Larch Wood Defect Definition and Microscopic Inversion Analysis Using the ELM Near-infrared Spectrum Optimization along with WOA-SVM

Near-infrared spectroscopy is a mature non-destructive testing technique that can be applied effectively to identify and distinguish the structural characteristics of wood from a microscopic perspective. To accurately describe the morphology of wood panels on multiple scales and uncover the mechanisms determining the mechanical properties of wood, the present study was initiated by first defining four regions-the knot, fiber deviation, transition, and clear wood regions. On the surface of solid wood panels, and then a method was presented for inversing the microscopic morphology of larch wood panels based on near-infrared spectral feature extraction and modeling analysis. The experiments revealed that the combinatorial optimization conducted after the extreme learning machine feature band optimization can help effectively extract appropriate near-infrared feature wavelengths, reduce model dimension, and improve model applicability and accuracy. Therefore, the near-infrared models established based on the combination of the whale optimization algorithm and a support vector machine could accurately define and distinguish the four regions on the wood surfaces. Moreover, it was confirmed that the application of NIR spectral features along with the ELM-WOA-SVM algorithm can help optimize the traditional linear description that models the defect morphology as a cone to an accurate nonlinear description and to perform highly accurate nonlinear inversion of panel morphology.

Automated summary

This study successfully achieved the inversion of the microscopic morphology of larch wood panels using near-infrared spectral feature extraction and modeling analysis. Combinatorial optimization and feature band optimization were utilized to extract appropriate near-infrared feature wavelengths, reduce model dimension, and improve model applicability and accuracy. The combination of the whale optimization algorithm and a support vector machine accurately defined and distinguished the different regions on the wood surfaces.