Naturally occurring knots reduce the mechanical strength of wood. Veneers from sugi (Cryptomeria japonica) served as research material to study the effect of knots and holes. Veneer samples were first subjected to a three-point bending test to obtain measured modulus of elasticity (MOE) values. Then, near-infrared (NIR) hyperspectral imaging (HSI) was used to construct a prediction model and map the predicted MOE values. This is the first attempt for MOE prediction from the entire veneer surface based on NIR-HSI technology, while the mathematical part relies on chemometrics and cross-validation partial least squares regression (CV-PLSR). Maps of MOE prediction values could distinguish between latewood (LW) and earlywood (EW), as well as between a sound knot and a dead knot.

In this study, static bending measurements and online NIR spectra acquisitions were combined to construct modulus of elasticity (MOE) prediction model for sugi lumber. NIR spectra were acquired from tangential surface of sugi lumbers at a speed of 120 m min −1 to assess its effectiveness in the wood industry. Cross-validation partial least squares regression (CV-PLSR) and test-set-validation partial least squares regression (TSV-PLSR) analyses were employed for analysing the data. The second derivative (2d) spectra with 19 smoothing points (Savitzky-Golay algorithm, second polynomial) gave the best result as spectral pre-processing treatment with the lowest root mean square error of cross-validation and the highest coefficient of determination for cross-validation based on the optimum number of latent variables as assessed from the minimum validation residual variance value in the CV-PLSR analysis. These 2d spectra were then used in the TSV-PLSR analysis for 100 repetitions to check the robustness of the calibration.

The fastest developed online near-infrared (NIR) spectroscopic system with the speed of 120m/min was applied to predict the quality of wooden veneer, i.e., the stiffness (modulus of elasticity/MOE) and the moisture content (MC) from two species, Japanese cedar (Cryptomeria japonica) and Douglas-fir (Pseudotsuga menziesii). The measurement of MOE was conducted by three-point bending test on air-dry MC (12-13%) and the MC measurement was conducted from 30% to 12%. The root mean square error of crossvalidation (RMSECV) values from MOE prediction for Japanese cedar veneer and Douglasfir veneer are 0.9GPa and 1.8GPa, while the RMSECV values from MC prediction are 1.0% and 0.6% for Japanese cedar veneer and Douglas-fir veneer, respectively. This fast speed of online NIR spectra measurement could give better prediction accuracy and be applied in the plywood production industrial line to increase the efficiency.

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Doubtlessly, wood identification is critically important for a number of sectors, including government organizations, the wooden-based industry, museums, law enforcement, and scientists working in the fields of botany, ecology, forestry, and wood technology. Unfortunately, most wood species listed as “the least-known species” lack essential knowledge or even their anatomical feature and basic properties to promote their usage. This research aimed to investigate the anatomical characteristics and fiber quality of the least-known timber species of Apocynaceae family, which are authentic wood collection from Xylarium Bogoriense, namely  , Ervatamia. sphaerocarpa, E. aurantiaca, Kopsia flavida, Lepiniopsis ternatensis, Plumeria acuminata, P. rubra, and Voacanga foetida.  Wood samples have indistinct growth ring boundaries, diffuse-porous vessels in diagonal and/or radial pattern, vessels in radial multiples of 4 or more cells, simple perforation plate, alternate intervessel pits; distinct borders of vessel-ray pits, similar with those of intervessel pits in size and shape throughout the ray cell, and septate fibers with simple pits to minutely bordered pits which are common in radial and tangential walls. Based on the fiber length and the derived values of fiber dimension, some species are classified into Quality Class II and III, and the rest of them are classified into Quality Class II or III for pulp and paper manufacturing. Based on general characteristics, commonly Apocynaceae can be used as handicrafts raw material. Based on the fiber quality, some species which are classified into Quality Class II, are predicted to have potential as pulp and paper material with medium quality.