Wood species identification by near-infrared spectroscopy
Wood species identification by near-infrared spectroscopy
复制标题
近红外光谱法鉴定木材树种
DOI：
10.1080/20426445.2016.1242270
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发表时间：
2017
影响因子：
1.1
通讯作者：
S. Avramidis
中科院分区：
文献类型：
--
作者：
C. Lazarescu;Foster Hart;Z. Pirouz;K. Panagiotidis;Shawn D. Mansfield;J. Barrett;S. Avramidis
建立了最优人工神经网络和偏最小二乘判别分析回归模型，并对近红外光谱（NIR）区分两种木材的准确性进行了测试。本文研究了西部铁杉（Tsuga heterophylla（Raf.）Sarg.）和冷杉（Abies amabilis（Dougl.）福布斯），然后在真空条件下将随机子集水饱和并再次扫描。该设计旨在捕获纤维饱和点以上的水分含量对分离算法的影响。我们的研究结果表明，这两种建模技术可以有效的工具，实现正确的识别超过86%的冷杉和94%的铁杉上窑干燥或完全饱和的董事会的物种识别。
An optimum artificial neural network and a partial least square with discriminant analysis regression were developed and tested for accuracy in distinguishing two wood species by using near-infrared (NIR) spectrum. A mixed population of kiln-dried wood boards of western hemlock (Tsuga heterophylla (Raf.) Sarg.) and amabilis fir (Abies amabilis (Dougl.) Forbes) were scanned by NIR and then a random sub-set was water saturated under vacuum conditions and scanned again. This design aimed to capture the effect of moisture content above the fibre saturation point on the separation algorithms. Our results revealed that both modelling techniques can be effective tools for species recognition achieving correct identification of over 86% for fir and 94% for hemlock on either kiln-dried or fully saturated boards.