首页> 外文会议>International symposium on emerging technologies of pulping and papermaking;ISETPP >PREDICTION OF WOOD DENSITY USING NIR SPECTROSCOPY: RADIAL VERSUS CROSS-SECTIONAL SURFACE DATA COLLECTION
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PREDICTION OF WOOD DENSITY USING NIR SPECTROSCOPY: RADIAL VERSUS CROSS-SECTIONAL SURFACE DATA COLLECTION

机译:利用近红外光谱法预测木材密度:径向对横截面数据的收集

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It has been demonstrated that data from SilviScan and NIR can be used to calibrate and predict wood properties. Most of the predictions are based on NIR spectra collected from the radial-longitudinal surface of the wood. However, predictions from the crosssectional surface are more practical in the field. In this work, wood density of balsam fir (Abies balsamea), black spruce (Picea mariana) and the combined samples were calibrated and predicted based on SilviScan and NIR results from both the radial and cross-sectional surfaces. The results showed that predictions from cross-sectional surface were as good as those from radial surface. The predictions for the combined samples were better than those for the single species samples. For the combined samples, the R2 of prediction were 0.89(based on radial surface) and 0.90 (based on cross-sectional surface), respectively. The best model from cross-sectional surface would require the NIR spectra of the full log, while only 3 spots of each strip would be needed for the radial surface.
机译:已经证明,来自SilviScan和NIR的数据可用于校准和预测木材性能。大多数预测是基于从木材的径向-纵向表面收集的NIR光谱。然而,从横截面的预测在本领域中更为实用。在这项工作中,对香脂冷杉(Abies balsamea),黑云杉(Picea mariana)和混合样品的木材密度进行了校准,并根据径向和横截面表面的SilviScan和NIR结果进行了预测。结果表明,从横截面的预测与从径向表面的预测一样好。组合样本的预测优于单个物种样本的预测。对于合并的样本,预测的R2分别为0.89(基于径向表面)和0.90(基于横截面表面)。从横截面的最佳模型将需要完整对数的NIR光谱,而径向表面仅需要每个条带的3个点。

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