首页> 外文期刊>Journal of the Indian Academy of Wood Science >Development and evaluation of models for specific gravity of Eucalyptus tereticornis wood by fourier transformed near infrared spectroscopy and partial least squares regression analysis
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Development and evaluation of models for specific gravity of Eucalyptus tereticornis wood by fourier transformed near infrared spectroscopy and partial least squares regression analysis

机译:傅里叶变换近红外光谱和偏最小二乘回归分析法建立和评价桉树木材比重模型。

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摘要

Specific gravity is very important factor in determining the economics of wood and wood products.This study is aimed to apply the near infrared spectroscopy technique with integrating sphere accessory for fast prediction of specific gravity of dry timber (moisture content 12 %) of Eucalyptus tereticornis from varying age group.Specific gravity was determined by conventional method and correlated with near infrared spectra using partial least square regression. The coefficient of determination of cross validation (R_(CV)~2) is in the range of 0.80-0.91 for radial face and 0.88-0.94 for tangential face. The calibration equations when applied to test set resulted in coefficient of determination (R_(TX)~2) ranging from 0.68 to 0.93 for radial face and 0.90 to 0.94 for tangential face. The ratio performance of deviation was calculated as the ratio of the standard deviation of the prediction set to the root mean square error of prediction and it was found to be between 2.1 and 3.8 for radial face and 3.2-4.1 for tangential face. The values were found identical when groups i.e., calibration and test set were, interchanged. The range error ratio for both faces models were also identical and indicating that models are quite satisfactory for screening purpose.
机译:比重是决定木材和木制品经济性的重要因素。本研究旨在将近红外光谱技术与积分球附件相结合,用于快速预测来自桉树的干燥桉木(水分含量为12%)的比重。通过常规方法确定比重,并使用偏最小二乘回归将其与近红外光谱相关联。交叉验证的确定系数(R_(CV)〜2)在径向面为0.80-0.91,在切向面为0.88-0.94。当将校准方程式应用于测试装置时,其确定系数(R_(TX)〜2)径向面为0.68至0.93,切向面为0.90至0.94。计算偏差的比率性能作为预测集的标准偏差与预测的均方根误差的比率,发现径向面在2.1到3.8之间,而切向面在3.2到4.1之间。当组即校准和测试集互换时,发现值相同。两个人脸模型的距离误差率也相同,这表明该模型对于筛选目的是非常令人满意的。

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