Fast online near infrared technique to predict modulus of elasticity and moisture content of sawn lumber

Wood products are required high degrees of structural performance and reliability in case of construction material. However, mechanical properties and moisture content of wood vary widely because of its highly anisotropic characteristics. Therefore, accurate nondestructive grading systems are desirable for wood industries. We developed a fast online grading apparatus for sawn lumber based on near-infrared (NIR) spectroscopy that utilizes a novel wavelength dispersive NIR spectrophotometer equipped with a diffraction grating linear sensor and high intensity lighting. This device makes it possible to acquire spectra from the entire surface of wood lumber running on a conveyor belt at a speed of 120 m/min. Predictive models for modulus of elasticity (MOE) and moisture content (MC) were developed from the NIR spectra with the aid of partial least squares regression (PLSR) analysis. The MOE and MC predictive models demonstrated sufficient levels of prediction accuracy for use on high speed conveyor belts. The developed device could be utilized for the online quality certification of sawn lumber in commercial sawmills.