Wood Moisture Content Determination by Handheld Near-Infrared Reflectance Spectrometer

Abstract

Rapid, accurate determination of wood moisture content is paramount for the wood industry, infrastructure maintenance, studies of plant physiology, and forest management. Near-IR reflectance spectroscopy (NIRS) is a widely used nondestructive technique for analyzing the properties of materials, including MC. Small, portable, handheld NIR spectrometers represent an emerging technology with strong potential for rapidly, affordably estimating materials properties. Here, we used a SCiO^TM miniature handheld NIR spectrometer and a partial least squares regression model to predict wood MC. 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 MC determined by oven-drying. The calibration and prediction datasets contained 2/3rd and 1/3rd of all data, respectively. We explored the effects of different spectral preprocessing algorithms (ie 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, RMSE_P: 0.32, the ratio of performance to deviation: 2.2). Broadly, we demonstrated that relatively low-cost miniature handheld NIR spectrometers such as the SCiO^TM can rapidly estimate percent MC in the wood of various species.

Citation

Thapa, Sandesh; Li, Ling; Rubert-Nason, Kennedy; Wang, Jinwu; Mirtes, Colter; Brown, Taylor; Pelletier, Eve; Zhang, Yong-Jiang. 2024. Wood Moisture Content Determination by Handheld Near-Infrared Reflectance Spectrometer. Wood and Fiber Science. 56(4): 197-206. https://doi.org/10.22382/wfs-2024-18.