Evaluation of the spatial variation in moisture content inside wood pieces during drying by NIR spectroscopy

The moisture content (MC) of wood affects its industrial performance, but it is difficult to monitor spatial variations in MC. Here, a multivariate regression was developed to estimate the MC from near infrared (NIR) spectra and was used to monitor the spatial variation in the MC of wood during air- and oven-drying. The spectra and mass of wood pieces were measured at five stages during drying (at each 20% loss of initial water mass). Wood pieces were dried naturally and oven-dried at 60 degrees C. Initially, 25 spectra were recorded at equidistant points covering the entire longitudinal x radial surface of the sample. Then, a planing machine was used to access the inside of the wood, and NIR spectra were measured for each new surface, at a total of 100 points spatially distributed within the wood pieces. The wood pieces were analyzed in their original state, and when they had lost 20, 40, 60, and 80% of their initial water mass. An NIR-based regression (R-p(2) = 0.90 and RMSEP = 10.51%) was applied to estimate the MC, and its spatial gradient during drying was then mapped. These analyses revealed the spatial variation in MC within wood pieces during drying.

Automated summary

A multivariate regression model was developed to estimate the moisture content (MC) of wood using near infrared (NIR) spectra, and was applied to monitor the spatial variation in MC during air- and oven-drying. The wood pieces were analyzed at different stages during drying and NIR-based regression was used to estimate the MC and map its spatial gradient.