相思树材性近红外预测模型的建立及优化
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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。
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.
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。
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.
引文
1.曹福亮.林分密度对南方型杨树木材性质的影响[A].吕士行,方升佐.杨树定向培育技术[C]．北京：中国林业出版社.1997,148-151.
2.陈诚,蒲俊文,姚胜,等.速生阔叶材杂交相思制浆性能的研究[J].造纸科学与技术,2009,28(4)：1-3，6.
3.陈诚,蒲俊文,姚胜,等.五种相思树制浆适应性能研究[J].中华纸业,2009(17)：80-82.
4.褚小立,陆婉珍.近红外化学成像的原理、仪器及应用[J].分析仪器,2008(4)：1-5.
5.褚小立,袁洪福,陆婉珍.基础数据准确性对近红外光谱分析结果的影响[J].光谱学与光谱分析,2005,25(6)：886-889.
6.褚小立,袁洪福,陆婉珍.近红外分析中光谱预处理及波长选用方法进展与应用[J].化学进展,2004,16(4)：528-542.
7.邓拥军,房桂干,李萍,等.四种相思树硫酸盐法制浆性能的比较[J].陕西科技大学学报,2006,24(2)：1-7.
8.董守龙,任芊,黄友之.近红外光谱分析技术的发展和应用[J].化工生产与技术,2004,1(6)：44-46.
9.段焰青,者为,杨涛,等.影响烟草近红外光谱分析结果准确性的因素[J].光谱实验室,2007,24(4)：705-710.
10.房桂干,王静霞.五种澳洲速生相思树制浆性能研究[J].林产化工通讯,1992,5：10-13.
11.费本华,江泽慧,阮锡根.银杏木材微纤丝角及其与生长轮密度相关模型的建立[J].木材工业,2005.14(3)：13-15.
12.袱香香,杨文忠,方升佐.木材微纤丝角研究的现状和发展趋势[J].南京林业大学学报,2002,26(6)：83-87.
13.高荣强,范世福,严衍禄,等.近红外光谱的数据预处理研究[J].光谱学与光谱分析,2004,24(12)：1563-1565.
14.高荣强,范世福.现代近红外光谱分析技术的原理及应用[J].分析仪器,2002(3)：9-12.
15.龚木荣,洪启清,沈文瑛,等.不同树龄马占相思制浆性能的研究[J].林产工业,1998,25(4)22-25
16.龚木荣,洪启清,张大同.低龄厚荚相思木的制浆造纸性能[J]．南京林业大学学报(自然科学版),2001,25(6)：48-50.
17.龚木荣,洪唐清,张大同.九年生马占、大叶、厚荚相思制浆性能的比较[J].林产工业,2001,28(6)：25-27.
18.龚木荣,李忠正.六年生马占、大叶、厚荚3种相思树制浆性能比较[J],2002,1：1-3.
19.龚玉梅,张炜.近红外光谱检测技术及其在林业中的应用[J].光谱学与光谱分析,2008,28(7)：1544-1548.
20.郭德荣.人工林红松微纤丝角变异与管胞长度和拉伸强度的关系[J].东北林业大学学报,1982,10(2).19-47.
21.黄安民,费本华,江泽慧,等.表面粗糙度对近红外光谱分析木材密度的影响[J].光谱学与光谱分析,2007,27(9)：1700-1702.
22.黄安民,江泽慧,李改云.杉木综纤维素和木质素的近红外光谱法测定[J].光谱学与光谱分析,2007,27(7)：1328-1331.
23.黄安民,江泽慧.近红外光谱技术在木材性质预测中的应用研究进展[J].世界林业研究,2007,20(1)：49-54.
24.黄安民.人工林杉木木材性质的近红外光谱预测[D]．北京：中国林业科学研究院,2006.9
25.纪淑娟,李东华,重滕和明.不同扫描方式南果梨近红外预测模型差异性研究[J].食品研究与开发,2008,29(7)：108-110.
26.江泽慧,费本华,杨忠.光谱预处理对近红外光谱预测木材纤维素结晶度的影响[J].光谱学与光谱分析,2007(03)：435-438.
27.江泽慧,黄安民,费本华,等.利用近红外光谱和X射线衍射技术分析木材微纤丝角[J].光谱学与光谱分析,2006,26(7)：1230-1233.
28.江泽慧,黄安民,王斌.木材不同切面的近红外光谱信息与密度快速预测[J].光谱学与光谱分析,2006,26(6)：1034-1037.
29.江泽慧,黄安民.木材中的水分及其近红外光谱分析[J].光谱学与光谱分析,2006,26(8)：1464-1468.
30.江泽慧,李改云,王戈,等.近红外光谱法测定毛竹综纤维素的含量研究[J].林产化学与工业,2007,27(1)：15-18.
31.江泽慧,刘君良,覃道春.相思类树种木材的资源、材性与加工利用[J].木材工业,2002,16(6)：6-9.
32.江泽慧,彭镇华.世界主要树种木材科学特性[M]．北京：科学出版社,2001,7.
33.江泽慧,杨忠,王戈,等.湿地松木材近红外光谱与其结晶度的相关性[J].林业科学,2007,43(10)：95-99.
34.江泽慧.中国林业工程[M].济南：济南出版社,2002,5.
35.姜伟,韩光亭,张元明.近红外光谱技术在植物纤维化学成分预测中的应用[J].中国纤检,2009,(4)：66-68.
36.蒋华松,沈文瑛,张大同,等.阔叶材原料特性与制浆之间关系的探讨[J].中国造纸,1998,1：36-39.
37.蒋焕煜,彭永石,谢丽娟,等.扫描次数对番茄叶漫反射光谱和模型精度的影响研究[J].光谱学与光谱分析,2008,28(8).1763-1733.
38.黎庆涛,李小梅,余炼,等：近红外漫反射光谱法测定纸浆卡伯值[J].中国造纸,2004,23(4)：8-10.
39.李改云,黄安民,王戈,等.近红外光谱法快速测定毛竹Klason木质素的含量[J].光谱学与光谱分析,2007,27(10)：1977-1980.
40.李坤,付时雨,詹怀宇.近红外光谱分析技术在制浆造纸中的应用[J].造纸科学与技术,2008(4)：40-44.
41.李小梅,黎庆涛,罗伟强,等.近红外光谱法分析植物纤维原料的组成[J].纸和造纸,2008,27(5)：58-61.
42.李小梅,王双飞.近红外光谱技术在造纸工业中的应用[J].中国造纸学报,2003,18(2)：189-194.
43.李欣雨.用近红外和拉曼光谱法预测木材和纸浆的性能[J].国际造纸,2008(05)：30-34.
44.李旭东,张根明.近红外光谱分析技术的发展与应用研究[J].重庆科技学院学报：自然科学版,2008,10(2)：33-35,40-44.
45.李炎,张月学,徐香玲,等.近红外反射光谱(NIRS)分析技术及其在农业上的应用[J].黑龙江农业科学,2008,(1)：105-108.
46.李勇,魏益民,王锋.影响近红外光谱分析结果准确性的因素[J].核农学报,2005,19(3)：236-240.
47.李忠正.林纸一体化与中国主要速生人工造纸树种的制浆造纸性能[J].中华纸业，2001,22.(7)：6-12.
48.练文柳,吴名剑,孙贤军,等.不同预处理方法对烟草近红外光谱预测模型的影响[J].烟草科技,2005,(2)：19-23.
49.梁高峰,贾宏汝,谷运红,等.近红外光谱分析技术及其在农业研究中的应用[J].安徽农业科学,2007,35(29)：9113-9115.
50.刘发龙,马新刚,程福银,等.近红外光谱分析技术在快速分析上的应用[J].分析测试技术与仪器,2008,14(4)：241-247.
51.刘光良.相思属树(Acacia sp.)材性及制浆造纸适应性(文献综述)[J].林产化工通讯,1997,31(5)：31-10.
52.刘宁宇,王伟,吴拥军.近红外光谱技术在药物分析中的应用[J].安阳师范学院学报,2005,(5)：50-52.
53.刘鹏,王志杰,陈永常.近红外光谱技术在造纸中的应用[J].黑龙江造纸,2009,37(1)：29-30.
54.刘艳青.近红外光谱分析的技术与应用[J].制造业自动化,2002,24(2)：61-62.
55.刘泽春,张峰,谢卫.烟草近红外光谱模型的适配性研究[J].烟草科技,2008,(5)：34-37.
56.卢长武,陆德俊.近红外水分仪在高定量浆板生产中的应用[J].中国造纸,2005,24(4)：65-66.
57.陆婉珍,袁洪福,徐广通,等.现代近红外光谱分析技术[M].北京：中国石化出版社,2000.
58.陆文达.近红外光谱(NIR)应用于密度和刚度变异宽广的多样样树种木材[J].国际木业,2002,32(12)：32.
59.陆文达.可见光谱和近红外光谱(VIS and NIR-Spectroscopy)在无损检测木材纹理角度方面的潜力研究[J].国际木业,2003,33(5)：34.
60.闵顺耕,李宁,张明祥.近红外光谱分析中异常值的判别与定量模型优化[J].光谱学与光谱分析,2004,24(10)：1205-1209.
61.尼珍,胡昌勤,冯芳.近红外光谱分析中光谱预处理方法的作用及其发展[J]．药物分析杂志,2008,28(5)：824-829.
62.潘志刚,吕鹏信,陆熙娴.中国厚英相思木制浆特性的研究[J].林产工业,1994,21(5)：17-19
63.潘志钢.中国主要外来树种引种栽培.北京：北京科学技术出版社[M],1994.
64.彭玉魁.近红外光谱分析技术及其在农业中的应用[J].陕西农业科学,2001(3)：25-28
65.蒲俊文,宋君龙,谢益民,等.三倍体毛白杨木质素结构特性研究[J].北京林业大学学报,2002(6)：211-215.
66.蒲俊文,宋君龙.三倍体毛白杨化学成分径向变异的研究[J].造纸科学与技术,2002,21(3)：1-3,7.
67.蒲俊文,宋君龙.三倍体毛白杨无性系纸浆材的选优[J].造纸科学与技术,2001,20(5)：11-13.
68.蒲俊文,宋君龙.三倍体毛白杨纤维形态变异的研究[J]．北京林业大学学报,2002,24(2)：62-66.
