Bark Content Determination of Ultra-Thin Fibreboard by
Hyperspectral Technique
YANG Chun-mei1, ZHU Zan-bin1, 2*, LI Yu-cheng1, MA Yan1, SONG Hai-yang3
1. College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
2. Xinyang Agriculture and Forestry University, Xinyang 464000, China
3. Asia Union Machinery Co., Ltd., Dunhua 133700, China
Abstract：Ultra-thin fiberboard with the thickness of 0.8mm is an innovative experimental product in the fiberboard category. The bark content greatly influences the setting of its production equipment parameters and the quality indicators such as static curvature strength and water resistance. It is important to determine the bark content in ultra-thin fiberboard wood fiber accurately. At present, the accurate determination of bark content is difficult. A fiber bark content detection model was established by hyperspectral near-infrared imaging system combined with relevant algorithms, and the fiber bark content detection method was innovated. In this experiment, spectroscopic sample images of poplar fibers containing poplar bark of 0%, 3%, 5%, 7%, 10%, 12%, 15%, 20%, 25%, 30%, and 100% were determined by the hyperspectral imager. The results of pretreatment of mean centralization (MC), multiple scattering corrections (MSC), standard normal variable transformation (SNV) and first-order (1-Der) derivative were analyzed, then the MSC was selected as the best pretreatment method for this test model. The spectral data pretreated by MSC were extracted by SPA and CARS, and the band combination with the highest correlation with the bark content was obtained, and the full-band model was compared and analyzed to establish a partial least squares regression (PLSR) model. From the experimental data, we can see differences in the predictive performance of the model of partial minimum secondary return (PLSR) established by pretreatment of MC, MSC, SNV and 1-Der. Among them, the performance of the MSC-PLSR model is the best. The correction determinant R2C is 0.994, the prediction determinant R2P is 0.985, the correction square root error RMSEC is 0.831%, and the prediction square root error RMSEP is 1.336%. 37 and 49 characteristic bands were extracted by SPA and CARS respectively, among which the CARS model was better, R2C was 0.991, R2P was 0.979, RMSEC was 0.885%, and RMSEP was 1.335%. The experimental results show that the hyperspectral imaging system combined with the corresponding algorithm can realize the detection of the bark content of the fiber. The study's results provide technical support and theoretical reference for the detection of the bark content in the production of ultra-thin fiberboard, which can effectively realize the quantitative detection of the bark content in the fiber, and innovate a model method that can determine the bark content of the fiberboard.
杨春梅，朱赞彬，李昱成，马 岩，宋海洋. 高光谱技术测定超薄纤维板纤维树皮含量[J]. 光谱学与光谱分析, 2023, 43(10): 3266-3271.
YANG Chun-mei, ZHU Zan-bin, LI Yu-cheng, MA Yan, SONG Hai-yang. Bark Content Determination of Ultra-Thin Fibreboard by
Hyperspectral Technique. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3266-3271.
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