Skip to main content

Advertisement

SpringerLink
Log in

Utilization of genetic algorithms to optimize loblolly pine wood property models based on NIR spectra and SilviScan data

  • Original
  • Published:
Wood Science and Technology Aims and scope Submit manuscript

Abstract

Near-infrared wavelengths selected by genetic algorithm were used to optimize partial least squares (PLS) regression models for loblolly pine (Pinus taeda L.) from the southeastern United States. Wood properties examined included density (D), microfibril angle, modulus of elasticity and tracheid coarseness (C), radial diameter (R), tangential diameter (T), and wall thickness (w)—measured by SilviScan. The optimization process was run for each property with Agenda 2020 samples utilized for PLS model development and the other sets used for prediction. The number of variables (i.e. wavelengths) varied from 10 to 100 with an optimum number identified by genetic algorithm. When compared to a full data set model (based on 700 wavelengths), calibration and prediction performance of optimized PLS regression models were superior for all properties. Importantly, representative wavelengths for each property were consistently related to recognized bond vibrations observed in specific wood components demonstrating that optimization targets wavelengths directly related to changes in wood chemistry within the examined loblolly pine samples.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Ayanleye S, Nasir V, Avramidis S, Cool J (2021) Effect of wood surface roughness on prediction of structural timber properties by infrared spectroscopy using ANFIS, ANN and PLS regression. Eur J Wood Prod 79(1):101–115

    Article  Google Scholar 

  • Bangalore AS, Shaffer RE, Small GW, Arnold MA (1996) Genetic algorithm -based method for selecting wavelengths and model size for use with partial least-squares regression: application to near-infrared spectroscopy. Anal Chem 68(23):4200–4212

    Article  CAS  Google Scholar 

  • Birkett MD, Gambino MJT (1988) Potential applications for near infrared spectroscopy in the pulping industry. Pap S Afr Nov/Dec: 5

  • Cogdill RP, Schimleck LR, Jones PD, Peter GF, Daniels RF, Clark A (2004) Estimation of the physical wood properties of Pinus taeda L. radial strips using least squares support vector machines. J. Near Infrared Spectrosc 12(4):263–270

    Article  CAS  Google Scholar 

  • Dahlen J, Antony F, Schimleck LR, Daniels R (2018) Relationships between static mechanical properties and SilviScan measured wood properties in loblolly pine. For Prod J 68(1):37–42

    Google Scholar 

  • De A, Chanda S, Tudu B, Bandyopadhyay RB, Hazarika AK, Sabhapondit S, Baruah BD, Tamuly P, Bhattachryya N (2017) Wavelength selection for prediction of polyphenol content in inward tea leaves using NIR. IEEE 7th Int Adv Comput Conf (IACC) Hyderabad 2017:184–187. https://doi.org/10.1109/IACC.2017.0050

    Article  Google Scholar 

  • Evans R (1994) Rapid measurement of the transverse dimensions of tracheids in radial wood sections from Pinus radiata. Holzforschung 48:168–172

    Article  Google Scholar 

  • Evans R (1999) A variance approach to the X-ray diffractometric estimation of microfibril angle in wood. Appita J 52(283–289):294

    Google Scholar 

  • Evans R (2006) Characterization of the cellulosic cell wall. Stokke DG, Groom L (ed) pp 138–146. Blackwell Publishing, Ames, IA, USA

  • Fernandes A, Lousada J, Morais J, Xavier J, Pereira J, Melo-Pinto P (2013) Measurement of intra-ring wood density by means of imaging VIS/NIR spectroscopy (hyperspectral imaging). Holzforschung 67(1):59–65

    Article  CAS  Google Scholar 

  • Greaves BL, Borralho NMG (1996) The influence of basic density and pulp yield on the cost of eucalypt kraft pulping: a theoretical model for tree breeding. Appita J 49(2):90–95

    CAS  Google Scholar 

  • Ho TX, Schimleck LR, Sinha A (2021) Utilization of genetic algorithms to optimize Eucalyptus globulus pulp yield models based on NIR spectra. Wood Sci Technol 55(3):757–776

    Article  CAS  Google Scholar 

  • Koljonen J, Nordling TEM, Alander JT (2008) A review of genetic algorithms in near-infrared spectroscopy and chemometrics: past and future. J Near Infrared Spectrosc 16:189–197

    Article  CAS  Google Scholar 

  • Li Y, Via BK, Young T, Li Y (2019) Visible-near infrared spectroscopy and chemometric methods for wood density prediction and origin/species identification. Forests 10:1078

    Article  Google Scholar 

  • Jones PD, Schimleck LR, Peter GF, Daniels RF, Clark A (2005a) 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

    Article  Google Scholar 

  • Jones PD, Schimleck LR, Peter GF, Daniels RF, Clark A (2005b) Non-destructive estimation of Pinus taeda L. tracheid morphological characteristics for samples from a wide range of sites in Georgia. Wood Sci Technol 39:529–545

    Article  CAS  Google Scholar 

  • Jordan L, Clark A, Schimleck LR, Hall DB, Daniels RF (2008) Regional variation in wood specific gravity of planted loblolly pine in the United States. Can J for Res 38:698–710

