研究目的
To investigate the relationship between wood surface roughness and the NIR spectra, aiming to reveal information pertaining to the surface roughness and its influence in the effect of models for Chinese fir and Eucalyptus wood properties built by NIR data.
研究成果
1. The NIR spectra absorption showed differences among samples from different surface roughnesses, and the absorption decreased with the increase in surface roughness.
2. Strong correlations were obtained between the measured and predicted values based on the Chinese fir samples and the mix of these two species samples. A relatively poor correlation was found in the models based on Eucalyptus samples; however, it was still significant.
3. The NIR spectra provides some information about surface roughness in the samples. The surface roughness may influence the effect of models intended to predict other attributes of Chinese fir and Eucalyptus wood properties built using the NIR data.
研究不足
The surface roughness models did not achieve the most optimal accuracy because of the limitation in the number and surface roughness of the samples. Future research should include a larger number of samples from more species and different surface roughness levels.
1：Experimental Design and Method Selection:
The study used the stylus profile method to measure surface roughness and NIR spectroscopy to analyze the wood samples. Partial least squares (PLS) analysis was employed to develop calibration models of surface roughness.
2：Sample Selection and Data Sources:
Chinese fir (Cunninghamia lanceolata) and Eucalyptus (Eucalyptus pellita) wood samples were collected from Anhui and Guangdong provinces, respectively. Five trees were collected from each species, and disks of 10 cm in length were cut from each tree at the height of 1.3 m.
3：3 m.
List of Experimental Equipment and Materials:
3. List of Experimental Equipment and Materials: Handysurf (E-35A) surface profileometer (Tokyo Seimitsu, Tokyo, Japan) for measuring surface roughness, ASD Field Spec? spectrometer (Analytical Spectral Devices, Boulder, CO, USA) for NIR reflectance spectra, and Unscrambler? software (CAMO Software Inc., Woodbridge, NJ, USA) for multivariate data analysis.
4：Experimental Procedures and Operational Workflow:
Surface roughness was measured five times for each sample. NIR reflectance spectra were obtained from the cross, radial, and tangential sections of wood samples. Two thirds of the samples were assigned to a calibration set, and one third to a prediction set.
5：Data Analysis Methods:
PLS analysis was used to develop calibration models of surface roughness. The performance of the models was assessed using coefficient of determination (R2), root mean square error of calibration (RMSEC), and root mean square error of prediction (RMSEP).
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