Skip to main content
SpringerLink
Log in

Classification of wood using differential thermogravimetric analysis

  • Published:
Journal of Thermal Analysis and Calorimetry Aims and scope Submit manuscript

Abstract

The aim of this study is to propose an alternative methodology to classify wood species using the first (DTG), second (2DTG), and third (3DTG) derivatives of the thermogravimetric curves (TG). Accordingly, the main contribution of this new procedure consists on classifying materials (wood) taking into account the mass loss rate and acceleration with respect to temperature. In our research, each TG curve is firstly smoothed using the local polynomial regression estimator, and the first, second, and third derivatives are estimated. The application of the local polynomial regression estimator provides a reliable way to obtain the TG derivatives, overcoming the noise problem in the TG derivative estimation. Then, using these estimated curves, the different wood classes are discriminated employing a nonparametric functional data analysis (NPFDA) technique, based on the Bayes rule and the Nadaraya-Watson regression estimator, and also novel functional generalized additive models (GAM). The latter allows to classify materials using simultaneously more than one type of thermal curves. The results are compared with those obtained using classical and machine learning multivariate supervised classification methods, such as Linear discriminant analysis, Quadratic classification, Naïve Bayes, Logistic regression, \(k\) Nearest neighbors, Neural networks, and Support vector machines. A regression model consisting of the mixture of the first derivatives of four generalized logistic components, one per principal wood constituent (water, hemicellulose, cellulose, and lignin), is applied to fit the DTG curves. The resulting 16 parameters from this fit characterize each curve and are used as datasets to apply the multivariate supervised classification methods. The use of the TG derivatives jointly with the TG curves has proved to be an optimal discriminating feature, when the new functional GAM techniques are employed.

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

Similar content being viewed by others

Abbreviations

DTG:

First derivative of the thermogravimetric curve

2DTG:

Second derivative of the thermogravimetric curve

3DTG:

Third derivative of the thermogravimetric curve

TG:

Thermogravimetric curve

NPFDA:

Nonparametric functional data analysis

GAM:

Generalized additive models

FTR:

Fourier transform Raman

NN:

Neural networks

\(k\)-NN:

\(k\) nearest neighbor

SVM:

Support vectors machines

PDSC:

Pressure differential scanning calorimetry

LDA:

Linear discriminant analysis

NBC:

Naïve Bayes classifier

FDA:

Functional data analysis

K-NPFDA:

Nonparametric functional data analysis based on kernel methods

IRLS:

Iteratively reweighted least-squares

ASE:

Average squared error

MDS:

Multidimensional scaling

GLM:

Generalized linear model

References

  1. Lewis IR, Daniel NW, Chaffin NC, Griffiths PR. Raman spectrometry and neural networks for the classification of wood types-1. Spectrochim Acta A, Mol Biomol Spectrosc. 1994;50:1943–58.

    Article  Google Scholar 

  2. Yang H, Lewis IR, Griffiths PR. Raman spectrometry and neural networks for the classification of wood types. 2. Kohonen self-organizing maps. Spectroc Acta Part A. 1999;55:2783–91.

    Article  Google Scholar 

  3. Piuri V, Scotti F. Design of an automatic wood types classification system by using fluorescence spectra. IEEE Trans Syst Man Cybern Part C-Appl Rev. 2010;40:358–66.

    Article  Google Scholar 

  4. Mallik A, Tarrío-Saavedra J, Francisco-Fernández M, Naya S. Classification of wood micrographs by image segmentation. Chemometrics Intell Lab Syst. 2011;107:351–62.

    Article  CAS  Google Scholar 

  5. Bremananth R, Nithya B, Saipriya R. A wood species recognition system. Int J Intell Syst Technol. 2009;4:54–60.

    Google Scholar 

  6. Wang HJ, Zhang GQ, Qi HN. Wood recognition using image texture features. PLoS One. 2013;8(e76):101.

  7. Rojas JAM, Alpuente J, Postigo D, Rojas IM, Vignote S. Wood species identification using stress-wave analysis in the audible range. Appl Acoust. 2001;72:934–42.

    Article  Google Scholar 

  8. Budrugeac P, Emandi A. The use of thermal analysis methods for conservation state determination of historical and/or cultural objects manufactured from lime tree wood. J Therm Anal Calorim. 2010;101:881–6.

    Article  CAS  Google Scholar 

  9. Cavallaro G, Donato DI, Lazzara G, Milioto S. A comparative thermogravimetric study of waterlogged archaeological and sound woods. J Therm Anal Calorim. 2011;104:451–7.

