摘要
针对微分法在有效消除光谱背景和基线漂移的同时会增加光谱噪声的问题,把最新发展的经验模态分解方法(EMD)引入到近红外光谱处理中来,以烟草的一阶导数近红外(NIR)光谱为研究对象,探讨经验模态分解在近红外光谱预处理中的应用,并与小波变换消噪效果进行了对比分析。结果表明,用基于经验模态分解去噪后的光谱进行分析,预测集的决定系数r2由去噪前的0.9705提高到0.9832,均方根误差(RMSEP)由去噪前的0.5606降为0.3310,比基于小波变换的分析结果略高。因此,经验模态分解方法对消除光谱的噪声是有效的,有效地提高了光谱的分析精度和模型的稳定,为近红外光谱预处理提供了一种新方法。
The derivative method can correct baseline effects but also add noise to it.The empirical mode decomposition（EMD） method was proposed to get the pretreatment of near-infrared（NIR） spectrum.Herein,the first derivative NIR spectrum of tobacco was served as the target and the application of EMD in NIR spectrum pretreatment was studied.Experimental results showed that the result after de-noising with EMD was satisfactory.The correlation ratio of the prediction set was improved from 0.9705 to 0.9832,and the RMSE reduced from 0.5606 to 0.3310.And this method obtained a better de-noising effect compared to wavelet transform method.It is concluded that EMD is a useful method to eliminate noise of NIR signals,which makes the final model more representative,stable and robust.EMD provides a new method for near-infrared spectrum pretreatment.
出处
《光学学报》
EI
CAS
CSCD
北大核心
2010年第1期267-271,共5页
Acta Optica Sinica
基金
国家863计划(2006AA06Z105)
湖南省"十一五"重点建设学科光学基金资助课题
关键词
光谱学
近红外光谱预处理
经验模态分解
spectroscopy near-infrared spectrum pretreatment（NIR） empirical mode decomposition（EMD）