    Article  Google Scholar 

  • Kellogg RM, Sastry CBR, Wellwood RW (1975) Relationships between cell-wall composition and cell-wall density. Wood Fiber Sci 7(3):170–177

    Google Scholar 

  • Ma T, Inagaki T, Tsuchikawa S (2017) Calibration of SilviScan data of Cryptomeria japonica wood concerning density and microfibril angles with NIR hyperspectral imaging with high spatial resolution. Holzforschung 71:341–347

    Article  CAS  Google Scholar 

  • Ma T, Inagaki T, Tsuchikawa S (2018) Non-destructive evaluation of wood stiffness and fiber coarseness, derived from SilviScan data, via near infrared hyperspectral imaging. J Near Infrared Spectrosc 26:398–405

    Article  Google Scholar 

  • Mora C, Schimleck LR (2010) Kernel regression methods for the prediction of wood properties of Pinus taeda using near infrared (NIR) spectroscopy. Wood Sci Technol 44(4):561–578

    Article  CAS  Google Scholar 

  • Nabavi M, Dahlen J, Schimleck L, Eberhardt TL, Montes C (2018) A regional calibration model for predicting loblolly pine tracheid properties using near-infrared spectroscopy. Wood Sci Technol 52(2):445–463

    Article  CAS  Google Scholar 

  • Nasir V, Nourian S, Zhou Z, Rahimi S, Avramidis S, Cool J (2019) Classification and characterization of thermally modified timber using visible and near-infrared spectroscopy and artificial neural networks: a comparative study on the performance of different NDE methods and ANNs. Wood Sci Technol 53(5):1093–1109

    Article  CAS  Google Scholar 

  • Schimleck LR, Tsuchikawa S (2021) Application of NIR spectroscopy to wood and wood derived products (Chapter 37). In: Ciurczak E, Igne B, Workman J, Burns D (eds) The handbook of near-infrared analysis, fourth edition, newly revised and expanded. CRC Press, Boca Raton, FL, pp 759–780

    Chapter  Google Scholar 

  • Schimleck LR, Evans R, Ilic J (2001) Estimation of Eucalyptus delegatensis clear wood properties by near infrared spectroscopy. Can J For Res 31(10):1671–1675

    Article  Google Scholar 

  • Schimleck LR, Kube PD, Raymond CA, Michell AJ, French J (2006a) Estimation of whole-tree kraft pulp yield of Eucalyptus nitens using near infrared spectra collected from increment cores. Can J For Res 35(12):2797–2805

    Article  Google Scholar 

  • Schimleck LR, Kube PD, Raymond CA, Michell AJ, French J (2006b) Extending near infrared reflectance (NIR) pulp yield calibrations to new sites and species. J Wood Chem Technol 26(4):299–311

    Article  CAS  Google Scholar 

  • Schimleck LR, Mora CR, Jordan L, White DE, Courchene CE, Purnell RC (2009) Determination of within-tree variation of Pinus taeda wood properties by near infrared spectroscopy. Part 1: development of multiple height calibrations. Appita J 62:130–136

    CAS  Google Scholar 

  • Schimleck L, Apiolaza L, Dahlen J, Downes G, Emms G, Evans R, Moore J, Pâques L, Van den Bulcke J, Wang X (2019) Non-destructive evaluation techniques and what they tell us about wood property variation. Forests 10:728

    Article  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 19:287–308

    Article  CAS  Google Scholar 

  • Snee R (1977) Validation of regression models: methods and examples. Technometrics 19:415–428. https://doi.org/10.2307/1267881

    Article  Google Scholar 

  • Tsuchikawa S, Kobori H (2015) A review of recent application of near infrared spectroscopy to wood science and technology. J Wood Sci 61(3):213–220

    Article  CAS  Google Scholar 

  • Villar A, Fernandez S, Gorritxategi E, Ciria JI, Fernandez LA (2014) Optimization of the multivariate calibration of a Vis-NIR sensor for the on-line monitoring of marine diesel engine lubricating oil by variable selection methods. Chemometr Intell Lab Syst 130:68–75

    Article  CAS  Google Scholar 

  • Wright JA, Birkett MD, Gambino MJT (1990) Prediction of pulp yield and cellulose content from wood samples using near-infrared reflectance spectroscopy. Tappi J 73(8):164–166

    CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Wood Science and Engineering, College of Forestry, Oregon State University, Corvallis, OR, 97331, USA

    Tu X. Ho, Laurence R. Schimleck & Arijit Sinha

  2. Warnell School of Forestry and Natural Resources, University of Georgia, 180 E Green St, Athens, GA, 30602, USA

    Joseph Dahlen

Authors
  1. Tu X. Ho
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Laurence R. Schimleck
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Joseph Dahlen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Arijit Sinha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tu X. Ho.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ho, T.X., Schimleck, L.R., Dahlen, J. et al. Utilization of genetic algorithms to optimize loblolly pine wood property models based on NIR spectra and SilviScan data. Wood Sci Technol 56, 1419–1437 (2022). https://doi.org/10.1007/s00226-022-01403-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00226-022-01403-z

Search

Navigation