    Article  CAS  Google Scholar 

  10. Francisco-Fernández M, Tarrío-Saavedra J, Mallik A, Naya S. A comprehensive classification of wood from thermogravimetric curves. Chemometrics Intell Lab Syst. 2012;118:159–72.

    Article  Google Scholar 

  11. Tarrío-Saavedra J, Naya S, Francisco-Fernández M, López-Beceiro J, Artiaga R. Functional nonparametric classification of wood species from thermal data. J Therm Anal Calorim. 2011;104:87–100.

    Article  Google Scholar 

  12. Tarrío-Saavedra J, Francisco-Fernández M, Naya S, López-Beceiro J, Artiaga R. Wood identification using pressure DSC data. J Chem. 2013;27:475–87.

    Article  Google Scholar 

  13. Wendlandt WW, Gallagher PK. Instrumentation. In: Turi EA, editor. Recent advances in functional data analysis and related topics. New York: Academic Press; 1981.

    Google Scholar 

  14. Prime RB, Bair HE, Gallagher PK, Riga A. Thermogravimetric analysis (TGA). In: Menczel JD, Prime RB, editors. Thermal analysis of polymers fundamentals and applications. San José: Wiley; 2009.

    Google Scholar 

  15. Diblasi C. Modeling chemical and physical processes of wood and biomass pyrolysis. Prog Energy Combust Scil. 2008;34:47–90.

    Article  CAS  Google Scholar 

  16. Gronli MG, Várhegyi G, Blasi C. Thermogravimetric analysis and devolatilization kinetics of wood. Ind Eng Chem Res. 2002;41:4201–8.

    Article  CAS  Google Scholar 

  17. Sebio-Puñal T, Naya S, Lopez-Beceiro J, Tarrio-Saavedra J, Artiaga R. Thermogravimetric analysis of wood, holocellulose, and lignin from five wood species. J Therm Anal Calorim. 2012;109:1163–7.

    Article  Google Scholar 

  18. Alén R, Kuoppala E, Pia O. Formation of the main degradation compound groups from wood and its components during pyrolysis. J Anal Appl Pyrolysis. 1996;36:137–48.

    Article  Google Scholar 

  19. Gašparovič L, Koreňová Z, Jelemenský L. Kinetic study of wood chips decomposition by TGA. In: Proceedings 36th international conference of SSCHE. Tatranské Matliare, World Scientific; 2009, vol 178, pp. 1–14.

  20. Müller-Hagedorn M, Bockhorn H, Krebs L, Müller U. A comparative kinetic study on the pyrolysis of three different wood species. J Anal Appl Pyrolysis. 2003;68–69:231–49.

    Article  Google Scholar 

  21. Raveendran K, Ganesh A, Khilar KC. Pyrolysis characteristics of biomass and biomass components. Fuel. 1996;75:987–98.

    Article  CAS  Google Scholar 

  22. Roberts AF. A review of kinetics data for the pyrolysis of wood and related substances. Combust Flame. 1970;14:261–72.

    Article  CAS  Google Scholar 

  23. Wang S, Wang K, Liu Q, Gu Y, Luo Z, Cen K, Fransson T. Comparison of the pyrolysis behavior of lignins from different tree species. Biotechnol Adv. 2009;27:562–7.

    Article  CAS  Google Scholar 

  24. Yang H, Yan R, Chen H, Lee DH, Zheng C. Characteristics of hemicellulose, cellulose and lignin pyrolysis. Fuel. 2007;86:1781–8.

    Article  CAS  Google Scholar 

  25. Mohan D, Pittman JCU, Steele PH. Pyrolysis of wood/biomass for bio-oil: a critical review. Energy Fuel. 2006;20:848–89.

    Article  CAS  Google Scholar 

  26. Dai J, Liu N, Shu L. Applicability of bi-pseudo component separated-stage model for decomposition of lignocellulosic materials in air at multiple heating rates. J Therm Anal Calorim. 2011;104:983–90.

    Article  CAS  Google Scholar 

  27. Cuevas A, Febrero M, Fraiman R. Linear functional regression: the case of fixed design and functional response. Can J Stat. 2002;30:285–300.

    Article  Google Scholar 

  28. Cuevas A, Febrero M, Fraiman R. An anova test for functional data. Comput Stat Data Anal. 2004;47:111–22.

    Article  Google Scholar 

  29. Ferraty F, Romain Y. The Oxford handbook of functional data analysis. Oxford: Oxford University Press; 2010.

    Google Scholar 

  30. Ferraty F, Vieu P. The functional nonparametric model and application to spectrometric data. Comput Stat. 2002;17:545–64.