69.蒲俊文,谢益民.三倍体毛白杨木质素结构特性研究[J]．北京林业大学学报,2002,24(5)：211-215.
70.秦冲,陈雯雯,何雄奎,等.近红外光谱分析中建模校正集的选择[J].光谱学与光谱分析,2009(10)：2661-2664.
71.沈文浩,谢益民,刘焕彬.化学计量学法处理可见光谱定量测定纸浆卡伯值[J].广东造纸,1999,(5-6)：90-95
72.沈文浩,谢益民,刘焕彬.人工神经网络在纸浆卡伯值光学定量分析中的应用[J].高等学校化学学报,2000,2](8)：1208-1210.
73.沈文浩,谢益民.近红外光谱技术在制浆造纸工业中的应用[J].广东造纸,1999(2)：11-13.
74.史永刚,冯新泸,李子存,等.近红外光谱分析中应考虑的几个问题[J].光谱实验室,2001,18(4)：435-437.
75.舒庆尧,吴殿星,夏英武.用近红外反射光谱测定小样本糙米粉的品质性状[J].中国农业科学,1999,32(4)：92-97.
76.宋志远,林元奎.近红外光谱分析技术在油品分析中的应用[J].分析仪器,1995(3)：41-45.
77.王丹红,吴文晞,林志武,等.近红外光谱法鉴别Tencel等四种纤维[J].福建分析测试,2009,18(4)：32-34.
78.王冬,叶升锋,闵顺耕,等.近红外光谱分辨率对液体样品近红外定量模型影响的研究[J].光谱学与光谱分析,2009,29(7)：1813-1817.
79.王海东,秦玉昌,吕小文,等.近红外光谱技术(NIRS)测定玉米粉碎粒度的研究[J]．饲料工业,2008,29(19)：4144.
80.王海水,汪冬梅,等.近红外光谱在品质分析和定量分析中的应用[J].分析测试技术与仪器,2002,8(3)：136-138.
81.王丽杰,郭建英,徐可欣.近红外光谱分析中建模样品优选方法的研究[J].红外技术,2005,27(1)：75-78.
82.王文真.在近红外光谱定量分析中应注意的几个问题[J],1996,(1)124-25.
‘83.王学琳,孔淑萍.傅里叶变换近红外光谱进展[J].现代仪器,1999(6)：1-3.
84.王学顺,戚大伟,黄安民.基于小波变换的木材近红外光谱去噪研究[J].光谱学与光谱分析,2009,29(08)：2059-2062.
85.王学顺,戚大伟,黄安民.基于小波模极大值的木材近红外光谱去噪[J].林业科学,2008,44(10)：109-112.
86.王学顺,戚大伟,黄安民.木材近红外光谱小波阈值去噪方法[J].东北林业大学学报，2009a，37(2)：32-34.
87.王一兵,王红宇,翟宏菊,等.近红外光谱分辨率对定量分析的影响[J].分析化学,2006,34(5)：699-701.
88.王玉荣,费本华,傅峰,等.基于近红外光谱技术预测木材纤维长度[J].中国造纸,2008,27(06)：6-9.。
89.王玉荣,江泽慧,赵荣军,等.快速检测木材微纤丝角的近红外光谱分析技术[J].林业机械与木工设备,2007,35(7)：35-36.
90.王志玲,王正,王元秀,等.现代近红外光谱技术——人造板性能无损检测的新方法[J].世界林业研究,2004,17(6)：22-24.
91.魏良明.普通玉米籽粒品质性状的遗传及其近红外测定方法的研究[D].北京：中国农业大学,2003.
92.吴静珠,王一鸣,张小超,等.近红外光谱分析中定标集样品挑选方法研究[J].农业机械学报,2006,37(4)：80-82.
93-吴新生,谢益民,刘焕彬,等.蒸煮过程纸浆卡伯值近红外光谱法在线测定[J].中国造纸学报,2003,18(1)：110-113.
94.吴新生,谢益民,帅兴华.基于近红外光谱法的造纸用木材原料的快速分类[J].中国造纸学报,2007,22(3)：14-16.
95.吴新生,谢益民.基于近红外光谱法的木材原料的Kohonen网络分类法[J].造纸科学与技术,2009b,28(4)：4-6.
96.吴新生,谢益民.主成分.逐步回归近红外光谱法测量纸浆卡伯值[J].光谱实验室,2005,22(1)：116-119.
97.夏柏杨,任芊.近红外光谱分析技术的一些数据处理方法的讨论[J].光谱实验室,2005,22(3)：629-634.
98.夏俊芳,李培武,李小昱,等.不同预处理对近红外光谱检测脐橙VC含量的影响[J].农业机械学报,2007,38(6)：107-111.
99.谢丽娟,刘东红,张宇环,等.分辨率对近红外光谱和定量分析的影响研究[J].光谱学与光谱分析,2007,27(8)：1489-1492.
100.谢益民,吴新生,刘焕彬,等.硫酸盐法蒸煮脱木素历程的近红外光谱表征法[J].中国造纸学报,1999,14(1)：6-8.
101.谢益民,伍红,郑志彤,等.蓝桉的树龄与化学组成及硫酸盐法制浆特性的关系[J].广东造纸.1999,1：7-10.
102.徐广通,袁洪福,陆婉珍.近红外光谱定量交叉检验模型适用性研究[J].光谱学与光谱分析,2001,21(4)：459-463.
103.徐广通,袁洪福.现代近红外光谱技术及应用进展[J].光谱学与光谱分析,2000,20(2)：134-142.
104.徐坤,刘鹏起,张玉娜,等.近红外光谱分析技术及应用[J].莱阳农学院学报,2001,18(3)：237-240.
105.薛国新,郑建文,洪启清.硫化钠预处理大叶相思树硫酸盐法深度脱木素蒸煮技术[J].中国造纸学报,1999,14(增刊)：8-12.
106.薛国新,郑建文,胡建安.不同树龄相思树混合木片硫酸盐法制浆[J].中国造纸1999,7：20-23.
]07.严衍禄.近红外光谱分析基础与应用[M]．北京：中国轻工业出版社,2005：128-129.
108.杨春节,何川,宋执环.基于近红外光谱与支持向量机的纸浆卡伯值在线测量[J].光谱学与光谱分析,2008,28(8)：1795-1798.
109.杨忠,江泽慧,费本华,等.近红外光谱技术及其在木材科学中的应用[J].林业科学,2005,41(4)：177-183.
110.杨忠.近红外光谱预测人工林湿地松木材性质与腐朽特性的研究[D]．北京：中国林业科学研究所,2005.9
111.姚胜,蒲俊文.近红外光谱分析技术在木材材性分析中的研究进展[J].光谱学与光谱分析,2009,29(4)：974-978.
112.余雁,江泽慧,王戈,等.采谱方式对竹材气干密度近红外预测模型精度的影响[J].北京林业大学学报,2007,29(4)：80-83.
113.俞建成,周德成,刘木清.近红外光谱分析的应用[J].红外,2003(8)：24-27.
114.虞华强,赵荣军,傅峰,等.利用近红外光谱技术预测杉木力学性质[J].西北林学院学报；2007,22(5)：149-154.
115.袁洪福.迷人的近红外光谱分析技术发展与思考[C].全国第一届近红外光谱学术会议论文集,北京,中国石化出版社,2006.
116.袁志发,周静芋.多元统计分析[M]．北京：科学出版社,2002.
117.袁子民.红外光谱法对山药的质量评价研究[D].郑州：河南中医学院,2002
118.曾黄元,杜官本,王昌命.近红外光谱分析技术及其在木材工业领域的应用[J].西南林学院学报,2006,26(1)：90-94.
119.张鸿富,李耀翔.近红外光谱技术在木材无损检测中应用研究综述[J].森林工程,2009(05).
120.张卉,宋妍,冷静,等.近红外光谱分析技术[J].光谱实验室,2.007,24(3).388-395.
12].张军,陈华才,陈星旦.近红外光谱温度修正定量分析模型的研究[J].光谱学与光谱分析,2005,25(6)：890-893.
122.张灵帅,邢军,谷运红,等.烟草近红外光谱分析结果影响因素综述[J]：安徽农业科学,2008,36(21)：9097-9099.
123.张录达,王韬,杨丽明,等.傅里叶变换近红外全谱回归分析的应用研究[J].光谱学与光谱分析,2005,25(12)：1959-1962.
124.张银,周孟然.近红外光谱分析技术的数据处理方法[J].红外技术,2007,29(6)：345-348.
125.赵丽丽,赵龙莲,李军会,等.傅里叶变换近红外光谱仪扫描条件对数学模型预测精度的影响[J].光谱学与光谱分析,2004,24(1)：41-44.
126.赵龙莲.近红外光谱定量分析数学模型的转移[D]．北京：中国农业大学,1999.
127.赵荣军,霍小梅,张黎.剂用近红外光谱技术预测粗皮桉木材弹性模量[J].光谱学与光谱分析,2009a,29(9)：2392-2395.
128.赵枝新,金岭梅.粉碎粒度对近红外分析仪测定饲料样品干物质和粗蛋白质含量的影响[J].山东饲料,2003(2)：17-18.
129.赵枝新,金岭梅.粉碎粒度对近红外分析仪测定鱼粉和豆粕氨基酸含量的影响[J].中国饲料,2002(6)：22-23.
130.郑咏梅,张军,李荣福,等.小麦近红外特征波长提取及蛋、白质含量测定[J].激光与红外,2003,33(2)：125-127.
]31.祝诗平,王刚,杨飞,等.粉末样品颗粒大小对花椒挥发油近红外光谱定量预测的影响研究[J].光谱学与光谱分析,2008,28(4)：775-779.
132. Alves, A., Schwanninger, M., Pereira, H., et al.. Calibration of NIR to assess lignin composition (H/G ratio) in maritime pine wood using analytical pyrolysis as the reference method[J]. Holzforschung.2006,60(1),29-31.
133. Antti, H., Alexandersson, D., Sjostrom, M., et al.. Detection of kappa number distributions in kraft pulps using NIR spectroscopy and multivariate calibration[J]. TAPPI Journal,2000,83(1): 102-108.
134. Arnold, S. A., Crowley, J., Vaidyanathan, S., et al.. At-line monitoring of a submerged filamentous bacterial cultivation using near infrared spectroscopy [J]. Enzyme Microb Tech.2000,27(9), 691-697.
