Wood surface roughness, surface free energy (SFE), wettability, and bonding quality for water-based acrylic coatings were investigated. The samples tested in this study included Pinus radiata, Pinus sylvestris, Larch, Hemp oak, Catalpa tree, and Camphor. Sandpaper with grits of 180, 240, 320, 400, and 500 was utilized to sand wood surfaces. The van OSS-Chaudhury-Good equation (vOCG) was used to calculate the SFE values. The modified model (M-D) was used to calculate the wettability based on the contact angle change rate (K value). The higher the K value, the faster the contact angle approaches equilibrium. A cross-cut test was used to evaluate the coating’s bonding quality. The anatomical structure of wood has an impact on the roughness of hardwood. The equilibrium contact angle is influenced by the wood species and sandpaper grit size. Sanding can make the surface of wood more wettable. Radiata pine that had been sanded to 180 grit had the highest SFE value. After finishing with waterborne acrylic, hardwood had a slightly better coating adhesion than softwood. Hemp oak wood had the lowest coating adhesion (0.6) and the highest K value (0.82). The best bonding quality (0.4) was supplied by the camphor wood with the lowest K value (0.13). Wettability in terms of K values was a good indication of determining the bonding quality of the water-based acrylic coatings.

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The feasibility of using a portable near-infrared (NIR) spectrometer combined with partial least squares for discrimination analysis (PLS-DA) to identify five similar Cinnamomum wood species was investigated. To improve model reliability and identification accuracy, the effects of three main spectra preprocessing methods and their combinations were examined. Then, the model performance created by spectra collected before and after specimen surface sanding were compared. In the PLS-DA model based on spectra preprocessed by standard normal variate (SNV) and first derivative combined, the identification accuracy of the five species was above 95%, and the compared results demonstrated the surface natural change influences the NIR model performance. It was shown that a portable NIR device combined with PLS-DA can be used to rapidly and accurately identify five similar Cinnamomum wood species.