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Quality Control of Thermally Modified Western Hemlock Wood Using Near-Infrared Spectroscopy and Explainable Machine

Vahid Nasir1, Laurence Schimleck1, Farshid Abdoli2

  • 1Department of Wood Science Engineering, Oregon State University, Corvallis, OR 97331, USA.

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|October 28, 2023
PubMed
Summary
This summary is machine-generated.

Near-infrared (NIR) spectroscopy combined with machine learning accurately classifies thermally modified wood. Wood color changes significantly impact NIR reflectance, improving classification accuracy for heat treatment intensity assessment.

Keywords:
ensemble learningfeature selectiongradient boosting machinenear-infrared (NIR) spectroscopyneural networksnondestructive evaluation (NDE)thermally treated timberwood modification

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Area of Science:

  • Materials Science
  • Wood Science
  • Spectroscopy

Background:

  • Quality control of thermally modified wood is crucial.
  • Nondestructive testing methods are needed to assess heat treatment intensity.
  • Near-infrared (NIR) spectroscopy offers potential for wood characterization.

Purpose of the Study:

  • To classify thermally modified wood using NIR spectroscopy and machine learning.
  • To identify critical NIR wavelengths for distinguishing heat treatment intensities.
  • To develop an explainable machine learning framework for wood quality control.

Main Methods:

  • Collected NIR spectra from untreated and thermally treated western hemlock samples (170°C, 212°C, 230°C).
  • Employed a TreeNet gradient boosting machine for classification without dimensionality reduction.
  • Analyzed feature importance to understand critical wavelengths and their contribution to model performance.

Main Results:

  • Achieved high classification accuracies (94.35% with 1100-2500 nm range).
  • Identified wood color changes as a primary driver of NIR reflectance variation among treatments.
  • Found that models incorporating wood color information were more accurate than those focusing solely on water or wood chemistry.

Conclusions:

  • The developed NIR spectroscopy and machine learning framework effectively classifies thermally modified wood.
  • Wood color is a key indicator for assessing heat treatment intensity via NIR.
  • This approach provides valuable insights for wood characterization and quality control applications using NIR spectroscopy.