135. Bailleres, H., Davrieux, F., and Ham-Pichavant, F.. Near infrared analysis as a tool for rapid screening of some major wood characteristics in a Eucalyptus breeding program[J]. Ann. For. Sci. 2002,59(5-6),479-490.
136. Birkett, M. D., Gambino, M. J. T.. Estimation of pulp kappa number with near-infrared spectroscopy [J]. TAPPI Journal,1989,72:193-197.
137. Birkett, M. D., Gambino, M. J. T.. Potential applications for near-infrared spectroscopy in the pulping industry[J]. Pap. S. Afr.,1988,11-12:34-38.
138. David P. Jones, Laurence R. Schimleck, Gary F. Peter et al.. Nondestructive estimation of wood chemical composition of sections of radial wood strips by diffuse reflectance near infrared spectroscopy[J]. Wood Sci. Technol,2005,28(11)
139. DEFO Maurice; TAYLOR Adam M.; BOND Brian. Determination of moisture content and density of fresh-sawn red oak lumber by near infrared spectroscopy[J]. Forest Products Journal, 2007,7, (5):68-72
140. DeThomas, F. A., Hall, J. W., and Monfre, S. L. Real-time monitoring of polyurethane production using near infrared spectroscopy[J].1994,Talanta 41,425-431.
141. Duffy, G.g. R K. A new method of relating wood density, pulp quality, and paper properties[J]. Appita J.1989,42(3):209-214.
142. Fardim, P., M. Marcia, C., Ferreira et al. Multivariate calibration for quantitative analysis of eucalypt kraft pulp by NIR spcctrometry[J]. Wood Chem. Tech.2002,22(1):67-81.
143. Gierlinger N., schwanninger M,. Hinterstoisser B., et al.. Rapid determination of heartwood extractives in Larix sp by means of Fourier transform near infrared spectroscopy[J].2002,10(3): 203-214.
144. Gindl W., teischinger A., schwanninger M. H. B.. The relationship between near infrared spectra of radial wood surfaces and wood mechanical properties[J]. Near Infrared Spectrosc.2001,9(4): 255-261.
145. Gladstone W. T., Barefoot A. C.. Kraft pulping of early wood and latewood from loblolly pine[J]. For. Prod. J.1970,20(2):17-24.
146. Hauksson J B, Bergqvist G, Bergsten U, et al. Prediction of basic wood properties for Norway spruce. Interpretation of near infrared spectroscopy data using partial least squares regression[J]. Wood Science and Technology.2001,35(6):475-485.
147. Henrik Antti, Daniel Alexandersson. Detection of kappa number distributions in kraft pulps using nir spectroscopy and multivariate calibration[J]. TAPPI JOURNAL.2000,83(3):102-108.
148. Henriksen H C, Naes T, Rodbotten R, et al.. Prediction of important sulphite pulp properties from near infrared spectra:A feasibility study and comparison of methods[J]. Journal of Near Infrared Spectroscopy.2004(12):279-285.
149. Hirakawa Y., Yamashita K., Fujisawa Y. et al.. The effects of S2 microfibril angle and density on MOE in Sugi tree logs [A]. B. G.Butterfield. Microfibril angle in wood[C]. NewZealand: Christchurch,1998.
150. Hodge, G. R., and Woodbridge, W.C..Use of near infrared spectroscopy to predict lignin content in tropical and sub-tropical pines[J].Journal of Near Infrared Spectroscopy.2004 12(6),381-390.
151. Holmgren, A., Bertgstrom, B., Gref, R., Ericsson, A.. Detection of pinosylvins in solid wood of Scots pine using Fourier transform Raman and infrared spectroscopy[J]. Journal of Wood Chemistry and Technology,1999,19:139-150.
152. Isogai A,Usuda M. Crystallinity index of cellulosic materials[J].Sen I Gakkaishi,1990,46:324-329
153. Jones P D, Schimleck L R, Peter G F, et al. Non-destructive estimation of Pinus taeda L tracheid morphological characteristics for samples from a wide range of sites in Georgia[J]. Wood science and technology.2005,39(7):529-545.
154. Jones P D, Schimleck L R, Peter G F, et al. Nondestructive estimation of wood chemical composition of sections of radial wood strips by diffuse reflectance near infrared spectroscopy[J]. Wood Science and Technology.2006,40(8):709-720.
155. Jones P. D., L. R. Schimleck, G. F. Peter, et al. Nondestructive estimation of Pinus taeda L. wood properties for samples from a wide range of sites in Georgia[J]. Canadian Journal of Forest Research,2005,35(1):85-92.
156. Jukka Koskinen, Juha Kortelainen, Janne. Measurement of TMP properties based on NIR spectral analysis. [R],2001.
157. Jurasek, L. Toward a 3-dimensional model of lignin structure[J]. J. Pulp Pap Sci.1995,21(8): 274-279.
158. Kelley S S, Rials T G, Snell R, et al. Use of near infrared spectroscopy to measure the chemical and mechanical properties of solid wood[J]. Wood Science and Technology.2004,38(4):257-276.
159. Kellogg, R.M., C.B.R. Sastry, and R.W. Wellwood. Relationships between cellwall composition and cell wall density[J]. Wood Fib. Sci.1975,7(3):170-177.
160. Kelly, S. S., Rials, T. G.., Senll, R.. et al. Use of Near Infrared Spectroscopy to Measure the Chemical and Mechanieal Properties of Solid Wood[J]. Wood Science and Technology,2003,23(17):62-65.
161.Kenji Ono, Masakazu Hiraide, Masahiro Amari, Determination of lignin, holocellulose, and organic solvent extractives in fresh leaf, litterfall, and organic material on forest floor using near-infrared reflectance spectroscopy[J]. The Japanese Forestry Society and Springer-Verlag. 2003,14(1):191-198.
162. Kube P. D., Raymond C. A.. Prediction of whole tree basic density and pulp yield using wood core samples in Eucalyptus nitens[J]. APPITA JOURNAL.2002,55:43-48.
163. Labosky P, A study of loblolly pine growth increments. Part Ⅱ. Pulp yield and related properties[J]. Tappi J.1972,55(4):530-534.
164. Lindgren, T., Edlund, U., Iversen, T.. A multivariate characterization of crystal transformations of cellulose[J]. Cellulose,1995,2:273-288.
165. Michell A J, Schimleck L R. Developing a method for the rapid assessment of pulp yield of plantation eucalypt trees beyond the year 2000[J]. Appita Journal.1998,51(6):428-432.
166. Michell. A. J.. Pulpwood quality estimation by near-infrared spectroscopic measurements on eucalypt woods[J]. APPITA,1995,48(6):425-428.
167. Norris K H, Barnes R F, Moore J E and Shenk J S. Predicting forage quality by infrared reflectance spectroscopy[J]. Journal of Animal Science,1976,43:889-897.
168. P. D. Jones, L. R. Schimleck, G. F. Peter et al. Nondestructive estimation of Pinus taeda L. wood properties for samples from a wide range of sites in Georgia[J]. Canadian Journal of Forest Research,2005,35(1):85-92.
169. P. David Jones, Laurence R. Schimleck, Gary F. Peter et al. Nondestructive estimation of wood chemical composition of sections of radial wood strips by diffuse reflectance near infrared spectroscopy[J]. Wood Sci. Technol,2005,28(11)
170. Park R S, Go rdon F J, A gnew R E, et al. The use of N ear Infrared Reflectance Spect ro scopy on dried samp lesto p redict bio logical parameters of grass silage [J].A nimal Feed Science Techno logy,1997,68:235-246.
171. Poke F S, Raymond C A. Predicting extractives, lignin, and cellulose contents using near infrared spectroscopy on solid wood in Eucalyptus globulus[J]. Journal of Wood Chemistry and Technology.2006,26(2):187-199.
172. Poke F S, Wright J K, Raymond C A. Predicting extractives and lignin contents in Eucalyptus globulus using near infrared reflectance analysis[J]. Journal of Wood Chemistry and Technology. 2004,24(1):55-67.
173. Raymond C A, Schimleck L R, Muneri A, et al. Nondestructive sampling of eucalyptus globulus and E. nitens for wood properties:Ⅲ. Predicted pulp yield using near infrared reflectance analysis[J]. Wood Science and Technology.2001,35(3):203-215.
174. Raymond C A, Schimleck L R. Development of near infrared reflectance analysis calibrations for estimating genetic parameters for cellulose content in Eucalyptus globulus[J]. Canadian Journal of Forest Research.2002,32(1):170-176.
175. Raymond, C. A., and Schimleck, L. R.. Development of near infrared reflectance analysis calibrations for estimating genetic parameters for cellulose content in Eucalyptus globulus[J].Can. J. For. Res.2002,32(1),170-176.
176. Robert Sykes, Bailian Li, Gary Hodge et al. Prediction of loblolly pine wood properties using transmittance near-infrared spectroscopy[J]. Canadian Journal of Forest Research, 2005,35(10):2423-2431.
177. Rocco Difoggio, Examination of Some Misconceptions About Near-Infrared Analysis[J]. Appl. Spectrosc.1995,49(1):67-75.
178. Roy W., Lightle Jr., Russel E, et al. Application of near-infrared reflectance spectroscopy to pulp yield and kappa number estimation[J].. Appita Annual Conference and Exhibition incorporating the ISWFPC.2005,2(13):385-389.
179. Saka S.. Relationship between microfibrillar angles and lignin content in the S2 layer of soft wood tracheids[J].Cellulose chemistry and technology,1987,21(3):225-231.
180. Schimleck L R, Evans R,I J. Estimation of Eucalyptus delegatensis clear wood. properties by near infrared spectroscopy[J]. Can. J. For. Res.2001,31(10):1671-1675.
181. Schimleck L R, Kube P D, Raymond C A, et al. Estimation of whole-tree kraft pulp yield of Eucalyptus nitens using near-infrared spectra collected from increment cores[J]. Canadian Journal of Forest Research.2005b,35(12):2797-2805.
182. Schimleck L R, Kube P D, Raymond C A, et al. Extending near infrared reflectance (NIR) pulp yield calibrations to new sites and species[J]. Journal of Wood Chemistry and Technology.2006, 26(4):299-311.
