To rapidly acquire fiber phenotypic data for wood quality assessment, we used a portable NIR spectro-meter to collect spectral data in 100 individuals of Schima superba at 18-year-old of 20 different provenances, and simultaneously collected wood cores. Wood basic density and the anatomical structure of wood fiber were measured. The standard normal variate (SNV), orthogonal signal correction (OSC), and multiplicative scatter correction (MSC) methods were used for spectral preprocessing, the competitive adaptive reweighted sampling (CARS) method were used for wavelength selection, and the partial least squares regression (PLSR) model were established. The results showed a significant difference for the absolute reflectance data between forest and indoor environments, and the spectral data of which were relatively independent. SNV, OSC and MSC showed significant differences for predictive performance of the model. OSC had the excellent preprocessing capability in multiple cha-racteristics of wood fiber ether in forest and indoor environments. The predictive accuracy of the models with R2 was 0.47-0.78 in forest (average=0.63), and R2 was 0.54-0.82 in indoor environment (average=0.71). However, the SNV and MSC methods could not establish the models, except the fiber wall-cavity ratio from forest data. After wavelength selection through the CARS method, the predictive accuracy of the models was significantly improved using both forest and indoor data (R2=0.58 and 0.72, respectively). When performed OSC before and after CARS, the predictive accuracy of the models was improved to 0.68 and 0.84 respectively using forest and indoor data. The OSC and CARS could significantly improve the accuracy of the models for wood fiber anatomical structures. First OSC, then CARS, and finally OSC methods could be used to establish the PLSR model for fiber length, fiber cell wall thickness, fiber lumen diameter, wood basic density, fiber cavity-width ratio, and fiber wall-cavity ratio, and the R2 ranged from 0.80 to 0.95. These models had effective predictive ability and accuracy to assess the physical properties of wood fibers of S. superba.
为快速获取木荷木纤维表型数据以评估木材质量,对18年生20个种源100个材料使用便携式近红外光谱仪采集光谱数据,同时测定木纤维基本密度和解剖结构等9个指标,通过SNV、OSC和MSC预处理光谱数据,并用CARS筛选波长,建立PLSR模型。结果表明: 林地与室内光谱数据存在显著差异,两者的光谱数据相对独立。SNV、OSC和MSC三种预处理方法对模型的预测效果差异显著,其中,OSC在林地和室内多项木纤维表型结构特征光谱预处理上表现优异,模型的预测精度林地R2=0.47～0.78(平均0.63),室内R2=0.54～0.82 (平均0.71)。而SNV和MSC方法仅对林地数据建立壁腔比模型的预测效果较好,其余模型效果不佳。通过CARS方法筛选波长后,林地和室内数据构建的模型预测精度得到有效提升(R2=0.58和0.72)。在CARS前后各执行一次OSC时,林地和室内数据构建的模型预测精度可分别提升至0.68和0.84。OSC预处理和CARS方法可以有效提高木纤维解剖结构构建模型的精度。木纤维长、双壁厚、腔径、木材基本密度、腔宽比和壁腔比可先通过OSC结合CARS进行处理,在经过一次OSC处理后建立PLSR模型,模型预测精度R2在0.80～0.95,可以预测评估木荷类木纤维物理性质指标。.
Keywords:
PLSR;