Skip to main content
SpringerLink
Log in

Near-infrared-based models for lignin syringyl/guaiacyl ratio of Eucalyptus benthamii and E. pellita using a streamlined thioacidolysis procedure as the reference method

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

Abstract

Lignin syringyl/guaiacyl (S/G) ratio is a key trait in the cellulose pulping industry. Near-infrared (NIR) spectroscopy data collected with a bench and a portable instrument were used to develop prediction models for S/G ratio from sawdust samples of 1220 trees of two contrasting Eucalyptus species, namely E. benthamii (n = 484), which is a temperate, cold-resistant species, and E. pellita (n = 736), which is a heat- and disease-resistant tropical species. For each species, samples were selected based on maximal NIR spectral variation and analyzed for S/G using a streamlined thioacidolysis method with minimal sample input. NIR models were developed for each species separately and jointly. Instead of just using the coefficient of determination (R2) and the ratio of performance to deviation (RPD), the Spearman’s rank correlation (rs) and the average of the coefficient of correlation between the references and predicted values (CVRP) were taken into account to evaluate the models. The bench spectrometer had a better performance than the portable instrument (R2 from 0.77 to 0.86 versus 0.31 to 0.47). Species-specific NIR models were better for E. benthamii (rs = 0.89, CVRP = 2.75%, R2 = 0.86 and RPD = 1.8), while the joint-species model was better for E. pellita (rs = 0.97, CVRP = 3.98%, R2 = 0.82 and RPD = 2.1). These NIR models should prove useful for high-throughput wood phenotyping in advanced breeding programs.

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

Similar content being viewed by others

References

  • Alves A, Simoes R, Stackpole DJ et al (2011) Determination of the syringyl/guaiacyl ratio of Eucalyptus globulus wood lignin by near infrared-based partial least squares regression models using analytical pyrolysis as the reference method. J Near Infrared Spectrosc 19:343–348. https://doi.org/10.1255/jnirs.946

    Article  CAS  Google Scholar 

  • Baillères H, Davrieux F, Ham-pichavant F (2002) Near infrared analysis as a tool for rapid screening of some major wood characteristics in a Eucalyptus breeding program. Ann For Sci 59:479–490. https://doi.org/10.1051/forest:2002032

    Article  Google Scholar 

  • del Río JC, Gutiérrez A, Rodríguez IM, Ibarra D, Martinez AT (2007) Composition of non-woody plant lignins and cinnamic acids by Py-GC/MS, Py/TMAH and FT-IR. J Anal Appl Pyrolysis 79:39–46. https://doi.org/10.1016/j.jaap.2006.09.003

    Article  CAS  Google Scholar 

  • Diniz CP, Grattapaglia D, de Alencar Figueiredo LF (2019) Comparative performance of bench and portable near infrared spectrometers for measuring wood samples of two Eucalyptus species (E. pellita and E. benthamii). In: Engelsen SB, Sørensen KM, van der Berg F (eds) 18th international conference near infrared spectroscopy. IM Publications Open, Chichester, pp 31–38. https://doi.org/10.1255/nir2017.031

  • Esbensen K, Geladi P, Larsen A (2014) The RPD myth. NIR News 25:24–28. https://doi.org/10.1255/nirn.1462

    Article  Google Scholar 

  • Fearn T (2014) The overuse of R2. NIR News 25:32. https://doi.org/10.1255/nirn.1464

    Article  Google Scholar 

  • Govender M, Bush T, Spark A, Bose SK, Francis RC (2009) An accurate and non-labor intensive method for the determination of syringyl to guaiacyl ratio in lignin. Bioresour Technol 100:5834–5839. https://doi.org/10.1016/j.biortech.2009.06.009

    Article  CAS  PubMed  Google Scholar 

  • Harwood CE, Alloysius D, Pomroy P, Robson KW, Haines NW (1997) Early growth and survival of Eucalyptus pellita provenances in a range of tropical environments, compared with E. grandis, E. urophylla and Acacia mangium. New For 14:203–219

