【作者】 姚胜；

【导师】 蒲俊文；

【作者基本信息】 北京林业大学 ， 林产化学加工工程， 2010， 博士

【摘要】 为了快速测定木材的材性,本文主要研究了不同因素对木材材性进红外定量模型预测性能的影响,并利用傅里叶变换近红外光谱分析技术建立了相思树木的不同材性的近红外预测数学模型,得出以下主要结论：扫描次数为8次的时候就可以建立较好的近红外预测模型；分辨率高低与木材聚戊糖定量检测结果的准确度没有必然的联系；可以通过多次装样来提高模型的预测准确性；用60目～100目的木粉的谱图建立的材性近红外预测模型都比较好；扫描样品时,分多次扫描建立的模型较好；轻压和不压可以建立较好的模型；基础数据越准确,所建立模型的精度越高,其对未知样本的预测结果也越准确,但基础数据较差的情况下建立的模型也有较好的预测效果；在样本不是很多的情况下,加入更多的建模样品比增加数据准确性更能提高模型的预测精度；不同谱图预处理方法对偏最小二乘法建立的相思树木聚戊糖含量交叉检验模型有很大的区别；在不同预处理方法中,一阶导数结合减去一条直线建立的模型最佳；选用较大的平滑点数模型有很好的预测性能,一般来说17点到25点比较好；PCA方法筛选的样品建立的模型优于用含量梯度法筛选的样品建立的模型。对近红外预测模型的研究结果表明：建立的水分近红外验证模型决定系数(R2val)为0.9886,预测均方根误差(RMSEP)为0.216%；克拉森木素模型R2val为0.9425,RMSEP为0.5%；苯醇抽提物模型R2val为0.9384,RMSEP为0.267%；聚戊糖模型R2val为0.9428,RMSEP为0.51%；综纤维素模型R2val为0.8272,RMSEP为0.907%；α-纤维素模型R2val为0.7495,RMSEP为1.17%；制浆得率模型R2val为0.593,RMSEP也有0.873%；用广西相思树木聚戊糖近红外模型不能很好测定福建相思树木聚戊糖含量,但在广西模型中加入三个福建样品后,该模型就能够较好的测定福建相思树木材样品。建立了不同树种木材样品的苯醇抽提物近红外预测模型,在相思树苯醇抽提物含量近红外预测模型中加入四个毛白杨样品后就能较好的测定毛白杨的苯醇抽提物含量。从相思树的化学材性、微观结构以及制浆得率来看,广西钦廉林场七年生卷荚相思木、广西高峰林场黑木相思木和杂交相思木以及福建卷荚相思木均是较好的纸浆材。更多还原