183. Schimleck L R, Kube P D, Raymond C A. Genetic improvement of kraft pulp yield in Eucalyptus nitens using cellulose content determined by near infrared spectroscopy [J]. Canadian Journal of Forest Research.2004d 34(11):2363-2370.
184. Schimleck L R, Michell A J, Raymond C A, et al. Estimation of basic density of Eucalyptus globulus using near-infrared spectroscopy[J]. Canadian Journal of Forest Research.1999,29(2): 194-201.
185. Schimleck L R, Mora C, Daniels R F. Estimation of the physical wood properties of green Pinus taeda radial samples by near infrared spectroscopy[J]. Canadian Journal of Forest Research.2003, 33(12):2297-2305.
186. Schimleck L R, Payne P, Wearne R H. Determination of important pulp properties of hybrid poplar by near infrared spectroscopy[J]. Wood and Fiber Science.2005,37(3):462-471.
187. Schimleck L R, Yazaki Y. Analysis of Pinus radiata D. Don bark by near infrared spectroscopy[J]. Holzforschung.2003,57(5):520-526.
188. Schimleck Laurence R., Robert Evans. Estimation of Pinus radiata D. Don tracheid morphological characteristics by near infrared spectroscopy[J]. Holzforschung,2004b,58(1):66-73.
189. Schimleck, L.R., and Michel, A. J. Determination of within-tree variation of kraft pulp yield using near-infrared spectroscopy[J].1998,TAPPI J.81(5),229-236.
190. Schimleck, L. R., Mora, C., Daniels, R.F.. Estimation of tracheid morphological characteristics of green Pinus taeda L. radial strips by near infrared spectroscopy[J]. Wood and Fiber Science, 2004c,36:527-535.
191. Schimleck, L. R., Raymond, C. A., Beadle, C. L., Downes et al. Applications of NIR spectroscopy to forest research[J]. APPITA.2000,53:458-464
192. Schimleck, L. R., Raymond, C. A., Beadle, C. L., Downes, G. M., Kube, P. D., and French, J.. Applications of NIR spectroscopy to forest research[J]. Appita J.2000,53(6),458-464.
193. Schimleck, L.R, French, J.. Application of NIR spectroscopy to clonal Eucalyptus globulus samples covering a narrow range of pulp yield[J]. Appita,2002c,55;149-154.
194. Schimleck, L.R:, R. Evans.. Estimation of microfibril angle of increment cores by near infrared spectroscopy [J].IAWA J.2002b 23:225-234.
195. Schimleck, L.R., R. Evans.. Estimation of wood stiffness of increment cores by near infrared spectroscopy:the development and application of calibrations based on selected cores[J]. IAWA J, 2002a,23:217-224.
196. Schimleck, L.R., Wright, P. J., Michell A J. Near-infrared spectra and chemical compositions of E. globulus and E. nitens plantation woods[J]. APPITA.1997,50(1):40-46.
197. Schimleckl Laurence R. Estimation of microfibril angle of increment cores by near infrared spectroscopy [J]. IAWA.2002e,23(2):225-234.
198. Schimleckl Laurence R., P. David Jones, Gary F. Petere et al. Nondestructive estimation of tracheid length from sections of radial wood strips by near infrared spectroscopy[J]. Holzforschung, 2004a,58:375-381.
199. Schultz T P, B D A. Rapid secondary analysis of lignocellulose:comparison of near infrared (NIR) and fourier transform infrared (FTIR)[J]. Tappi Journal.1990,73:209-212.
200. Schwanninger,.M. Hinterstoisser, B.. Klason lignin:Modifications to improve the precision of the standardized determination[J]. Holzforschung,2002,56:161-166.
201. Schwanninger, M., and Hinterstoisser, B.. Klason lignin:Modifications to improve the precision of the standardized determination[J]. Holzforschung.2002,56(2),161-166.
202. Sefaran Nelson L; Denise Conradie, Turner P. Progress in the use of near-infrared absorption spectroscopy as a tool for the rapid determination of pulp yield in plantation eucalypts[J]. TAPPSA Journal.2000,53(11):15-17.
203. Segal L, Creely J J,Martin Jr,et al. An empirical method for estimating the degree of crystallinity of native cellulose using the X-ray diffractometer[J]. Textile Research Journal,1959,29:786-794
204. Shenk, J.S., Westethaus M. O., Hoover M. R., Analysis of forages by infrared reflectance. [J], Journal of Dairy Science.1979,62(5):807-812.
205. S(?)rensen L.K., True accuracy of near infrared spectroscopy and its dependence on precision of reference data[J]. J. Near Infrared Spectrosc.2002,10(1):15-25.
206. Sykes R, Li B, Hodge G, et al. Prediction of loblolly pine wood properties using transmittance near-infrared spectroscopy[J]. Canadian Journal of Forest Research.2005,35(10):2423-2431.
207. Tatsuhiko Yamada, Ting-Feng Yeh, Hou-Min Chang et al. Rapid analysis of transgenic trees using transmittance near-infrared spectroscopy (NIR) [J]. Holzforschung,2006,60:24-28.
208. Terdwongworakul Anupun Vittaya Punsuwan, Warunee Thanapase et al. Rapid assessment of wood chemical properties and pulp yield of Eucalyptus camaldulensis in Thailand tree plantations by near infrared spectroscopy for improving wood selection for high quality pulp[J]. The Japan Wood Research Society,2005,51:167-171.
209. Ting Feng Yeh, Hou Min Chang, John F Kadla. Rapid prediction of solid wood lignin content using Transmittance near-infrared spectroscopy[J]. Agric Food Chem.2004,52:1435-1439.
210. Tran, C. D., Oliveira, D., and Grishko, V. I.. Determination of enantiomeric compositions of pharmaceutical products by near-infrared spectrometry[J].Anal. Biochem.2004,325(2),206-214.
211. Tsuchikawa S. A review of recent near infrared research for wood and paper[J]. Applied Spectroscopy Reviews.2007,42(1):43-71.
212. Tsuchikawa, S., Torii, M., Tsutsumi, S.. Application of near infrared spectrophotometry to wood for. Calibration equations for moisture content[J]. Mokuzai Gakkaishi,1996,42:743-754.
213. Via B K, Shupe T F, Stine M, et al. Tracheid length prediction in.Pinus palustris by means of near infrared spectroscopy:The influence of age[J]. Holz als Roh-und Werkstoff.2005,63(3): 231-236.
214. Via B K, So C L, Shupe T F, et al. Ability of near infrared spectroscopy to monitor air-dry density distribution and variation of wood[J]. Wood and Fiber Science.2005,37(3):394-402.
215. Via B K. Modeling longleaf pine (Pinus palustris Mill) wood properties using near infrared spectroscopy[D]. Louisiana State University,2004.
216. Via B. K., T. F. Shupe, M. Stin. Tracheid length prediction in Pinus palustris by means of near infrared spectroscopy:the influence of age[J]. Holz als Roh und Werkstoff,2005,63(4):231-236.
217. Walke, J C., Butterfield B. G., The importance of microfibril angle for the processing industries [J]. New Zealand Forestry,1995,40(4):34-40.
218. Wesley, Larroque, O., Osborne, B. G.. Measurement of gliadin and glutenin content of flour by NIR spectroscopy[J]. J. Cereal Sci.2001,34(2),125-133.
219. Williams P C, Norris K H, Sobering D C. Determination of protein and moisture in wheat and barley by near infrared transmission. [J]. Agric FoodChem,1985,33:239-244
220. Williams, P. C, and Sobering, D. C. Comparison of commercial near infrared transmittance and reflectance instruments for analysis of whole grains and seeds. [J]Near Infrared Spectrosc.1993, 1:25-32.
221. WoldS.AnttiH.Lindgren.rOmhnaJ.orthogonalsignalorredtionofnear-infraredSpectra,Chemon.nItell .Lab.Syst.[J],1998,44:75-185.
222. Wright J A, M D Bitkett M J T G. Prediction of pulp yield and cellulose content from wood samples using near infrared reflectance spectroscopy[J]. TAPPI JOURNAL.1990,73(8):165-166.
223. Wright, J. A., Birkett, M. D., and Gambino, M. J. T.. Prediction of pulp yield and cellulose content from wood samples using near infrared reflectance spectroscopy[J].TAPPI J..1990,73(8),164-166.
224. XuYouming, ChenShiyin, FangWenb. Variation of tracheid length arid Microfibril angle and the irrelationship of slash pine grown in different sites[J]. Journal of Huazhong Agricultural University,1999.18(1):83-88.
225. Yamada T, Yeh T F, Chang H M, et al. Rapid analysis of transgenic trees using transmittance near-infrared spectroscopy (NIR)[J]; Holzforschung.2006,60(1):24-28.
226. Yao Sheng, Guofeng Wu, Mian Xing, et al. Determination of lignin content in acacia spp. using near infrared reflectance spectroscopy, BioResources,5(2),556-562.
227. Yao Sheng, Pu Junwen, Wu Guofeng, et al. Determination of Holocelloluse and Alpha-celloluse Contents in Acacia spp. using NIRS. The 14th International Conference on Near Infrared Spectroscopy. Thailand,2009.11
228. Yao Sheng, Pu Junwen, Xin Mian, et al. Paper Properties of the ECF Bleached Pulp from Triploid of Populus tomentosa Carr,IAWPS2008, Harbin,2008.9.
229. Yao Sheng, Pu Junwen, Zhang Yong, et al. Comparison of Pulp Properties from ASAM, Kraft and ASAQ Processes of Triploid of Populus tomentosa Carr.,2nd International Papermaking and Environment Conference, tianjin,2008.5,395-398.
230. Yeh, T F, Chang H M K J. Rapid prediction of solid wood lignin content using transmittance near-infrared spectroscopy[J]. Journal of Agricultural and Food Chemistry.2004,52:1435-1439.
231. Yeh, T. F., Chang, H. M., and Kadla, J. F.. Rapid prediction of solid wood lignin content using transmittance near-infrared spectroscopy[J] Journal of Agricultural and Food Chemistry.2004,52(6),1435-1439.
232. Yuzak E, Lohrke C. At-Line Kappa Number Measurement by Near-Infrared Spectroscopy[C]. 1993.
233. Ze-hui Jiang, Zhong Yang, Chi-leung So C H. Rapid prediction of wood crystallinity in Pinus elliotii plantation wood by near-infrared spectroscopy [J]. Journal of Wood Science.2007,53(5): 449-453.
