Prediction of Wood Mechanical and Chemical Properties in the Presence and Absence of Blue Stain Using Two near Infrared Instruments
Prediction of Wood Mechanical and Chemical Properties in the Presence and Absence of Blue Stain Using Two near Infrared Instruments
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使用两种近红外仪器预测存在和不存在蓝斑时木材的机械和化学特性
DOI：
10.1255/jnirs.538
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发表时间：
2005
影响因子：
1.8
通讯作者：
L. Groom
中科院分区：
文献类型：
--
作者：
B. Via;C. So;T. Shupe;L. Eckhardt;M. Stine;L. Groom
本研究的目的是（a）确定实木中的蓝色染色是否影响从未染色木材群体开发的校准方程，（B）评估在没有从主仪器进行校准转移的情况下由从仪器进行扫描时引入的偏倚，以及（c）通过仪器、染色剂和仪器×染色剂相互作用划分基于吸光度的变化。结果有助于确定这种情况下所需的校准转移。以木材的木质素、抽出物、弹性模量（莫伊）、抗折模量（莫尔）和密度为因变量，建立了5个性状的染色不敏感方程。然而，当一个从近红外仪器引入没有校准传输，五个预测性状中有三个显着偏置的存在下染色。进一步的分析揭示了染色剂和仪器之间的相互作用，表明在使用从机进行扫描期间也引入了仪器偏倚。对于多元线性回归（MLR）和主成分回归（PCR），发现如果一个性状需要更多的波长（或主成分）来预测因变量，则由于蓝变引起的偏差变得越来越突出。当引入染色剂而没有校准转移时，发现PCR比MLR执行得更好。这一发现暗示了PCR在外推条件下比MLR更有效，但并不旨在支持缺乏校准转移。最后，Mallow Cp诊断证明了在模型选择中的价值，尽管众所周知的要求（Cp - p ≤ 0）显得保守。对于MLR和PCR，Cp - p ≤ 5通常产生适用的模型，而Cp - p > 7大约是模型性能下降的阈值。
The objective of this research was to (a) determine if blue stain in solid wood influenced calibration equations developed from a non-stained wood population, (b) assess the bias introduced when scanning was performed by the slave instrument without calibration transfer from the master instrument and (c) partition absorbance-based variation by instrument, stain and instrument × stain interaction. The results helped to determine the calibration transfer needed for this case. The dependent variables assessed from clear and stained wood were lignin, extractives, modulus of elasticity (MOE), modulus of rupture (MOR) and density When the master instrument was used for both calibration and prediction, it was found that stain-insensitive equations for the five traits could be built. However, when a slave near infrared instrument was introduced without calibration transfer, three out of five predicted traits were significantly biased by the presence of stain. Further analysis revealed an interaction between stain and instrument indicating that instrument bias was also introduced during scanning with a slave. For both multiple linear regression (MLR) and principal components regression (PCR), it was found that if a trait needed more wavelengths (or principal components) for prediction of the dependent variable, bias due to blue stain became increasingly prominent. PCR was found to perform better than MLR when stain was introduced with no calibration transfer. Such a finding alludes that PCR works better than MLR under extrapolation conditions but is not intended to support a lack of calibration transfer. Finally, the Mallows Cp diagnostic proved valuable in model selection although the well-known requirement of (Cp – p ≤ 0) appeared conservative. For MLR and PCR, a Cp – p ≤ 5 often yielded applicable models while Cp – p > 7 was about the threshold where model performance dropped.