【Abstract】 To be able to breed for suitable trees, it is essential to be able to screen large numbers of individual trees. Measuring wood properties by traditional chemistry is costly and time-consuming. Near infrared spectroscopy has made rapid progress as analytical techniques. Being capable of making nondestructive, rapid, high efficient and convenient analysis, NIR technique is suitable to analyze wood properties.The calibration models to predict the chemical composition, such as moisture content, lignin content, holocellulose, a-cellulose, hemicelluloses and extractive, basic density and pulp yield in Acalia Spp were developed by applying on Fourier near infrared spectroscopy. The factors that influenced the NIR veracity were studied, including resolution, scan number, test repetition, scan numbers, degree of tightnsss, scanning time, pretreatmeng methods, wavenumbers, samples number, the different cross of wood, and the accuracy of reference data.To provide foundation with optimum test condition when modeling, factors affecting the accuracy of wood near-infrared spectroscopy were studied. The results show that scan number does have effect on NIR spectra and prediction models, the calibration and prediction model was robust with 32 scan number. Resolution does not have significant effect on the NIR models, but the spectrum with high resolution was rougher than that with low resolution. Moreover, the scan speed was lower and more data size was required when high resolution was used. The result of NIR model can be enhanced when scan the sample repetitive. The model based on rough powders can gave more accurate evaluation indexes vs that based on fine powders. The best model is based on the mixture powders. Depressing sample could increase errors brought by sample tightness. The best NIR calibration statistics and the most accurate prediction results were aligned with the most accurate reference data. However, based on statistical analysis of numerous calibration samples, it is possible for NIR calibration models to obtain more accurate prediction results than the laboratory reference data used in the calibration sets. It is better to make less search for high accurate reference data and instead to introduce more calibration samples to improve the ruggedness of the calibration models. In order to search an appropriate pretreatment method to determine hemicellulose in Acalia Spp.. The effect of 11 pretreatment methods to the model based on PLS has been compared. The model developed by pretreated spectra had the highest correlation coefficient, and the lowest relative deviation than the model developed by raw spectra. The best pretreatment method was 1st derivative+straight line subtraction. The different smoothing point were secleted when the spectra were pretreated by the 1st derivative+straight line subtraction,17,23 and 25 point smooth can obtain good results. It shows that 6000 cm-1～5500cm-1 band have significant correlation with hemicelluloses content, and select appropriate wave band is very important for a good calibration model. Selection of representative samples for calibration can directly influence the representative and accurateness of the model. Two methods based on the grads of samples’concent and the Mahalanobis distance of spectral was compared. The later had built better calibration.The study showed that NIR analysis can be reliably used to predict moisture content, lignin content, hemicelluloses, and extractive content in Acacia spp.. NIR calibration predicted values for moisture were close to the laboratory results. The regression results obtained were explained with an R2CV=0.9884 and RMSECV=0.194%, R2val=0.9886, RMSEP=0.216%. The cross validation of lignin content is explained with an R2CV=0.9553 and RMSECV=0.371%, R2val= 0.9425, RMSEP=0.5%. The cross validation of extractive content is explained with an R2CV=0.9345 and RMSECV=0.227%, R2va,= 0.9384, RMSEP=0.267%. The cross validation of hemicellulose content is explained with an R2CV =0.9382 and RMSECV=0.509%, R2val= 0.9137, RMSEP=0.575%. The cross-calibration results of the holocellulose and a-cellulose were not as good as for the hemicellulose and lignin’s. The cross-calibtation models for holocellulose resulted in an R2CV of 0.8922 while fora-cellulose the R2CV was 0.858, and the RMSECV is 0.573% and 0.828%. When used the cross-calibration to predict the validation set, the R2val of holocellulose and a-cellulose were 0.858 and 0.7495, respectively. The RMSEP were 0.828% and 1.17%, respectively. The results indicated that the accuracy of models was influenced by the sample surface from which NIR spectra were obtained. The model based on transverse section of wood owned the highest accuracy, followed sequentially by the models respectively based on the radial section tangential section of wood. Good correlations between NIR spectra and basic density were achieved for wood meal and solid wood. The screened pulp yields were predicted by NIR. The regression results obtained were explained with an R2CV=0.7327 and RMSECV=0.739% when the original spectrum was expressed as the Straight line subtraction and the wavelength range between 9770.1cm-1 and 5446.3cm-1.The prediction bias is very large when using the calibration built with samples from Guangxi province to predict Fujian samples, however, addition of three Fujian samples to the Guangxi calibration set was sufficient to greatly reduce predictive errors and that the inclusion of eight Fujian samples in the Guangxi set was sufficient to give relatively stable predictive errors. The RMSEP is very large when using the calibration built with Acacia spp samples to predict Triploid Populus tomentosa samples. Addition of four Triploid Populus tomentosa samples to the Acacia spp samples set was greatly reduce the predictive errors.According to the chemical component, microstructure and pulp yield, the 7 years old A. cincinnata, in Guangxi qinlian farm, the A. melanoxylon and A.mangium×A.auriculiformis in Guangxi gaofeng farm and A. cincinnata in Fujian are fit to pulping.更多还原