2.陈诚,蒲俊文,姚胜,等.速生阔叶材杂交相思制浆性能的研究[J].造纸科学与技术,2009,28(4)：1-3，6.
3.陈诚,蒲俊文,姚胜,等.五种相思树制浆适应性能研究[J].中华纸业,2009(17)：80-82.
4.褚小立,陆婉珍.近红外化学成像的原理、仪器及应用[J].分析仪器,2008(4)：1-5.
5.褚小立,袁洪福,陆婉珍.基础数据准确性对近红外光谱分析结果的影响[J].光谱学与光谱分析,2005,25(6)：886-889.
6.褚小立,袁洪福,陆婉珍.近红外分析中光谱预处理及波长选用方法进展与应用[J].化学进展,2004,16(4)：528-542.
7.邓拥军,房桂干,李萍,等.四种相思树硫酸盐法制浆性能的比较[J].陕西科技大学学报,2006,24(2)：1-7.
8.董守龙,任芊,黄友之.近红外光谱分析技术的发展和应用[J].化工生产与技术,2004,1(6)：44-46.
9.段焰青,者为,杨涛,等.影响烟草近红外光谱分析结果准确性的因素[J].光谱实验室,2007,24(4)：705-710.
10.房桂干,王静霞.五种澳洲速生相思树制浆性能研究[J].林产化工通讯,1992,5：10-13.
11.费本华,江泽慧,阮锡根.银杏木材微纤丝角及其与生长轮密度相关模型的建立[J].木材工业,2005.14(3)：13-15.
12.袱香香,杨文忠,方升佐.木材微纤丝角研究的现状和发展趋势[J].南京林业大学学报,2002,26(6)：83-87.
13.高荣强,范世福,严衍禄,等.近红外光谱的数据预处理研究[J].光谱学与光谱分析,2004,24(12)：1563-1565.
14.高荣强,范世福.现代近红外光谱分析技术的原理及应用[J].分析仪器,2002(3)：9-12.
15.龚木荣,洪启清,沈文瑛,等.不同树龄马占相思制浆性能的研究[J].林产工业,1998,25(4)22-25
16.龚木荣,洪启清,张大同.低龄厚荚相思木的制浆造纸性能[J]．南京林业大学学报(自然科学版),2001,25(6)：48-50.
17.龚木荣,洪唐清,张大同.九年生马占、大叶、厚荚相思制浆性能的比较[J].林产工业,2001,28(6)：25-27.
18.龚木荣,李忠正.六年生马占、大叶、厚荚3种相思树制浆性能比较[J],2002,1：1-3.
19.龚玉梅,张炜.近红外光谱检测技术及其在林业中的应用[J].光谱学与光谱分析,2008,28(7)：1544-1548.
20.郭德荣.人工林红松微纤丝角变异与管胞长度和拉伸强度的关系[J].东北林业大学学报,1982,10(2).19-47.
21.黄安民,费本华,江泽慧,等.表面粗糙度对近红外光谱分析木材密度的影响[J].光谱学与光谱分析,2007,27(9)：1700-1702.
22.黄安民,江泽慧,李改云.杉木综纤维素和木质素的近红外光谱法测定[J].光谱学与光谱分析,2007,27(7)：1328-1331.
23.黄安民,江泽慧.近红外光谱技术在木材性质预测中的应用研究进展[J].世界林业研究,2007,20(1)：49-54.
24.黄安民.人工林杉木木材性质的近红外光谱预测[D]．北京：中国林业科学研究院,2006.9
25.纪淑娟,李东华,重滕和明.不同扫描方式南果梨近红外预测模型差异性研究[J].食品研究与开发,2008,29(7)：108-110.
26.江泽慧,费本华,杨忠.光谱预处理对近红外光谱预测木材纤维素结晶度的影响[J].光谱学与光谱分析,2007(03)：435-438.
27.江泽慧,黄安民,费本华,等.利用近红外光谱和X射线衍射技术分析木材微纤丝角[J].光谱学与光谱分析,2006,26(7)：1230-1233.
28.江泽慧,黄安民,王斌.木材不同切面的近红外光谱信息与密度快速预测[J].光谱学与光谱分析,2006,26(6)：1034-1037.
29.江泽慧,黄安民.木材中的水分及其近红外光谱分析[J].光谱学与光谱分析,2006,26(8)：1464-1468.
30.江泽慧,李改云,王戈,等.近红外光谱法测定毛竹综纤维素的含量研究[J].林产化学与工业,2007,27(1)：15-18.
31.江泽慧,刘君良,覃道春.相思类树种木材的资源、材性与加工利用[J].木材工业,2002,16(6)：6-9.
32.江泽慧,彭镇华.世界主要树种木材科学特性[M]．北京：科学出版社,2001,7.
33.江泽慧,杨忠,王戈,等.湿地松木材近红外光谱与其结晶度的相关性[J].林业科学,2007,43(10)：95-99.
34.江泽慧.中国林业工程[M].济南：济南出版社,2002,5.
35.姜伟,韩光亭,张元明.近红外光谱技术在植物纤维化学成分预测中的应用[J].中国纤检,2009,(4)：66-68.
36.蒋华松,沈文瑛,张大同,等.阔叶材原料特性与制浆之间关系的探讨[J].中国造纸,1998,1：36-39.
37.蒋焕煜,彭永石,谢丽娟,等.扫描次数对番茄叶漫反射光谱和模型精度的影响研究[J].光谱学与光谱分析,2008,28(8).1763-1733.
38.黎庆涛,李小梅,余炼,等：近红外漫反射光谱法测定纸浆卡伯值[J].中国造纸,2004,23(4)：8-10.
39.李改云,黄安民,王戈,等.近红外光谱法快速测定毛竹Klason木质素的含量[J].光谱学与光谱分析,2007,27(10)：1977-1980.
40.李坤,付时雨,詹怀宇.近红外光谱分析技术在制浆造纸中的应用[J].造纸科学与技术,2008(4)：40-44.
41.李小梅,黎庆涛,罗伟强,等.近红外光谱法分析植物纤维原料的组成[J].纸和造纸,2008,27(5)：58-61.
42.李小梅,王双飞.近红外光谱技术在造纸工业中的应用[J].中国造纸学报,2003,18(2)：189-194.
43.李欣雨.用近红外和拉曼光谱法预测木材和纸浆的性能[J].国际造纸,2008(05)：30-34.
44.李旭东,张根明.近红外光谱分析技术的发展与应用研究[J].重庆科技学院学报：自然科学版,2008,10(2)：33-35,40-44.
45.李炎,张月学,徐香玲,等.近红外反射光谱(NIRS)分析技术及其在农业上的应用[J].黑龙江农业科学,2008,(1)：105-108.
46.李勇,魏益民,王锋.影响近红外光谱分析结果准确性的因素[J].核农学报,2005,19(3)：236-240.
47.李忠正.林纸一体化与中国主要速生人工造纸树种的制浆造纸性能[J].中华纸业，2001,22.(7)：6-12.
48.练文柳,吴名剑,孙贤军,等.不同预处理方法对烟草近红外光谱预测模型的影响[J].烟草科技,2005,(2)：19-23.
49.梁高峰,贾宏汝,谷运红,等.近红外光谱分析技术及其在农业研究中的应用[J].安徽农业科学,2007,35(29)：9113-9115.
50.刘发龙,马新刚,程福银,等.近红外光谱分析技术在快速分析上的应用[J].分析测试技术与仪器,2008,14(4)：241-247.
51.刘光良.相思属树(Acacia sp.)材性及制浆造纸适应性(文献综述)[J].林产化工通讯,1997,31(5)：31-10.
52.刘宁宇,王伟,吴拥军.近红外光谱技术在药物分析中的应用[J].安阳师范学院学报,2005,(5)：50-52.
53.刘鹏,王志杰,陈永常.近红外光谱技术在造纸中的应用[J].黑龙江造纸,2009,37(1)：29-30.
54.刘艳青.近红外光谱分析的技术与应用[J].制造业自动化,2002,24(2)：61-62.
55.刘泽春,张峰,谢卫.烟草近红外光谱模型的适配性研究[J].烟草科技,2008,(5)：34-37.
56.卢长武,陆德俊.近红外水分仪在高定量浆板生产中的应用[J].中国造纸,2005,24(4)：65-66.
57.陆婉珍,袁洪福,徐广通,等.现代近红外光谱分析技术[M].北京：中国石化出版社,2000.
58.陆文达.近红外光谱(NIR)应用于密度和刚度变异宽广的多样样树种木材[J].国际木业,2002,32(12)：32.
59.陆文达.可见光谱和近红外光谱(VIS and NIR-Spectroscopy)在无损检测木材纹理角度方面的潜力研究[J].国际木业,2003,33(5)：34.
60.闵顺耕,李宁,张明祥.近红外光谱分析中异常值的判别与定量模型优化[J].光谱学与光谱分析,2004,24(10)：1205-1209.
61.尼珍,胡昌勤,冯芳.近红外光谱分析中光谱预处理方法的作用及其发展[J]．药物分析杂志,2008,28(5)：824-829.
62.潘志刚,吕鹏信,陆熙娴.中国厚英相思木制浆特性的研究[J].林产工业,1994,21(5)：17-19
63.潘志钢.中国主要外来树种引种栽培.北京：北京科学技术出版社[M],1994.
64.彭玉魁.近红外光谱分析技术及其在农业中的应用[J].陕西农业科学,2001(3)：25-28
65.蒲俊文,宋君龙,谢益民,等.三倍体毛白杨木质素结构特性研究[J].北京林业大学学报,2002(6)：211-215.
66.蒲俊文,宋君龙.三倍体毛白杨化学成分径向变异的研究[J].造纸科学与技术,2002,21(3)：1-3,7.
67.蒲俊文,宋君龙.三倍体毛白杨无性系纸浆材的选优[J].造纸科学与技术,2001,20(5)：11-13.
68.蒲俊文,宋君龙.三倍体毛白杨纤维形态变异的研究[J]．北京林业大学学报,2002,24(2)：62-66.
69.蒲俊文,谢益民.三倍体毛白杨木质素结构特性研究[J]．北京林业大学学报,2002,24(5)：211-215.
