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Scientia Silvae Sinicae ›› 2024, Vol. 60 ›› Issue (5): 169-176.doi: 10.11707/j.1001-7488.LYKX20220456

• Research papers • Previous Articles     Next Articles

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

Shoujia Liu1,2,3,Tuo He1,2,3,4,*,Yang Lu1,2,3,Lichao Jiao1,2,3,Juan Guo1,2,3,Wiedenhoeft Alex C5,Yafang Yin1,2,3   

  1. 1. Research Institute of Wood Industry, Chinese Academy of Forestry Beijing 100091
    2. Wood Collection, Chinese Academy of Forestry Beijing 100091
    3. Wood Specimen Resource Center of National Forestry and Grassland Administration Beijing 100091
    4. Wildlife Conservation Monitoring Center, National Forestry and Grassland Administration Beijing 100714
    5. Center for Wood Anatomy Research, Forest Products Laboratory Madison WI 53726
  • Received:2022-07-06 Online:2024-05-25 Published:2024-06-14
  • Contact: Tuo He

Abstract:

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.

Key words: Swietenia, quantitative wood anatomy, variation pattern, random forest, wood identification

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