Sheng Yao

The accuracy of near infrared prediction of hemicellulose content arising from varying introduced errors.

Reference method noise is one of the important factors which affects the accuracy and the precision of NIR predicted values. In this paper, noise was deliberately and artificially added to the reference data of hemicelluloses content of Acacia spp. in four different ways, namely adding absolute error, relative error, random absolute error or random relative error. The effect of the addition of different error to the reference data on NIR calibration models and their prediction results were studied. Although the results of calibration models were very poor when different errors were added to the reference data, using the resulting model to predict values for unknown samples to which errors were not added, resulted in predictions that were better than expected, especially for addition of random absolute error and random relative error condition. The results indicate that the NIR calibration models produce predicted values that are acceptable if the noise is not too large.

Determination of Holocellulose and Alpha-Cellulose Contents in Triploid Clones of Populus tomentosa Using NIRS

The aim of this work was to study the ability of NIR spectroscopy to determine holocellulose and alpha-cellulose contents in triploid clones of Populus tomentosa. For this purpose 90 trees with five families in different planting density grown in Shandong were analyzed. Calibration models were developed from wet chemical and NIR data using partial least squares (PLS) regression. High coefficients of determination (R2) and low root mean square errors of cross-validation (RMSECV) were obtained for holocellulose (R2 = 0.7805, RMSECV =0.652) and alpha-cellulose (R2 = 0.8702, RMSECV = 0.548) from wood meal. Prediction produced high correlation coefficients between laboratory and predicted values for holocellulose and alpha-cellulose contents, which R2 values are 0.7467 and 0.8505, and RMSEP are 0.993 and 0.553, respectively. This study showed that NIR analysis can be reliably used to predict holocellulose and alpha-cellulose contents in triploid clones of Populus tomentosa.

Study on Compressed Wood with Urea-Formaldehyde Prepolymer

In this research, the urea-formaldehyde prepolymer and multilayer hot-press drying was used to obtain modified wood. The timbers were compressed and dried by the multilayer hot-press drying kiln. The combination of chemical modification and hot-press drying can improve the drying rate, which can also enhanced the physical and mechanical properties. Based on the findings, the basic density of modified wood improved 25.2 %, the bending strength and the bend elastic modulus improved 15.6 % and 25.0 % respectively. The dimensional stability of the treatment appeared to be slightly higher than that of untreated samples under the same conditions for processing temperatures and times. The improving dimensional stability of wood mainly was attributed to the prepolymer that changes wood cell wall components such as the degradation of the hemicelluloses and cellulose during hot-press drying treatment. The XRD results indicated that the degree of crystallinity increased to 35.45 %from 31.25 %. The TGA results show that the degradation of the samples can be divided into two step, both of the maximum weight loss velocity temperature of the two step increased to 266 °C, 355 °C from 244 °C and 341 °C.