Identification of Pinus species related to historic architecture in Korea using NIR chemometric approaches
Identification of Pinus species related to historic architecture in Korea using NIR chemometric approaches
复制标题
DOI：
10.1007/s10086-016-1540-0
复制
发表时间：
2016-04-01
影响因子：
2.9
通讯作者：
Sugiyama, Junji
中科院分区：
文献类型：
--
作者：
Hwang, Sung-Wook;Horikawa, Yoshiki;Sugiyama, Junji
在本研究中，我们鉴定了在韩国被用作传统建筑材料的类似松树树种。设计判别模型，对赤松、赤松、赤松的木材种类进行了鉴别。Et Zucc.和生长于俄罗斯和德国的西洋参(P.sylvestris L.)。木块样本更适用于野外木材识别，然而，人脸测量具有不同的光谱特征。因此，我们还从木粉中制备了片剂样品，以确定物种鉴定的关键因素，而不会因木材表面的光谱差异而产生差异。密花青杨为。对生长于俄罗斯和德国的赤霉菌和密花赤霉菌进行了清晰的分类，两个样本类型的正确预测率均为100%。用木块样品建立的判别模型表现良好，8000~4000 cm(-1)近红外光谱区对应的R(P)(2)值均大于0.90。用片剂样本建立的判别模型也表现出较好的判别性能。片剂样品减小了各物种在二阶导数近红外光谱中的光谱差异。然而，使用药片样本提供了更具体的物种信息，导致了更准确的分类。
In the present study, we identify similar Pinus species that have been used as building materials for traditional architecture in Korea. Discriminant models were designed to identify wood species of Pinus densiflora forma erecta Uyeki, P. densiflora Sieb. et Zucc., and P. sylvestris L. grown in Russia and Germany using near-infrared (NIR) spectroscopy coupled with multivariate analysis. Wood block samples are more practical to use in the field for wood identification; however, the face measurements have different spectral characteristics. Thus, we also prepared tablet samples from wood powder to determine the key factors for species identification without the spectral differences resulting from the wood faces. P. densiflora for. erecta and P. densiflora were clearly classified from P. sylvestris grown in Russia and Germany with a correct prediction rate of 100 % in both sample types. The discriminant models using wood block samples exhibited good performances as the R (p) (2) values corresponding to NIR spectral regions of 8000-4000 cm(-1) were higher than 0.90. The discriminant models using tablet samples also showed good discriminant performance. The tablet samples reduced the spectral differences of each species in the second derivative NIR spectra. However, using the tablet samples provided more specific species information, resulting in a more accurate classification.