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桃花心木木材定量解剖特征变异性及其识别OA北大核心CSTPCD

Quantitative Anatomy Analysis on Wood Feature Variability and Wood Identification of Swietenia Species

中文摘要英文摘要

[目的]基于定量解剖方法探究桃花心木属 3种树种木材的构造特征,揭示其不同构造特征的种间和种内变异规律,为木材"种"水平的准确识别提供科学依据.[方法]针对大叶桃花心木、桃花心木和矮叶桃花心木木材标本显微切片,通过光学显微镜采集其横、径、弦三切面微观构造图像,利用图像分析软件分别测量导管分子长度、管孔弦向直径、管孔频率、木纤维长度、木射线宽度、木射线高度和木射线频率 7个定量解剖特征,分析其不同构造特征的种间和种内变异规律;采用随机森林算法对木材树种进行识别,比较木材不同定量解剖特征对识别准确率的贡献度.[结果]3种桃花心木木材显微切片图像中管孔、轴向薄壁组织和木射线等构造特征相似,通过人工方式难以区分;除木射线宽度外,其他 6组定量解剖特征在种间均存在显著差异;随机森林算法对桃花心木的识别准确率达 86.67%,7个定量解剖特征对木材识别结果的贡献度存在差异,其中导管分子长度的平均准确率降低度值(3.956)和平均基尼指数降低度值(6.311)最大,管孔弦向直径次之,木射线宽度的平均准确率降低度值(0.797)和平均基尼指数降低度值(2.175)最小,表明导管分子长度的贡献度最大,木射线宽度的贡献度最小.[结论]采用木材定量解剖方法,揭示 3种桃花心木构造特征的种间和种内变异规律以及在木材识别中起关键作用的定量解剖特征,可为实现木材"种"水平的准确识别提供科学依据.

[Objective]Based on quantitative wood anatomy(QWA)method,the wood anatomical features of three Swietenia species were analyzed to reveal the patterns of their interspecific and intraspecific variation,and provide a scientific basis for accurate wood identification at the level of"species".[Method]The microscopic images of S.macrophylla,S.mahagoni and S.humilis were collected from transverse,radial and tangential section respectively using a microscope,and the quantitative data of seven anatomical features of wood,i.e.the vessel element length(VEL),tangential diameter of vessel lumina(TVD),vessels per square milimeter(FOV),fiber length(FL),ray width(RW),ray height(RH)and rays per millimeter(RPMM)were measured by image analysis software to investigate the interspecific and intraspecific variation of wood anatomical features.Additionally,the random forest algorithm was used to discriminate three Swietenia species at the species level,and the contributions of different quantitative anatomical features to wood identification were comparatively analyzed.[Result]The wood anatomical features of the three Swietenia species were highly closed in vessel,axial parenchyma and wood rays under microscope,therefore it is difficult to distinguish them artificially.However there were significant difference among three Swietenia species in six quantitative wood anatomical features except for RW.The random forests algorithm has a discrimination accuracy of 86.67%for Swietenia mahagoni.VEL showed the highest value of the mean decrease accuracy(3.956)and the mean decrease Gini(6.311),followed by the TVD.RW exhibited the lowlest value of the mean decrease accuracy(0.797)and the mean decrease Gini(2.175).Among the seven wood anatomical features,VEL exhibit the most contribution to the wood identification accuracy,while the RW had the least contribution.[Conclusion]This study reveals the interspecific and intraspecific variation of three selected Swietenia species and the key quantitative anatomical features in wood identification based on quantitative wood anatomy,which provided a scientific basis of the accurate wood identification at"species"level.

刘守佳;何拓;陆杨;焦立超;郭娟;Alex CWiedenhoeft;殷亚方

中国林业科学研究院木材工业研究所 北京 100091||中国林业科学研究院木材标本馆 北京 100091||国家林业和草原局木材标本资源库 北京 100091中国林业科学研究院木材工业研究所 北京 100091||中国林业科学研究院木材标本馆 北京 100091||国家林业和草原局木材标本资源库 北京 100091||国家林业和草原局野生动物保护监测中心 北京 100714美国林产品实验室木材解剖研究中心 麦迪逊 WI 53706

林学

桃花心木定量解剖变异规律随机森林木材识别

Swieteniaquantitative wood anatomyvariation patternrandom forestwood identification

《林业科学》 2024 (005)

169-176 / 8

北京市自然科学基金项目"基于精细构造特征和深度学习的木材自动精准识别方法研究"(32201496).

10.11707/j.1001-7488.LYKX20220456

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