    Article  Google Scholar 

  • Hein PRG, Lima JT, Chaix G (2009) Robustness of models based on near infrared spectra to predict the basic density in Eucalyptus urophylla wood. J Near Infrared Spectrosc 17:141–150. https://doi.org/10.1255/jnirs.833

    Article  CAS  Google Scholar 

  • Hein PRG, Lima JT, Chaix G (2010) Effects of sample preparation on NIR spectroscopic estimation of chemical properties of Eucalyptus urophylla S.T. Blake wood. Holzforschung 64:45–54. https://doi.org/10.1515/HF.2010.011

    Article  CAS  Google Scholar 

  • Higa RCV, Pereira JCD (2003) Usos potenciais do Eucalyptus benthamii Maiden et Cambage [Potential uses of Eucalyptus benthamii Maiden et Cambage]. Colombo: Embrapa Florestas, Comunicado Técnico 100, 4p. ISSN 1517-5030

  • Hodge GR, Acosta JJ, Unda F, Woodbridge WC, Mansfield SD (2018) Global near infrared spectroscopy models to predict wood chemical properties of Eucalyptus. J Near Infrared Spectrosc 26:117–132. https://doi.org/10.1177/0967033518770211

    Article  CAS  Google Scholar 

  • Khaled RAH, Duru M, Decruyenaere V, Jouany C, Cruz P (2006) Using leaf traits to rank native grasses according to their nutritive value. Rangel Ecol Manag 59:648–654. https://doi.org/10.2111/05-031R2.1

    Article  Google Scholar 

  • Lima BM de (2014) Bridging genomics and quantitative genetics of Eucalyptus: genome-wide prediction and genetic parameter estimation for growth and wood properties using high-density SNP data. Doctoral thesis, Universidade de São Paulo, p 93. https://doi.org/10.11606/t.11.2014.tde-25062014-085814

  • Lupoi JS, Singh S, Davis M et al (2014) High-throughput prediction of eucalypt lignin syringyl/guaiacyl content using multivariate analysis: a comparison between mid-infrared, near-infrared, and Raman spectroscopies for model development. Biotechnol Biofuels 7:1–14. https://doi.org/10.1186/1754-6834-7-93

    Article  CAS  Google Scholar 

  • Milagres FR (2013) Espectroscopia de infravermelho próximo para predição de propriedades da madeira de híbridos de Eucalyptus spp. [Near infrared spectroscopy for prediction of Eucalyptus spp. hybrids wood properties]. Doctoral thesis, Universidade Federal de Viçosa, 96 p. http://locus.ufv.br/handle/123456789/588

  • Müller BSF, Neves LG, de Almeida Filho JE et al (2017) Genomic prediction in contrast to a genome-wide association study in explaining heritable variation of complex growth traits in breeding populations of Eucalyptus. BMC Genom 18:524. https://doi.org/10.1186/s12864-017-3920-2

    Article  Google Scholar 

  • Myburg AA, Potts BB, Marques CM et al (2007) Eucalyptus. In: Kole C (ed) Forest trees, 1st edn. Springer, Berlin, pp 115–160

    Chapter  Google Scholar 

  • Pereira JCD, Sturion JA, Higa AR, et al (2000) Características da madeira de algumas espécies de eucalipto plantadas no Brasil [Wood characteristics of some Eucalyptus species planted in Brazil]. Colombo: Embrapa Florestas, Documentos 30, 113 p

  • Ramadevi P, Hegde DV, Varghese M et al (2016) Evaluation of lignin syringyl/guaiacyl ratio in Eucalyptus camaldulensis across three diverse sites based on near infrared spectroscopic calibration modelling with five Eucalyptus species and its impact on kraft pulp yield. J Near Infrared Spectrosc 24:529–536. https://doi.org/10.1255/jnirs.1251