    Article  Google Scholar 

  31. Ferraty F, Vieu P. Curves discrimination: a nonparametric functional approach. Comput Stat. 2003;44:161–73.

    Google Scholar 

  32. Ferraty F, Vieu P. Nonparametric functional data analysis: theory and practice. New York: Springer-Verlag; 2006.

    Google Scholar 

  33. Locantore N, Marron JS, Simpson DG, Tripoli N, Zhang JT, Cohen KL. Robust principal component analysis for functional data (with discussion). Test. 1999;8:1–74.

  34. Naya S, Francisco-Fernández M, Tarrío-Saavedra J, López-Beceiro J, Artiaga R. Application of functional data analysis to material science. In: Ferraty F, editor. Recent advances in functional data analysis and related topics. Berlin Heilderberg: Springer-Verlag; 2011.

    Google Scholar 

  35. Ramsay JO, Silverman BW. Functional data analysis. New York: Springer-Verlag; 2005.

    Book  Google Scholar 

  36. Tarrío-Saavedra J, Naya S, Francisco-Fernández M, López-Beceiro J, Artiaga R. Application of functional ANOVA to the study of thermal stability of micro-nano silica epoxy composites. Chemometrics Intell Lab Syst. 2011;105:114–24.

    Article  Google Scholar 

  37. R Development Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; 2011. http://www.R-project.org

  38. Febrero M, Oviedo M. fda.usc: The functional package: summary information. R package version 1.0; 2009.

  39. Febrero-Bande M, González-Manteiga W. Generalized additive models for functional data. Test. 2013;22:278–92.

    Article  Google Scholar 

  40. Wood S. Generalized additive models: an introduction with R. Boca Raton: Chapman and Hall/CRC; 2006.

    Google Scholar 

  41. Fisher RA. The use of multiple measurements in taxonomic problems. Ann Eugen. 1936;7:179–88.

    Article  Google Scholar 

  42. Hill T, Lewicki P. Statistics methods and applications. Tulsa: StatSoft; 2007.

    Google Scholar 

  43. Fix E, Hodges JL. Discriminatory analysis, nonparametric discrimination: consistency properties. USAF School of Aviation Medicine, Randolph Field, Texas Technical Report 4; 1951.

  44. Vapnik V. Statistical learning theory. New York: Wiley; 1998.

    Google Scholar 

  45. Ripley BD. Pattern recognition and neural networks. Cambridge: Cambridge University Press; 1996.

    Book  Google Scholar 

  46. López-Granados F, Peña Barragán JM, Jurado-Expósito M, Francisco-Fernández M, Cao R, Alonso-Betanzos A, Fontenla-Romero O. Multispectral classification of grass weeds and wheat (Triticum durum) using linear and nonparametric functional discriminant analysis and neural networks. Weed Res. 2008;48:28–37.

    Article  Google Scholar 

  47. Wehrens R. Chemometrics with R. multivariate data analysis in the natural sciences and life sciences. New York: Springer-Verlag; 2011.

    Google Scholar 

  48. Wand MP, Jones MC. Kernel smoothing. London: Chapman and Hall; 1995.

    Book  Google Scholar 

  49. Ruppert D, Sheather SJ, Wand M. An effective bandwidth selector for local least squares regression. J Am Stat Assoc. 1995;90:1257–70.

    Article  Google Scholar 

Download references

Acknowledgements

This research has been supported by the Spanish Ministry of Science and Innovation, Grant MTM2008-00166 (ERDF included) and Grant MTM2011-22393. The authors wish to express special thanks to Manuel Oviedo for his valuable comments.

Author information

Authors and Affiliations

  1. Departamento de Matemáticas, Facultad de Informática, Universidade da Coruña, A Coruña, Spain

    Mario Francisco-Fernández

  2. Departamento de Matemáticas, Escuela Politécnica Superior, Universidade da Coruña, Ferrol, Spain

    Javier Tarrío-Saavedra & Salvador Naya

  3. Departamento de Ingeniería Industrial II, Escuela Politécnica Superior, Universidade da Coruña, Ferrol, Spain

    Jorge López-Beceiro & Ramón Artiaga

Authors
  1. Mario Francisco-Fernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Javier Tarrío-Saavedra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Salvador Naya
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jorge López-Beceiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ramón Artiaga
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mario Francisco-Fernández.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Francisco-Fernández, M., Tarrío-Saavedra, J., Naya, S. et al. Classification of wood using differential thermogravimetric analysis. J Therm Anal Calorim 120, 541–551 (2015). https://doi.org/10.1007/s10973-014-4260-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10973-014-4260-y

Keywords

Search

Navigation