Towards Inline Prediction of Color Development for Wood Stained with Chemical Stains Using Near-Infrared Spectroscopy

Abstract

The chemical composition of wood determines the color development when applying chemical stains to the surface of wood. However, different species and individuals from the same species can show variations in the chemical composition, resulting in the risk of nonuniform color development in industrial staining processes between different batches of wood. In the present study, near-infrared (NIR) models were developed to predict wood specimen color development after applying three different concentrations of the chemical stains iron acetate and sodium bicarbonate. The modeling dataset included the NIR spectra of the untreated wood, stain treatment, concentration, and the International Commission on Illumination (CIE) L*a*b* color value before stain application for 210 specimens from five commercial wood species, including red oak (Quercus rubra), white oak (Quercus alba), yellow poplar (Liriodendron tulipifera), southern yellow pine (Pinus spp.), and western red cedar (Thuja plicata). The models were developed by partial least squares regression (PLSR), using 13 different mathematical transformations on the NIR spectra as well as the raw spectral data. Models with single stains and global-species/stain models were developed and compared. The models for iron acetate showed promising results in predicting the color development with the coefficient of determination for cross-validation ( ≥ 0.92), while the models for sodium bicarbonate showed acceptable results with of 0.71 to 0.89. However, a global model including both stains resulted in an unsatisfying prediction of the CIE L*a*b* color values, with of 0.46 to 0.76. The NIR models can be useful for online predictions of color development in industrial staining processes of wood with chemical stains.