WOOD MOISTURE CONTENT DETERMINATION BY HANDHELD NEAR-INFRARED REFLECTANCE SPECTROMETER

Authors

  • Sandesh Thapa University of Maine
  • Ling Li School of Forest Resources, University of Maine, Orono ME 04469
  • Kennedy Rubert-Nason Division of Natural Sciences, University of Maine – Fort Kent, 23 University Drive, Fort Kent, ME, 04743, USA
  • Jinwu Wang Forest Products Laboratory, U.S. Forest Service, 1 Gifford Pinchot Drive, Madison, WI 53726 USA
  • Colter Mirtes Division of Natural Sciences, University of Maine – Fort Kent, 23 University Drive, Fort Kent, ME, 04743, USA
  • Taylor Brown Division of Natural Sciences, University of Maine – Fort Kent, 23 University Drive, Fort Kent, ME, 04743, USA
  • Eve Pelletier Division of Natural Sciences, University of Maine – Fort Kent, 23 University Drive, Fort Kent, ME, 04743, USA
  • Yongjiang Zhang School of Biology and Ecology, University of Maine, Orono ME 04469 USA

Abstract

Rapid, accurate determination of wood moisture content is of paramount importance for the wood industry, infrastructure maintenance, studies of plant physiology, and forest management. Near-infrared reflectance spectroscopy (NIRS) is a widely used non-destructive technique for analyzing properties of materials, including moisture content. Small, portable, handheld NIR spectrometers represent an emerging technology with strong potential for rapidly, affordably estimating materials properties. Here, we used a SCiOTM miniature handheld NIR spectrometer and a partial least squares (PLS) regression model to predict wood moisture content.  The model was developed using spectra (740-1070 nm) collected from increment borer wood samples from 41 representative softwood and hardwood trees, calibrated against gravimetric wood moisture content determined by oven-drying. The calibration and prediction datasets contained 2/3rd and 1/3rd of all data, respectively. We explored effects of different spectral preprocessing algorithms (i.e., first and second-order derivatives and standard normal variate transformations) on model performance. First-order derivative spectra with five latent variables yielded the most robust model (R2: 0.72, RMSEP: 0.32, ratio of performance to deviation: 2.2). Broadly, we demonstrated that relatively low-cost miniature handheld NIR spectrometers such as the SCiOTM can rapidly estimate percent moisture content in wood of various species.

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Published

2024-12-16

Issue

Vol. 56 No. 4 (2024): Wood and Fiber Science

Section

Research Contributions

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