表面粗糙度对NIR预测落叶松基本密度的影响

Effects of surface roughness on NIR-based larch wood basic density prediction

【目的】对不同表面粗糙度的落叶松木材光谱进行分析,建立适合不同表面粗糙度的近红外模型,为提高近红外模型预测木材密度精度和普适性提供理论依据。【方法】以黑龙江省星火林场落叶松木材为研究对象,对未打磨(M0)、150目打磨(M1)和320目打磨样品(M2)的近红外光谱进行分析。分别采用11点移动平均平滑、基线校正(baseline)和SG平滑进行了光谱预处理以去除冗余光谱信息,采用人工选择、反向区间偏最小二乘法(BiPLS)和联合区间偏最小二乘法(SiPLS)完成波段优选,构建针对不同表面粗糙度的单一预测模型及包含3种表面粗糙度样品的近红外混合模型。【结果】M0样品包含的光谱信息要多于另外2种粗糙度,3种预处理方法中,SG平滑预处理的建模效果综合评价最好。基于3种波段优选方法分别建立M0、M1和M2的基本密度预测模型,SiPLS波段选择方法效果更好,对于M0、M1、M2这3种表面粗糙度样品,验证集相关系数R及均方根误差(R_(MSEP))分别为0.865 9和0.022 7、0.766 0和0.021 4、0.725 6和0.027 4。以3种不同粗糙度混合建立的SiPLS-混合预测模型,对于不同粗糙度样品的预测能力好于各粗糙度的单一模型,对于M0、M1、M2这3种表面粗糙度样品,模型的R_(MSEP)分别降低了11%、25%、5%。【结论】基于3种表面粗糙度所构建的近红外模型都可以实现木材密度的有效预测且采用SiPLS优选波段所建模型的预测精度为M0>M1> M2,SiPLS波段选择方法可以优化表面粗糙度对预测模型的影响,在此基础上建立的混合模型则使近红外预测模型更加具有普适性,为木材的分类优选及精细化利用提供了理论基础及技术支持。

【Objective】The spectra of larch wood with different surface roughness were analyzed,and the NIR models suitable for different surface roughness were established,which provided a theoretical basis for improving the accuracy and universality of NIR models in predicting wood density.【Method】Taking larch wood from Xinghuo Forest Farm in Heilongjiang Province as the research object,the near-infrared spectra of unpolished(M0),150-mesh(M1)and 320-mesh(M2)samples were analyzed and studied.11-point moving average smoothing,baseline correction and SG smoothing were used for spectral preprocessing to remove redundant spectral information.Manual selection,backward interval partial least squares(BiPLS)and synergy interval partial least squares(SiPLS)were used to complete band optimization.A single prediction model for different surface roughness and a mixed near-infrared model with three surface roughness samples were constructed.【Result】The M0 sample contained more spectral information than the other two samples.Among the three pretreatment methods,the comprehensive evaluation of the modeling effect of SG smoothing pretreatment showed the best.The basic density prediction models of M0,M1 and M2 were established based on the three band optimization methods,and the band selection method of SiPLS had the best effect.For the three surface roughness samples of M0,M1 and M2,the validation set correlation coefficients R and R_(MSEP) were 0.8659 and 0.0227,0.7660 and 0.0214,0.7256 and 0.0274,respectively.The prediction ability of the SIPLS mixed prediction model based on the mixture of three different roughness samples was better than that of the single model based on each roughness sample.For the three surface roughness samples of M0,M1 and M2,the R_(MSEP) of the model decreased by 11%,25%and 5%,respectively.【Conclusion】The NIR models based on the three kinds of surface roughness samples can achieve effective prediction of wood density,and the prediction accuracy of the model is M0>M1>M2.The SiPLS band selection method can be used to optimize the influence of surface roughness on the prediction model,and the mixed model established on this basis makes the NIR prediction model more universal.It provides a theoretical basis and technical support for the classification,optimization and fine utilization of wood.