Abstract
A fast online grading apparatus for sawn lumber based on near-infrared (NIR) spectroscopy has been developed. The method is based on a novel wavelength dispersive NIR spectrophotometer equipped with a diffraction grating linear sensor and high-intensity lighting. It was possible to acquire spectra from the entire surface of Hinoki cypress lumber sections traveling on a conveyor belt at a speed of 120 m min-1. Additionally, predictive models for moisture content (MC) and modulus of elasticity (MOE) under various MC conditions were developed from the NIR spectra with the aid of partial least squares regression (PLSR) analysis. Both the MC and MOE predictive models demonstrated sufficient levels of prediction accuracy for use on high-speed conveyor belts, and the results of various experiments indicate that the developed device could be applied for the online quality certification of sawn lumber in commercial sawmills.
Acknowledgments
This research was partly supported by the Research and Development Projects for Application in Promoting New Policy of Agriculture, Forestry and Fisheries, Japan (No. 22003) and the Ministry of Education, Science, Sports and Culture (MEXT), Grant-in-Aid for Scientific Research (No. 22248020).
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