School of Biological Sciences, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.
School of Chemical Sciences, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.
Int J Biol Macromol. 2018 Jul 1;113:507-514. doi: 10.1016/j.ijbiomac.2018.02.105. Epub 2018 Feb 17.
Near infrared (NIR) spectroscopy coupled with partial least squares (PLS-1) regression was used to predict the lignin contents and monosaccharide compositions of milled wood of Pinus radiata. The effects of particle size and moisture content were investigated by collecting NIR spectra of four sample types: large (<0.422mm) and small (<0.178mm) particles, in both ambient and dry conditions. PLS-1 models were constructed using mixtures of compression wood (CW) and opposite wood (OW) that provided a linear range of cell-wall compositions. Our results show that lignin contents and monosaccharide compositions of pure CWs and OWs can be successfully predicted using NIR spectra of all four sample types. However, large particles in ambient conditions have the most efficient preparation and the standard error (SE) values for lignin (2.10%), arabinose (0.34%), xylose (1.33%), galactose (2.54%), glucose (6.98%), mannose (1.48%), galacturonic acid (0.22%), glucuronic acid (0.06%), and 4-O-methylglucuronic acid (0.25%) were achieved.
近红外（NIR）光谱结合偏最小二乘法（PLS-1）回归被用于预测辐射松研磨木的木质素含量和单糖组成。通过收集四种样品类型的近红外光谱来研究粒径和含水量的影响：在环境和干燥条件下的大（<0.422mm）和小（<0.178mm）颗粒。使用混合的压缩木（CW）和对生木（OW）构建 PLS-1 模型，为细胞壁成分提供了线性范围。我们的结果表明，使用所有四种样品类型的近红外光谱可以成功预测纯 CW 和 OW 的木质素含量和单糖组成。然而，环境条件下的大颗粒具有最高的制备效率，并且可以达到木质素（2.10%）、阿拉伯糖（0.34%）、木糖（1.33%）、半乳糖（2.54%）、葡萄糖（6.98%）、甘露糖（1.48%）、半乳糖醛酸（0.22%）、葡萄糖醛酸（0.06%）和 4-O-甲基葡萄糖醛酸（0.25%）的标准误差（SE）值。
