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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

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Abstract

Visible and near-infrared (VIS–NIR) spectroscopy was used for classifying and predicting the properties of thermally modified Western hemlock wood. The specimens were treated at 170 °C, 212 °C, and 230 °C. The dimensional reduction was performed using linear discriminant analysis, and the resulted dataset was used for wood classification using the support vector machines and the linear vector quantization neural network. The VIS–NIR dataset was also used to predict the wood moisture content, swelling coefficient, water absorption, density, dynamic modulus of elasticity, and hardness. The “adaptive neuro-fuzzy inference system” (ANFIS), “Group Method of Data Handling” (GMDH), and “multilayer perceptron” (MLP) neural networks were employed for predicting the wood properties. It was shown that regardless of the type of the neural network, NIR dataset provided a robust model with 100% classification accuracy, which can be implemented in industrial scale for in-line timber quality control. The results indicated that the ANFIS and GMDH neural network showed higher performance than the MLP model for predicting the wood properties. While the VIS–NIR data resulted in a promising accuracy for predicting the wood moisture content and dimensional stability parameters, it did not seem suitable for the prediction of wood density and its mechanical properties. The performance of the VIS–NIR spectroscopy method for classification and characterization of heat-treated timber was compared with that obtained using the color measurement and the stress wave method detected by the acoustic emission sensor.

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Abbreviations

EMC:

Equilibrium moisture content (%)

H :

Relative humidity (%)

H l :

Longitudinal hardness

H s :

Side hardness

MOE:

Modulus of elasticity

MOEdyn :

Dynamic modulus of elasticity

MOR:

Modulus of rupture

V :

Stress wave velocity

V 0 :

Oven-dried volume

V s :

Volume after wetting with water

VSC:

Volumetric swelling coefficient (%)

W 0 :

Oven-dry weight

W s :

Weight after soaking in water

WA:

Water absorption (%)

ρ :

Wood density (kg/m3)

AE:

Acoustic emission

ANFIS:

Adaptive neuro-fuzzy inference system

ANN:

Artificial neural network

FCM:

Fuzzy C-mean

FIS:

Fuzzy inference system

GMDH:

Group method of data handling

LDA:

Linear discriminant analysis

LVQ:

Linear vector quantization

MLP:

Multilayer perceptron

MSE:

Mean square error

NDE:

Non-destructive evaluation

NIR:

Near-infrared

VIS–NIR:

Visible and Near-infrared

NN:

Neural network

PLS:

Partial least-squares

SMO:

Sequential minimal optimization

SVM:

Support vector machines

TM:

Thermal modification

TMT:

Thermally treated timber

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Authors and Affiliations

  1. Centre for Advanced Wood Processing, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada

    Vahid Nasir, Sepideh Nourian, Sohrab Rahimi, Stavros Avramidis & Julie Cool

  2. School of Information Engineering, Zhejiang A&F University, Hangzhou, 311300, China

    Zhu Zhou

Authors
  1. Vahid Nasir
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  2. Sepideh Nourian
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  3. Zhu Zhou
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  4. Sohrab Rahimi
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  5. Stavros Avramidis
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  6. Julie Cool
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Correspondence to Vahid Nasir.

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Nasir, V., Nourian, S., Zhou, Z. et al. 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, 1093–1109 (2019). https://doi.org/10.1007/s00226-019-01120-0

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