.5 and 1 m/s) were also explored. LS-SVM is superior to PLS-LDA in terms of classification performance at moving conditions. The best results were obtained using the LS-SVM method when spectra were collected on the transverse surface at a speed of 1 m/s and pretreated by smoothing. The sensitivity, specificity, and accuracy for both calibration and validation sets were all 100%. Collectively, NIR spectroscopy combined with LS-SVM is capable of on-line species separation of green hem-fir mix prior to wood drying.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

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

    Article  CAS  Google Scholar 

  • Barnes RJ, Dhanoa MS, Lister SJ (1989) Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Appl Spectrosc 43(5):772–777

    Article  CAS  Google Scholar 

  • Braga JWB, Pastore TCM, Coradin VTR, Camargos JAA, Silva ARd (2011) The use of near infrared spectroscopy to identify solid wood specimens of Swietenia Macrophylla (Cites Appendix II). Iawa J 32(2):285–296

    Article  Google Scholar 

  • Brùndum J, Munck L, Henckel P, Karlsson A, Tornberg E, Engelsen SB (2000) Prediction of water-holding capacity and composition of porcine meat by comparative spectroscopy. Meat Sci 55(2):177–185

    Article  Google Scholar 

  • Brunner M, Eugster R, Trenka E, Bergamin-Strotz L (1996) FT-NIR spectroscopy and wood identification. Holzforschung 50(2):130–134

    Article  CAS  Google Scholar 

  • Chu X, Yuan H, Lu W (2004) Progress and application of spectral data pretreatment and wavelength selection methods in NIR analytical technique. Prog Chem 16(4):528–542

    CAS  Google Scholar 

  • Coast Forest Products Association (2018) Coastal Products. http://www.coastforest.org/products/product-directory/species/. Accessed 24 Nov 2018

  • 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

    Article  Google Scholar 

  • Dawson-Andoh B, Adedipe OE (2012) Rapid spectroscopic separation of three Canadian softwoods. Wood Sci Technol 46(6):1193–1202

    Article  CAS  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(5):68–72

    CAS  Google Scholar 

  • Flæte PO, Haartveit EY, Vadla K (2006) Near infrared spectroscopy with multivariate statistical modelling as a tool for differentiation of wood from tree species with similar appearance. N Z J For Sci 36(2/3):382–392

    Google Scholar 

  • Fujimoto T, Kurata Y, Matsumoto K, Tsuchikawa S (2010) Feasibility of near-infrared spectroscopy for online multiple trait assessment of sawn lumber. J Wood Sci 56(6):452–459

    Article  CAS  Google Scholar 

  • 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(2):113–119

    Article  CAS  Google Scholar 

  • Jones PD, Schimleck LR, Peter GF, Daniels RF III, Clark A (2006) Nondestructive estimation of wood chemical composition of sections of radial wood strips by diffuse reflectance near infrared spectroscopy. Wood Sci Technol 40(8):709–720

    Article  CAS  Google Scholar 

  • Kennard RW, Stone LA (1969) Computer aided design of experiments. Technometrics 11(1):137–148

    Article  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

    Article  CAS  Google Scholar 

  • Lazarescu C, Hart F, Pirouz Z, Panagiotidis K, Mansfield SD, Barrett JD, Avramidis S (2017) Wood species identification by near-infrared spectroscopy. Int Wood Prod J 8(1):32–35

    Article  Google Scholar 

  • Leblon B, Adedipe O, Hans G et al (2013) A review of near-infrared spectroscopy for monitoring moisture content and density of solid wood. For Chron 89(5):595–606

    Article  Google Scholar 

  • Li H-D, Xu QS, Liang YZ (2018) libPLS: an integrated library for partial least squares regression and linear discriminant analysis. Chemom Intell Lab 176:34–43

    Article  CAS  Google Scholar 

  • Mehrotra R, Singh P, Kandpal H (2010) Near infrared spectroscopic investigation of the thermal degradation of wood. Thermochim Acta 507–508:60–65

    Article  Google Scholar 

  • Nicolaï BM, Beullens K, Bobelyn E, Peirs A, Saeys W, Theron IK, Lammertyn J (2007) Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biol Technol 46(2):99–118

    Article  Google Scholar 

  • Pastore TCM, Braga JWB, Coradin VTR 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

