表面粗糙度对近红外光谱分析木材密度的影响

Influence of Sample Surface Roughness on Mathematical Model of NIR Quantitative Analysis of Wood Density

近红外(NIR)光谱技术是一种基于多元统计分析建立预测模型的定量分析技术,应用十分广泛,然而,近红外光谱分析结果的准确性会受到多种因素的影响。文章分析了木材表面粗糙度对近红外光谱预测木材密度的影响。结果表明,当待预测样品的粗糙度和建模样品的粗糙度一致时,分析结果较好;二者不一致时,分析误差较大。采用不同粗糙度样品建立混合预测模型,可以显著提高模型对粗糙度的适应性和稳健性。

Near infrared spectroscopy is widely used as a quantitative method,and the main multivariate techniques consist of regression methods used to build prediction models,however,the accuracy of analysis results will be affected by many factors.In the present paper,the influence of different sample roughness on the mathematical model of NIR quantitative analysis of wood density was studied.The result of experiments showed that if the roughness of predicted samples was consistent with that of calibrated samples,the result was good,otherwise the error would be much higher.The roughness-mixed model was more flexible and adaptable to different sample roughness.The prediction ability of the roughness-mixed model was much better than that of the single-roughness model.