Li Gai-Yun, Huang An-Min, Qin Te-Fu
Research Institute of Wood Industry, Chinese Academy of Forestry, Beijing 100091, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2009 Jul;29(7):1868-71.
A rapid modeling method for predicting the chemical components contents of bamboo was presented. The holocellulose contents and lignin contents of 54 samples from three growth years, two longitudinal positions and three radial positions were analyzed according to traditional chemical methods. Eleven samples were selected based on their holocellulose content and lignin content from these 54 samples to cover the range of holocellulose content and lignin content. Eleven samples were mixed at preset ratio with each other to give 21 mixed samples, the holocellulose content and lignin contents of which were computed. Another 22 samples with different chemical component contents were selected from the same 54 samples. The relationship between the chemical component contents and the diffuse reflectance NIR spectra of these samples was established using partial least squares regression. The correlation coefficient of prediction model for holocellulose content and lignin content was 0.92 and 0.93, respectively. The standard error of prediction for holocellulose content and lignin content was 1.04% and 0.913, respectively. The prediction results were similar to those from the prediction models developed by traditional methods. The results presented in this study demonstrate that samples can be prepared rapidly by the mixture of samples with each other and their chemical component contents can be computed. The technique will significantly reduce sampling time and analyzing time without adversely affecting the quality of the model.
提出了一种预测竹子化学成分含量的快速建模方法。按照传统化学方法分析了来自三个生长年份、两个纵向位置和三个径向位置的54个样本的综纤维素含量和木质素含量。从这54个样本中根据其综纤维素含量和木质素含量选择了11个样本,以覆盖综纤维素含量和木质素含量的范围。将11个样本按预设比例相互混合,得到21个混合样本,并计算了它们的综纤维素含量和木质素含量。从相同的54个样本中又选取了另外22个化学成分含量不同的样本。使用偏最小二乘回归建立了这些样本的化学成分含量与近红外漫反射光谱之间的关系。综纤维素含量和木质素含量预测模型的相关系数分别为0.92和0.93。综纤维素含量和木质素含量的预测标准误差分别为1.04%和0.913。预测结果与传统方法建立的预测模型的结果相似。本研究结果表明,通过样本相互混合可以快速制备样本,并计算其化学成分含量。该技术将显著减少采样时间和分析时间,且不会对模型质量产生不利影响。