    Article  CAS  Google Scholar 

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

    CAS  Google Scholar 

  • Shou G, Zhang W, Gu Y, Zhao D (2014) Application of near infrared spectroscopy for discrimination of similar rare woods in the Chinese market. J Near Infrared Spectrosc 22(6):423–432

    Article  CAS  Google Scholar 

  • Snel FA, Braga JWB, da Silva D et al (2018) Potential field-deployable NIRS identification of seven Dalbergia species listed by CITES. Wood Sci Technol 52(5):1411–1427

    Article  CAS  Google Scholar 

  • Sofianto IAd, Inagaki T, Kato K, Itoh M, Tsuchikawa S (2017) Modulus of elasticity prediction model on sugi (Cryptomeria japonica) lumber using online near-infrared (NIR) spectroscopic system. Int Wood Prod J 8(4):193–200

    Article  Google Scholar 

  • Sohi A, Avramidis S, Mansfield S (2017) Near-infrared spectroscopic separation of green chain sub-alpine fir lumber from a spruce-pine-fir mix. BioResources 12(2):3720–3727

    Article  CAS  Google Scholar 

  • Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300

    Article  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • Tsuchikawa S, Hirashima Y, Sasaki Y, Ando K (2005) Near-infrared spectroscopic study of the physical and mechanical properties of wood with meso-and micro-scale anatomical observation. Appl Spectrosc 59(1):86–93

    Article  CAS  Google Scholar 

  • Tsuchikawa S, Inoue K, Noma J, Hayashi K (2003) Application of near infrared spectroscopy to wood discrimination. J Wood Sci 49(1):29–35

    Article  Google Scholar 

  • Watanabe K, Hart F, Mansfield SD, Avramidis S (2010) Detection of wet-pockets on the surface of Tsuga heterophylla (Raf.) Sarg. by near infrared (NIR) spectroscopy. Holzforschung 64(1):55–60

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • Watanabe K, Mansfield SD, Avramidis S (2012) Wet-pocket classification in Abies lasiocarpa using spectroscopy in the visible and near infrared range. Eur J Wood Prod 70(1–3):61–67

    Article  Google Scholar 

  • Yang Z, Jiang Z, Lü B (2012) Investigation of near infrared spectroscopy of rosewood. Spectrosc Spect Anal 32(9):2405–2408

    CAS  Google Scholar 

  • 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(4):8505–8517

    CAS  Google Scholar 

  • Yu HY, Niu XY, Lin HJ, Ying YB, Li BB, Pan XX (2009) A feasibility study on on-line determination of rice wine composition by Vis–NIR spectroscopy and least-squares support vector machines. Food Chem 113(1):291–296

    Article  CAS  Google Scholar 

  • Zhong Y, Lü B, Huang A-m, Liu Y-n, Xie X-q (2012) Rapid identification of softwood and hardwood by near infrared spectroscopy of cross-sectional surfaces. Spectrosc Spect Anal 32(7):1785–1789

    Google Scholar 

  • Zhou Z, Yin J, Zhou S, Zhou H, Zhang Y (2016) Detection of knot defects on coniferous wood surface using near infrared spectroscopy and chemometrics. BioResources 11(4):9533–9546

    Article  CAS  Google Scholar 

  • Zhou Z, Zeng S, Li X, Zheng J (2015) Nondestructive detection of blackheart in potato by visible/near infrared transmittance spectroscopy. J Spectro 2015:1–9

    Google Scholar 

Download references

Acknowledgements

This work was supported by State Scholarship Fund of China Scholarship Council (No. 201708330485), Zhejiang Provincial Science and Technology Key R & D Projects of China (No. 2018C02013), and Pre-research Project of the Research Center for Smart Agriculture and Forestry in Zhejiang A&F University (No. 2013ZHNL03). Many thanks to Brandon Chan, Pablo Chung, and Joseph Kim, the UBC-Centre for Advanced Wood Processing for wood specimen preparation and Prof. Shawn Mansfield for allowing the authors to use his NIR spectrometer.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhu Zhou.

Ethics declarations

Conflict of interest

There is no conflict of interest associated with this research.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, Z., Rahimi, S. & Avramidis, S. On-line species identification of green hem-fir timber mix based on near infrared spectroscopy and chemometrics. Eur. J. Wood Prod. 78, 151–160 (2020). https://doi.org/10.1007/s00107-019-01479-8

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00107-019-01479-8

Navigation