    Article  CAS  Google Scholar 

  • Raymond CA, Schimleck LR (2002) Development of near infrared reflectance analysis calibrations for estimating genetic parameters for cellulose content in Eucalyptus globulus. Can J For Res 32:170–176. https://doi.org/10.1139/X01-174

    Article  Google Scholar 

  • Robinson AR, Mansfield SD (2009) Rapid analysis of poplar lignin monomer composition by a streamlined thioacidolysis procedure and near-infrared reflectance-based prediction modeling. Plant J 58:706–714. https://doi.org/10.1111/j.1365-313X.2009.03808.x

    Article  CAS  PubMed  Google Scholar 

  • Stackpole DJ, Vaillancourt RE, Alves A, Rodrigues J, Potts BM (2011) Genetic variation in the chemical components of Eucalyptus globulus wood. Genes Genomes Genet G3(1):151–159. https://doi.org/10.1534/g3.111.000372

    Article  Google Scholar 

  • Viana LC, Trugilho PF, Hein PRG et al (2010) Modelos de calibração e a espectroscopia no infravermelho próximo para predição das propriedades químicas e da densidade básica da madeira de Eucalyptus (Calibration models and near infrared spectroscopy for predicting chemical properties and basic density). Cienc Florest 20:367–376. https://doi.org/10.5902/198050981859

    Article  Google Scholar 

  • Weng JK, Chapple C (2010) The origin and evolution of lignin biosynthesis. New Phytol 187:273–285. https://doi.org/10.1111/j.1469-8137.2010.03327.x

    Article  CAS  PubMed  Google Scholar 

  • Williams P (2014) Tutorial: The RPD statistic: a tutorial note. NIR news 25:22. https://doi.org/10.1255/nirn.1419

    Article  Google Scholar 

  • Zeaiter M, Roger JM, Bellon-Maurel V (2005) Robustness of models developed by multivariate calibration. Part II: The influence of pre-processing methods. Trends Anal Chem 24:437–445. https://doi.org/10.1016/j.trac.2004.11.023

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank the Laboratory of Forest Products (Brazilian Forest Service) for the availability of the MicroNIR 1700 acquired through Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Project no. 473936/2013-5) and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior for the master’s degree scholarship for CPD. We also acknowledge Embrapa Forestry Center for providing the raw wood samples. This work was also supported by PRONEX-FAPDF Grant 2009/00106-8 ‘NEXTREE,’ CNPq Grant 400663/2012-0 and Embrapa Grant 03.11.01.007.00.00 to DG. DG received a research fellowship from CNPq.

Author information

Authors and Affiliations

  1. Botany Department, Biology Institute, University of Brasília, CP: 04457, Brasília, DF, 70910-900, Brazil

    Carolina Pinto Diniz & Lúcio Flávio de Alencar Figueiredo

  2. Plant Genetics Laboratory, EMBRAPA Genetic Resources and Biotechnology, Brasília, DF, 70770-910, Brazil

    Dario Grattapaglia

  3. Graduate Program in Genomic Sciences and Biotechnology, Catholic University of Brasília, SGAN 916 Modulo B, Brasília, DF, 70790-160, Brazil

    Dario Grattapaglia

  4. Department of Wood Science, University of British Columbia, 4030-2424 Main Mall, Vancouver, BC, V6T 1Z4, Canada

    Shawn D. Mansfield

Authors
  1. Carolina Pinto Diniz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dario Grattapaglia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shawn D. Mansfield
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lúcio Flávio de Alencar Figueiredo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lúcio Flávio de Alencar Figueiredo.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is 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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Diniz, C.P., Grattapaglia, D., Mansfield, S.D. et al. Near-infrared-based models for lignin syringyl/guaiacyl ratio of Eucalyptus benthamii and E. pellita using a streamlined thioacidolysis procedure as the reference method. Wood Sci Technol 53, 521–533 (2019). https://doi.org/10.1007/s00226-019-01090-3

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00226-019-01090-3

Search

Navigation