During conversion of bamboo into biofuels and chemicals, it is necessary to efficiently predict the chemical composition and digestibility of biomass. However, traditional methods for determination of lignocellulosic biomass composition are expensive and time consuming. In this work, a novel and fast method for quantitative and qualitative analysis of chemical composition and enzymatic digestibilities of juvenile bamboo and mature bamboo fractions (bamboo green, bamboo timber, bamboo yellow, bamboo node, and bamboo branch) using visible–near infrared spectra was evaluated. The developed partial least squares models yielded coefficients of determination in calibration of 0.88, 0.94, and 0.96, for cellulose, xylan, and lignin of bamboo fractions in raw spectra, respectively. After visible–near infrared spectra being pretreated, the corresponding coefficients of determination in calibration yielded by the developed partial least squares models are 0.994, 0.990, and 0.996, respectively. The score plots of principal component analysis of mature bamboo, juvenile bamboo, and different fractions of mature bamboo were obviously distinguished in raw spectra. Based on partial least squares discriminant analysis, the classification accuracies of mature bamboo, juvenile bamboo, and different fractions of bamboo (bamboo green, bamboo timber, bamboo yellow, and bamboo branch) all reached 100 %. In addition, high accuracies of evaluation of the enzymatic digestibilities of bamboo fractions after pretreatment with aqueous ammonia were also observed. The results showed the potential of visible–near infrared spectroscopy in combination with multivariate analysis in efficiently analyzing the chemical composition and hydrolysabilities of lignocellulosic biomass, such as bamboo fractions.

To investigate the relationship between sunlight and artificial light sources on the weathering of wood, three woods, namely, Tectona grandis L.F. (teak), Stereospermum colais (mabberley), and Dicorynia guianensis (basralocus), were tested under natural sunlight for 733 days and artificial xenon light for 180 h, respectively. A comparison between sunlight and artificial xenon light was made based on surface color changes at various intervals. The results showed that the woods suffered from more severe aging in the artificial xenon light exposure than that in the natural sunlight exposure. At the early stage of exposure, very good relationships were found between 70 days under natural sunlight weathering and 60 h under artificial xenon light weathering. Compared with natural sunlight, about a 30 times faster aging process was identified in the artificial xenon light. However, the linear relationship vanished at the later aging stage. It was found that the color change fluctuated in natural sunlight, while it increased steadily in artificial xenon light. The wood species affected the aging of woods. In natural sunlight exposure, the color change decreased in the order of mabberley > teak > basralocus, while in artificial xenon light exposure, color change decreased in the order of mabberley > basralocus > teak due to the easier volatilization of extractives in artificial xenon light than in natural sunlight.

The swift and non-destructive classification of wood species holds crucial significance for the utilization and trade of wood resources. Portable near-infrared (NIR) spectrometers have the potential for rapid and non-destructive wood species identification, and while several studies have explored related methodologies, further research on their practical application is needed. To address this research gap, this study proposes a multi-scale convolutional neural network (CNN) combined with a portable NIR spectrometer (wavelengths range: 908 to 1676 nm) for wood species identification. To enhance the capability of directly extracting robust features from NIR spectral data collected by a portable spectrometer, the Gramian angular field (GAF) method is introduced to transform 1-dimensional (1D) NIR spectral data into 2-dimensional (2D) data matrices. Furthermore, a multi-scale CNN model is utilized for direct feature extraction. The representation by 2D matrices, instead of 1D NIR spectral data, aligns with 2D convolutional operations and enables a more robust extraction of discriminative features. In the experimental phase, eight wood species were identified using the proposed method, alongside commonly used multivariate data analysis and machine learning (ML) methods. The StratifiedGroupKFold dataset partitioning approach and five-fold cross-validation were used. Additionally, nine spectral preprocessing methods were compared, and principal component analysis (PCA) was used for feature extraction in the ML method. Evaluation metrics, such as accuracy, precision, and recall, were adopted to assess the performance of the methods. The proposed multi-scale CNN model, in combination with 2D GAF matrices of the 1D spectral data, yielded the most accurate results with a mean accuracy of 97.34% in the five-fold validation. These findings present a new approach for the construction of a rapid, non-destructive, and automatic wood species identification method using a portable NIR spectrometer.

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.

Identifying wood accurately and rapidly is one of the best ways to prevent wood product fakes and adulterants in forestry products. Wood identification traditionally relies heavily on special experts that spend extensive time in the laboratory. A new method is proposed that uses near-infrared (NIR) spectra at a wavelength of 780–2300 nm incorporated with the gray-level co-occurrence (GLCM) texture feature to accurately and rapidly identify timbers. The NIR spectral features were determined by principal component analysis (PCA), and the digital image features extracted with the GLCM were used to create a support vector machine (SVM) model to identify the timbers. The results from fusion features of raw spectra and four GLCM features of 25 timbers showed that identification accuracy by the model was 99.43%. A sample anisotropy and heterogeneity comparative analysis revealed that the wood identification information from the transverse surface had more characteristics than that from the tangential and radial surfaces. Furthermore, short-wavelength pre-processed NIR bands of 780–1100 nm and 1100–2300 nm realized high identification accuracy of 99.43% and 100%, respectively. The four GLCM features were effective for improving identification accuracy by improving the data spatial clustering features.