Startseite The relationship between color and mechanical properties of heat-treated wood predicted based on support vector machines model
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The relationship between color and mechanical properties of heat-treated wood predicted based on support vector machines model

  • Shuang Chen , Jiapeng Wang , Yanxu Liu , Zhangjing Chen , Yafang Lei ORCID logo und Li Yan ORCID logo EMAIL logo
Veröffentlicht/Copyright: 14. Oktober 2022
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Abstract

Thermal modification or heat treatment can cause the loss of mechanical property of wood. In this study, Poplar (Populus tomentosa Carr.) and spruce (Picea obies Mast.) were heat treated at 180, 200, and 220 °C for 2–10 h. Changes of color (L*, a* and b*) and mechanical strength including modulus of elasticity (MOE), modulus of rupture (MOR) and shear strength after heat treatment were analyzed. Time-temperature superposition methods were used to quantify color and mechanical strength. The prediction models of MOR, MOE and shear strength were assessed with support vector regression model (SVR) based on color parameters. The trends of color change and mechanical strength after heat treatment were highly consistent. The values of apparent activation energy (E a ) calculated from color parameters (110.6–187.2 kJ/mol) were identical to those from mechanical strengths (103.2–219.2 kJ/mol). Color parameters were used as input variables, and the MOE, MOR, and shear strength were output parameters in the established SVR model. Gaussian radial basis function (RBF) was found to be a kernel function for SRV model. Optimal hyperparameters in SVR model were obtained using cross-validation and grid search. The determination coefficients for MOE, MOR, and shear strength were 0.903, 0.835, and 0.865, respectively for poplar. The high correlation suggested that wood mechanical strength can be predicted non-destructively through measuring color parameters after heat treatment.


Corresponding author: Li Yan, Department of Wood Science and Technology, Forestry College, Northwest A & F University, Yangling, Shaanxi 712100, China, E-mail:
Shuang Chen and Jiapeng Wang contributed equally to this article.

Funding source: Fundamental Research Funds for the Central Universities

Award Identifier / Grant number: 2452019057

Award Identifier / Grant number: 31971590

  1. Author contributions: Shuang Chen and Jiapeng Wang: methodology, investigation, writing – original draft, data curation. Yanxu Liu: formal analysis, software and visualization. Zhangjing Chen: writing – review & editing. Yafang Lei: supervision and validation. Li Yan: conceptualization, resources, supervision, funding acquisition, project administration. All the authors have approved the submission for publication.

  2. Research funding: This study was supported by the National Natural Science Foundation of China (31971590) and Fundamental Research Funds for the Central Universities (2452019057).

  3. Conflict of interest statement: The authors declare that they have no conflicts of interest regarding this article.

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/hf-2022-0075).


Received: 2022-05-02
Accepted: 2022-09-28
Published Online: 2022-10-14
Published in Print: 2022-12-16

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