70.秦冲,陈雯雯,何雄奎,等.近红外光谱分析中建模校正集的选择[J].光谱学与光谱分析,2009(10)：2661-2664.
71.沈文浩,谢益民,刘焕彬.化学计量学法处理可见光谱定量测定纸浆卡伯值[J].广东造纸,1999,(5-6)：90-95
72.沈文浩,谢益民,刘焕彬.人工神经网络在纸浆卡伯值光学定量分析中的应用[J].高等学校化学学报,2000,2](8)：1208-1210.
73.沈文浩,谢益民.近红外光谱技术在制浆造纸工业中的应用[J].广东造纸,1999(2)：11-13.
74.史永刚,冯新泸,李子存,等.近红外光谱分析中应考虑的几个问题[J].光谱实验室,2001,18(4)：435-437.
75.舒庆尧,吴殿星,夏英武.用近红外反射光谱测定小样本糙米粉的品质性状[J].中国农业科学,1999,32(4)：92-97.
76.宋志远,林元奎.近红外光谱分析技术在油品分析中的应用[J].分析仪器,1995(3)：41-45.
77.王丹红,吴文晞,林志武,等.近红外光谱法鉴别Tencel等四种纤维[J].福建分析测试,2009,18(4)：32-34.
78.王冬,叶升锋,闵顺耕,等.近红外光谱分辨率对液体样品近红外定量模型影响的研究[J].光谱学与光谱分析,2009,29(7)：1813-1817.
79.王海东,秦玉昌,吕小文,等.近红外光谱技术(NIRS)测定玉米粉碎粒度的研究[J]．饲料工业,2008,29(19)：4144.
80.王海水,汪冬梅,等.近红外光谱在品质分析和定量分析中的应用[J].分析测试技术与仪器,2002,8(3)：136-138.
81.王丽杰,郭建英,徐可欣.近红外光谱分析中建模样品优选方法的研究[J].红外技术,2005,27(1)：75-78.
82.王文真.在近红外光谱定量分析中应注意的几个问题[J],1996,(1)124-25.
‘83.王学琳,孔淑萍.傅里叶变换近红外光谱进展[J].现代仪器,1999(6)：1-3.
84.王学顺,戚大伟,黄安民.基于小波变换的木材近红外光谱去噪研究[J].光谱学与光谱分析,2009,29(08)：2059-2062.
85.王学顺,戚大伟,黄安民.基于小波模极大值的木材近红外光谱去噪[J].林业科学,2008,44(10)：109-112.
86.王学顺,戚大伟,黄安民.木材近红外光谱小波阈值去噪方法[J].东北林业大学学报，2009a，37(2)：32-34.
87.王一兵,王红宇,翟宏菊,等.近红外光谱分辨率对定量分析的影响[J].分析化学,2006,34(5)：699-701.
88.王玉荣,费本华,傅峰,等.基于近红外光谱技术预测木材纤维长度[J].中国造纸,2008,27(06)：6-9.。
89.王玉荣,江泽慧,赵荣军,等.快速检测木材微纤丝角的近红外光谱分析技术[J].林业机械与木工设备,2007,35(7)：35-36.
90.王志玲,王正,王元秀,等.现代近红外光谱技术——人造板性能无损检测的新方法[J].世界林业研究,2004,17(6)：22-24.
91.魏良明.普通玉米籽粒品质性状的遗传及其近红外测定方法的研究[D].北京：中国农业大学,2003.
92.吴静珠,王一鸣,张小超,等.近红外光谱分析中定标集样品挑选方法研究[J].农业机械学报,2006,37(4)：80-82.
93-吴新生,谢益民,刘焕彬,等.蒸煮过程纸浆卡伯值近红外光谱法在线测定[J].中国造纸学报,2003,18(1)：110-113.
94.吴新生,谢益民,帅兴华.基于近红外光谱法的造纸用木材原料的快速分类[J].中国造纸学报,2007,22(3)：14-16.
95.吴新生,谢益民.基于近红外光谱法的木材原料的Kohonen网络分类法[J].造纸科学与技术,2009b,28(4)：4-6.
96.吴新生,谢益民.主成分.逐步回归近红外光谱法测量纸浆卡伯值[J].光谱实验室,2005,22(1)：116-119.
97.夏柏杨,任芊.近红外光谱分析技术的一些数据处理方法的讨论[J].光谱实验室,2005,22(3)：629-634.
98.夏俊芳,李培武,李小昱,等.不同预处理对近红外光谱检测脐橙VC含量的影响[J].农业机械学报,2007,38(6)：107-111.
99.谢丽娟,刘东红,张宇环,等.分辨率对近红外光谱和定量分析的影响研究[J].光谱学与光谱分析,2007,27(8)：1489-1492.
100.谢益民,吴新生,刘焕彬,等.硫酸盐法蒸煮脱木素历程的近红外光谱表征法[J].中国造纸学报,1999,14(1)：6-8.
101.谢益民,伍红,郑志彤,等.蓝桉的树龄与化学组成及硫酸盐法制浆特性的关系[J].广东造纸.1999,1：7-10.
102.徐广通,袁洪福,陆婉珍.近红外光谱定量交叉检验模型适用性研究[J].光谱学与光谱分析,2001,21(4)：459-463.
103.徐广通,袁洪福.现代近红外光谱技术及应用进展[J].光谱学与光谱分析,2000,20(2)：134-142.
104.徐坤,刘鹏起,张玉娜,等.近红外光谱分析技术及应用[J].莱阳农学院学报,2001,18(3)：237-240.
105.薛国新,郑建文,洪启清.硫化钠预处理大叶相思树硫酸盐法深度脱木素蒸煮技术[J].中国造纸学报,1999,14(增刊)：8-12.
106.薛国新,郑建文,胡建安.不同树龄相思树混合木片硫酸盐法制浆[J].中国造纸1999,7：20-23.
]07.严衍禄.近红外光谱分析基础与应用[M]．北京：中国轻工业出版社,2005：128-129.
108.杨春节,何川,宋执环.基于近红外光谱与支持向量机的纸浆卡伯值在线测量[J].光谱学与光谱分析,2008,28(8)：1795-1798.
109.杨忠,江泽慧,费本华,等.近红外光谱技术及其在木材科学中的应用[J].林业科学,2005,41(4)：177-183.
110.杨忠.近红外光谱预测人工林湿地松木材性质与腐朽特性的研究[D]．北京：中国林业科学研究所,2005.9
111.姚胜,蒲俊文.近红外光谱分析技术在木材材性分析中的研究进展[J].光谱学与光谱分析,2009,29(4)：974-978.
112.余雁,江泽慧,王戈,等.采谱方式对竹材气干密度近红外预测模型精度的影响[J].北京林业大学学报,2007,29(4)：80-83.
113.俞建成,周德成,刘木清.近红外光谱分析的应用[J].红外,2003(8)：24-27.
114.虞华强,赵荣军,傅峰,等.利用近红外光谱技术预测杉木力学性质[J].西北林学院学报；2007,22(5)：149-154.
115.袁洪福.迷人的近红外光谱分析技术发展与思考[C].全国第一届近红外光谱学术会议论文集,北京,中国石化出版社,2006.
116.袁志发,周静芋.多元统计分析[M]．北京：科学出版社,2002.
117.袁子民.红外光谱法对山药的质量评价研究[D].郑州：河南中医学院,2002
118.曾黄元,杜官本,王昌命.近红外光谱分析技术及其在木材工业领域的应用[J].西南林学院学报,2006,26(1)：90-94.
119.张鸿富,李耀翔.近红外光谱技术在木材无损检测中应用研究综述[J].森林工程,2009(05).
120.张卉,宋妍,冷静,等.近红外光谱分析技术[J].光谱实验室,2.007,24(3).388-395.
12].张军,陈华才,陈星旦.近红外光谱温度修正定量分析模型的研究[J].光谱学与光谱分析,2005,25(6)：890-893.
122.张灵帅,邢军,谷运红,等.烟草近红外光谱分析结果影响因素综述[J]：安徽农业科学,2008,36(21)：9097-9099.
123.张录达,王韬,杨丽明,等.傅里叶变换近红外全谱回归分析的应用研究[J].光谱学与光谱分析,2005,25(12)：1959-1962.
124.张银,周孟然.近红外光谱分析技术的数据处理方法[J].红外技术,2007,29(6)：345-348.
125.赵丽丽,赵龙莲,李军会,等.傅里叶变换近红外光谱仪扫描条件对数学模型预测精度的影响[J].光谱学与光谱分析,2004,24(1)：41-44.
126.赵龙莲.近红外光谱定量分析数学模型的转移[D]．北京：中国农业大学,1999.
127.赵荣军,霍小梅,张黎.剂用近红外光谱技术预测粗皮桉木材弹性模量[J].光谱学与光谱分析,2009a,29(9)：2392-2395.
128.赵枝新,金岭梅.粉碎粒度对近红外分析仪测定饲料样品干物质和粗蛋白质含量的影响[J].山东饲料,2003(2)：17-18.
129.赵枝新,金岭梅.粉碎粒度对近红外分析仪测定鱼粉和豆粕氨基酸含量的影响[J].中国饲料,2002(6)：22-23.
130.郑咏梅,张军,李荣福,等.小麦近红外特征波长提取及蛋、白质含量测定[J].激光与红外,2003,33(2)：125-127.
]31.祝诗平,王刚,杨飞,等.粉末样品颗粒大小对花椒挥发油近红外光谱定量预测的影响研究[J].光谱学与光谱分析,2008,28(4)：775-779.
132. Alves, A., Schwanninger, M., Pereira, H., et al.. Calibration of NIR to assess lignin composition (H/G ratio) in maritime pine wood using analytical pyrolysis as the reference method[J]. Holzforschung.2006,60(1),29-31.
133. Antti, H., Alexandersson, D., Sjostrom, M., et al.. Detection of kappa number distributions in kraft pulps using NIR spectroscopy and multivariate calibration[J]. TAPPI Journal,2000,83(1): 102-108.
134. Arnold, S. A., Crowley, J., Vaidyanathan, S., et al.. At-line monitoring of a submerged filamentous bacterial cultivation using near infrared spectroscopy [J]. Enzyme Microb Tech.2000,27(9), 691-697.
135. Bailleres, H., Davrieux, F., and Ham-Pichavant, F.. Near infrared analysis as a tool for rapid screening of some major wood characteristics in a Eucalyptus breeding program[J]. Ann. For. Sci. 2002,59(5-6),479-490.
136. Birkett, M. D., Gambino, M. J. T.. Estimation of pulp kappa number with near-infrared spectroscopy [J]. TAPPI Journal,1989,72:193-197.
137. Birkett, M. D., Gambino, M. J. T.. Potential applications for near-infrared spectroscopy in the pulping industry[J]. Pap. S. Afr.,1988,11-12:34-38.
138. David P. Jones, Laurence R. Schimleck, Gary F. Peter et al.. Nondestructive estimation of wood chemical composition of sections of radial wood strips by diffuse reflectance near infrared spectroscopy[J]. Wood Sci. Technol,2005,28(11)
139. DEFO Maurice; TAYLOR Adam M.; BOND Brian. Determination of moisture content and density of fresh-sawn red oak lumber by near infrared spectroscopy[J]. Forest Products Journal, 2007,7, (5):68-72
140. DeThomas, F. A., Hall, J. W., and Monfre, S. L. Real-time monitoring of polyurethane production using near infrared spectroscopy[J].1994,Talanta 41,425-431.
141. Duffy, G.g. R K. A new method of relating wood density, pulp quality, and paper properties[J]. Appita J.1989,42(3):209-214.
142. Fardim, P., M. Marcia, C., Ferreira et al. Multivariate calibration for quantitative analysis of eucalypt kraft pulp by NIR spcctrometry[J]. Wood Chem. Tech.2002,22(1):67-81.
143. Gierlinger N., schwanninger M,. Hinterstoisser B., et al.. Rapid determination of heartwood extractives in Larix sp by means of Fourier transform near infrared spectroscopy[J].2002,10(3): 203-214.
144. Gindl W., teischinger A., schwanninger M. H. B.. The relationship between near infrared spectra of radial wood surfaces and wood mechanical properties[J]. Near Infrared Spectrosc.2001,9(4): 255-261.
145. Gladstone W. T., Barefoot A. C.. Kraft pulping of early wood and latewood from loblolly pine[J]. For. Prod. J.1970,20(2):17-24.
146. Hauksson J B, Bergqvist G, Bergsten U, et al. Prediction of basic wood properties for Norway spruce. Interpretation of near infrared spectroscopy data using partial least squares regression[J]. Wood Science and Technology.2001,35(6):475-485.
147. Henrik Antti, Daniel Alexandersson. Detection of kappa number distributions in kraft pulps using nir spectroscopy and multivariate calibration[J]. TAPPI JOURNAL.2000,83(3):102-108.
148. Henriksen H C, Naes T, Rodbotten R, et al.. Prediction of important sulphite pulp properties from near infrared spectra:A feasibility study and comparison of methods[J]. Journal of Near Infrared Spectroscopy.2004(12):279-285.
149. Hirakawa Y., Yamashita K., Fujisawa Y. et al.. The effects of S2 microfibril angle and density on MOE in Sugi tree logs [A]. B. G.Butterfield. Microfibril angle in wood[C]. NewZealand: Christchurch,1998.
150. Hodge, G. R., and Woodbridge, W.C..Use of near infrared spectroscopy to predict lignin content in tropical and sub-tropical pines[J].Journal of Near Infrared Spectroscopy.2004 12(6),381-390.
151. Holmgren, A., Bertgstrom, B., Gref, R., Ericsson, A.. Detection of pinosylvins in solid wood of Scots pine using Fourier transform Raman and infrared spectroscopy[J]. Journal of Wood Chemistry and Technology,1999,19:139-150.
152. Isogai A,Usuda M. Crystallinity index of cellulosic materials[J].Sen I Gakkaishi,1990,46:324-329
153. Jones P D, Schimleck L R, Peter G F, et al. Non-destructive estimation of Pinus taeda L tracheid morphological characteristics for samples from a wide range of sites in Georgia[J]. Wood science and technology.2005,39(7):529-545.
154. Jones P D, Schimleck L R, Peter G F, et al. Nondestructive estimation of wood chemical composition of sections of radial wood strips by diffuse reflectance near infrared spectroscopy[J]. Wood Science and Technology.2006,40(8):709-720.
155. Jones P. D., L. R. Schimleck, G. F. Peter, et al. Nondestructive estimation of Pinus taeda L. wood properties for samples from a wide range of sites in Georgia[J]. Canadian Journal of Forest Research,2005,35(1):85-92.
156. Jukka Koskinen, Juha Kortelainen, Janne. Measurement of TMP properties based on NIR spectral analysis. [R],2001.
157. Jurasek, L. Toward a 3-dimensional model of lignin structure[J]. J. Pulp Pap Sci.1995,21(8): 274-279.
158. Kelley S S, Rials T G, Snell R, et al. Use of near infrared spectroscopy to measure the chemical and mechanical properties of solid wood[J]. Wood Science and Technology.2004,38(4):257-276.
159. Kellogg, R.M., C.B.R. Sastry, and R.W. Wellwood. Relationships between cellwall composition and cell wall density[J]. Wood Fib. Sci.1975,7(3):170-177.
160. Kelly, S. S., Rials, T. G.., Senll, R.. et al. Use of Near Infrared Spectroscopy to Measure the Chemical and Mechanieal Properties of Solid Wood[J]. Wood Science and Technology,2003,23(17):62-65.
161.Kenji Ono, Masakazu Hiraide, Masahiro Amari, Determination of lignin, holocellulose, and organic solvent extractives in fresh leaf, litterfall, and organic material on forest floor using near-infrared reflectance spectroscopy[J]. The Japanese Forestry Society and Springer-Verlag. 2003,14(1):191-198.
162. Kube P. D., Raymond C. A.. Prediction of whole tree basic density and pulp yield using wood core samples in Eucalyptus nitens[J]. APPITA JOURNAL.2002,55:43-48.
163. Labosky P, A study of loblolly pine growth increments. Part Ⅱ. Pulp yield and related properties[J]. Tappi J.1972,55(4):530-534.
164. Lindgren, T., Edlund, U., Iversen, T.. A multivariate characterization of crystal transformations of cellulose[J]. Cellulose,1995,2:273-288.
165. Michell A J, Schimleck L R. Developing a method for the rapid assessment of pulp yield of plantation eucalypt trees beyond the year 2000[J]. Appita Journal.1998,51(6):428-432.
166. Michell. A. J.. Pulpwood quality estimation by near-infrared spectroscopic measurements on eucalypt woods[J]. APPITA,1995,48(6):425-428.
167. Norris K H, Barnes R F, Moore J E and Shenk J S. Predicting forage quality by infrared reflectance spectroscopy[J]. Journal of Animal Science,1976,43:889-897.
168. P. D. Jones, L. R. Schimleck, G. F. Peter et al. Nondestructive estimation of Pinus taeda L. wood properties for samples from a wide range of sites in Georgia[J]. Canadian Journal of Forest Research,2005,35(1):85-92.
169. P. David Jones, Laurence R. Schimleck, Gary F. Peter et al. Nondestructive estimation of wood chemical composition of sections of radial wood strips by diffuse reflectance near infrared spectroscopy[J]. Wood Sci. Technol,2005,28(11)
170. Park R S, Go rdon F J, A gnew R E, et al. The use of N ear Infrared Reflectance Spect ro scopy on dried samp lesto p redict bio logical parameters of grass silage [J].A nimal Feed Science Techno logy,1997,68:235-246.
171. Poke F S, Raymond C A. Predicting extractives, lignin, and cellulose contents using near infrared spectroscopy on solid wood in Eucalyptus globulus[J]. Journal of Wood Chemistry and Technology.2006,26(2):187-199.
172. Poke F S, Wright J K, Raymond C A. Predicting extractives and lignin contents in Eucalyptus globulus using near infrared reflectance analysis[J]. Journal of Wood Chemistry and Technology. 2004,24(1):55-67.
173. Raymond C A, Schimleck L R, Muneri A, et al. Nondestructive sampling of eucalyptus globulus and E. nitens for wood properties:Ⅲ. Predicted pulp yield using near infrared reflectance analysis[J]. Wood Science and Technology.2001,35(3):203-215.
174. Raymond C A, Schimleck L R. Development of near infrared reflectance analysis calibrations for estimating genetic parameters for cellulose content in Eucalyptus globulus[J]. Canadian Journal of Forest Research.2002,32(1):170-176.
175. Raymond, C. A., and Schimleck, L. R.. Development of near infrared reflectance analysis calibrations for estimating genetic parameters for cellulose content in Eucalyptus globulus[J].Can. J. For. Res.2002,32(1),170-176.
176. Robert Sykes, Bailian Li, Gary Hodge et al. Prediction of loblolly pine wood properties using transmittance near-infrared spectroscopy[J]. Canadian Journal of Forest Research, 2005,35(10):2423-2431.
177. Rocco Difoggio, Examination of Some Misconceptions About Near-Infrared Analysis[J]. Appl. Spectrosc.1995,49(1):67-75.
178. Roy W., Lightle Jr., Russel E, et al. Application of near-infrared reflectance spectroscopy to pulp yield and kappa number estimation[J].. Appita Annual Conference and Exhibition incorporating the ISWFPC.2005,2(13):385-389.
179. Saka S.. Relationship between microfibrillar angles and lignin content in the S2 layer of soft wood tracheids[J].Cellulose chemistry and technology,1987,21(3):225-231.
180. Schimleck L R, Evans R,I J. Estimation of Eucalyptus delegatensis clear wood. properties by near infrared spectroscopy[J]. Can. J. For. Res.2001,31(10):1671-1675.
181. Schimleck L R, Kube P D, Raymond C A, et al. Estimation of whole-tree kraft pulp yield of Eucalyptus nitens using near-infrared spectra collected from increment cores[J]. Canadian Journal of Forest Research.2005b,35(12):2797-2805.
182. Schimleck L R, Kube P D, Raymond C A, et al. Extending near infrared reflectance (NIR) pulp yield calibrations to new sites and species[J]. Journal of Wood Chemistry and Technology.2006, 26(4):299-311.
183. Schimleck L R, Kube P D, Raymond C A. Genetic improvement of kraft pulp yield in Eucalyptus nitens using cellulose content determined by near infrared spectroscopy [J]. Canadian Journal of Forest Research.2004d 34(11):2363-2370.
184. Schimleck L R, Michell A J, Raymond C A, et al. Estimation of basic density of Eucalyptus globulus using near-infrared spectroscopy[J]. Canadian Journal of Forest Research.1999,29(2): 194-201.
185. Schimleck L R, Mora C, Daniels R F. Estimation of the physical wood properties of green Pinus taeda radial samples by near infrared spectroscopy[J]. Canadian Journal of Forest Research.2003, 33(12):2297-2305.
186. Schimleck L R, Payne P, Wearne R H. Determination of important pulp properties of hybrid poplar by near infrared spectroscopy[J]. Wood and Fiber Science.2005,37(3):462-471.
187. Schimleck L R, Yazaki Y. Analysis of Pinus radiata D. Don bark by near infrared spectroscopy[J]. Holzforschung.2003,57(5):520-526.
188. Schimleck Laurence R., Robert Evans. Estimation of Pinus radiata D. Don tracheid morphological characteristics by near infrared spectroscopy[J]. Holzforschung,2004b,58(1):66-73.
189. Schimleck, L.R., and Michel, A. J. Determination of within-tree variation of kraft pulp yield using near-infrared spectroscopy[J].1998,TAPPI J.81(5),229-236.
190. Schimleck, L. R., Mora, C., Daniels, R.F.. Estimation of tracheid morphological characteristics of green Pinus taeda L. radial strips by near infrared spectroscopy[J]. Wood and Fiber Science, 2004c,36:527-535.
191. Schimleck, L. R., Raymond, C. A., Beadle, C. L., Downes et al. Applications of NIR spectroscopy to forest research[J]. APPITA.2000,53:458-464
192. Schimleck, L. R., Raymond, C. A., Beadle, C. L., Downes, G. M., Kube, P. D., and French, J.. Applications of NIR spectroscopy to forest research[J]. Appita J.2000,53(6),458-464.
193. Schimleck, L.R, French, J.. Application of NIR spectroscopy to clonal Eucalyptus globulus samples covering a narrow range of pulp yield[J]. Appita,2002c,55;149-154.
194. Schimleck, L.R:, R. Evans.. Estimation of microfibril angle of increment cores by near infrared spectroscopy [J].IAWA J.2002b 23:225-234.
195. Schimleck, L.R., R. Evans.. Estimation of wood stiffness of increment cores by near infrared spectroscopy:the development and application of calibrations based on selected cores[J]. IAWA J, 2002a,23:217-224.
196. Schimleck, L.R., Wright, P. J., Michell A J. Near-infrared spectra and chemical compositions of E. globulus and E. nitens plantation woods[J]. APPITA.1997,50(1):40-46.
197. Schimleckl Laurence R. Estimation of microfibril angle of increment cores by near infrared spectroscopy [J]. IAWA.2002e,23(2):225-234.
198. Schimleckl Laurence R., P. David Jones, Gary F. Petere et al. Nondestructive estimation of tracheid length from sections of radial wood strips by near infrared spectroscopy[J]. Holzforschung, 2004a,58:375-381.
199. Schultz T P, B D A. Rapid secondary analysis of lignocellulose:comparison of near infrared (NIR) and fourier transform infrared (FTIR)[J]. Tappi Journal.1990,73:209-212.
200. Schwanninger,.M. Hinterstoisser, B.. Klason lignin:Modifications to improve the precision of the standardized determination[J]. Holzforschung,2002,56:161-166.
201. Schwanninger, M., and Hinterstoisser, B.. Klason lignin:Modifications to improve the precision of the standardized determination[J]. Holzforschung.2002,56(2),161-166.
202. Sefaran Nelson L; Denise Conradie, Turner P. Progress in the use of near-infrared absorption spectroscopy as a tool for the rapid determination of pulp yield in plantation eucalypts[J]. TAPPSA Journal.2000,53(11):15-17.
203. Segal L, Creely J J,Martin Jr,et al. An empirical method for estimating the degree of crystallinity of native cellulose using the X-ray diffractometer[J]. Textile Research Journal,1959,29:786-794
204. Shenk, J.S., Westethaus M. O., Hoover M. R., Analysis of forages by infrared reflectance. [J], Journal of Dairy Science.1979,62(5):807-812.
205. S(?)rensen L.K., True accuracy of near infrared spectroscopy and its dependence on precision of reference data[J]. J. Near Infrared Spectrosc.2002,10(1):15-25.
206. Sykes R, Li B, Hodge G, et al. Prediction of loblolly pine wood properties using transmittance near-infrared spectroscopy[J]. Canadian Journal of Forest Research.2005,35(10):2423-2431.
207. Tatsuhiko Yamada, Ting-Feng Yeh, Hou-Min Chang et al. Rapid analysis of transgenic trees using transmittance near-infrared spectroscopy (NIR) [J]. Holzforschung,2006,60:24-28.
208. Terdwongworakul Anupun Vittaya Punsuwan, Warunee Thanapase et al. Rapid assessment of wood chemical properties and pulp yield of Eucalyptus camaldulensis in Thailand tree plantations by near infrared spectroscopy for improving wood selection for high quality pulp[J]. The Japan Wood Research Society,2005,51:167-171.
209. Ting Feng Yeh, Hou Min Chang, John F Kadla. Rapid prediction of solid wood lignin content using Transmittance near-infrared spectroscopy[J]. Agric Food Chem.2004,52:1435-1439.
210. Tran, C. D., Oliveira, D., and Grishko, V. I.. Determination of enantiomeric compositions of pharmaceutical products by near-infrared spectrometry[J].Anal. Biochem.2004,325(2),206-214.
211. Tsuchikawa S. A review of recent near infrared research for wood and paper[J]. Applied Spectroscopy Reviews.2007,42(1):43-71.
212. Tsuchikawa, S., Torii, M., Tsutsumi, S.. Application of near infrared spectrophotometry to wood for. Calibration equations for moisture content[J]. Mokuzai Gakkaishi,1996,42:743-754.
213. Via B K, Shupe T F, Stine M, et al. Tracheid length prediction in.Pinus palustris by means of near infrared spectroscopy:The influence of age[J]. Holz als Roh-und Werkstoff.2005,63(3): 231-236.
214. Via B K, So C L, Shupe T F, et al. Ability of near infrared spectroscopy to monitor air-dry density distribution and variation of wood[J]. Wood and Fiber Science.2005,37(3):394-402.
215. Via B K. Modeling longleaf pine (Pinus palustris Mill) wood properties using near infrared spectroscopy[D]. Louisiana State University,2004.
216. Via B. K., T. F. Shupe, M. Stin. Tracheid length prediction in Pinus palustris by means of near infrared spectroscopy:the influence of age[J]. Holz als Roh und Werkstoff,2005,63(4):231-236.
217. Walke, J C., Butterfield B. G., The importance of microfibril angle for the processing industries [J]. New Zealand Forestry,1995,40(4):34-40.
218. Wesley, Larroque, O., Osborne, B. G.. Measurement of gliadin and glutenin content of flour by NIR spectroscopy[J]. J. Cereal Sci.2001,34(2),125-133.
219. Williams P C, Norris K H, Sobering D C. Determination of protein and moisture in wheat and barley by near infrared transmission. [J]. Agric FoodChem,1985,33:239-244
220. Williams, P. C, and Sobering, D. C. Comparison of commercial near infrared transmittance and reflectance instruments for analysis of whole grains and seeds. [J]Near Infrared Spectrosc.1993, 1:25-32.
221. WoldS.AnttiH.Lindgren.rOmhnaJ.orthogonalsignalorredtionofnear-infraredSpectra,Chemon.nItell .Lab.Syst.[J],1998,44:75-185.
222. Wright J A, M D Bitkett M J T G. Prediction of pulp yield and cellulose content from wood samples using near infrared reflectance spectroscopy[J]. TAPPI JOURNAL.1990,73(8):165-166.
223. Wright, J. A., Birkett, M. D., and Gambino, M. J. T.. Prediction of pulp yield and cellulose content from wood samples using near infrared reflectance spectroscopy[J].TAPPI J..1990,73(8),164-166.
224. XuYouming, ChenShiyin, FangWenb. Variation of tracheid length arid Microfibril angle and the irrelationship of slash pine grown in different sites[J]. Journal of Huazhong Agricultural University,1999.18(1):83-88.
225. Yamada T, Yeh T F, Chang H M, et al. Rapid analysis of transgenic trees using transmittance near-infrared spectroscopy (NIR)[J]; Holzforschung.2006,60(1):24-28.
226. Yao Sheng, Guofeng Wu, Mian Xing, et al. Determination of lignin content in acacia spp. using near infrared reflectance spectroscopy, BioResources,5(2),556-562.
227. Yao Sheng, Pu Junwen, Wu Guofeng, et al. Determination of Holocelloluse and Alpha-celloluse Contents in Acacia spp. using NIRS. The 14th International Conference on Near Infrared Spectroscopy. Thailand,2009.11
228. Yao Sheng, Pu Junwen, Xin Mian, et al. Paper Properties of the ECF Bleached Pulp from Triploid of Populus tomentosa Carr,IAWPS2008, Harbin,2008.9.
229. Yao Sheng, Pu Junwen, Zhang Yong, et al. Comparison of Pulp Properties from ASAM, Kraft and ASAQ Processes of Triploid of Populus tomentosa Carr.,2nd International Papermaking and Environment Conference, tianjin,2008.5,395-398.
230. Yeh, T F, Chang H M K J. Rapid prediction of solid wood lignin content using transmittance near-infrared spectroscopy[J]. Journal of Agricultural and Food Chemistry.2004,52:1435-1439.
231. Yeh, T. F., Chang, H. M., and Kadla, J. F.. Rapid prediction of solid wood lignin content using transmittance near-infrared spectroscopy[J] Journal of Agricultural and Food Chemistry.2004,52(6),1435-1439.
232. Yuzak E, Lohrke C. At-Line Kappa Number Measurement by Near-Infrared Spectroscopy[C]. 1993.
233. Ze-hui Jiang, Zhong Yang, Chi-leung So C H. Rapid prediction of wood crystallinity in Pinus elliotii plantation wood by near-infrared spectroscopy [J]. Journal of Wood Science.2007,53(5): 449-453.